• GENEYX ANALYSIS
• 1.0 Geneyx Device Information
  • 1.1 Intended Use
  • 1.2 Indications for Use
  • 1.3 Contraindications
  • 1.4 Warnings
  • 1.5 Precautions
  • 1.6 Description
  • 1.7 System Requirements
  • 1.8 Cybersecurity Guidance
  • 1.9 Maintenance and Upgrade Guidance
    • 1.9.1 Clinical Performance & Risk/Benefit Information
    • 1.9.2 Storage Specifications
  • 1.10 Technical Support and Turnaround Times
• 2.0 Introduction to Geneyx Analysis
  • 2.1 QuickStart Guide
    • 2.1.1 Upload Local Fastq Files
  • 2.2 Apply VCF from fastq to Protocol
  • 2.3 Upload VCF to Protocol
  • 2.4 Analysis Details
  • 2.5 Variant Analysis Workflow
    • 2.5.1 Visualize in GenomeBrowse
    • 2.5.2 Desktop IGV Integration
  • 2.6 Interpret Results
  • 2.7 Patient Report
• 3.0 Geneyx Manual
  • 3.1 Accessing Geneyx
    • 3.1.1 Login and Register Dialog
    • 3.1.2 Navigating Geneyx Analysis
  • 3.2 Dashboard
    • 3.2.1 Dashboard View
    • 3.2.2 Start New Analysis
    • 3.2.3 Protocol Selection
    • 3.2.4 Subject Information
    • 3.2.5 Importing Samples
    • 3.2.6 Clinical Information
    • 3.2.7 Account
      • 3.2.7.1 Account Management
    • 3.2.8 Roles
    • 3.2.9 Assign VCF Samples
    • 3.2.10 Analysis Delegation
    • 3.2.11 Usage
  • 3.3 Analysis
    • 3.3.1 Analyses
    • 3.3.2 Analysis Details
    • 3.3.3 Creating a Focused Workflow
    • 3.3.4 Creating a Copy of Analysis
    • 3.3.5 Analysis History
    • 3.3.6 Recalculate Local Frequency
  • 3.4 VCF Samples
    • 3.4.1 VCF Details
  • 3.5 Reports
  • 3.6 Subjects
    • 3.6.1 Subject Details
    • 3.6.2 VUS Monitor
  • 3.7 Data Management
    • 3.7.1 Processing Tasks
    • 3.7.2 Seq Samples
    • 3.7.3 Upload Single Sample (fastq)
    • 3.7.4 Entering Subject Information (fastq)
    • 3.7.5 Entering Seq Samples Details (fastq)
    • 3.7.6 Selecting File Source (fastq)
    • 3.7.7 Selecting Seq Sample Files (fastq)
    • 3.7.8 Selecting Seq Sample Pipeline (fastq)
    • 3.7.9 Sequencing Samples Details
  • 3.8 Batches (fastq)
    • 3.8.1 Batch Upload (fastq)
  • 3.9 Variant Browser
  • 3.10 Settings
    • 3.10.1 Enrichment Kits & Smart Filtering
    • 3.10.2 Gene Panels
    • 3.10.3 Variant Maps
    • 3.10.4 Allele Frequency Backlog
    • 3.10.5 Sequencing Machines
    • 3.10.6 Data Sources
    • 3.10.7 Case Sub Statuses
    • 3.10.8 ACMG Settings
    • 3.10.9 Protocols
    • 3.10.10 Filters
    • 3.10.11 Genetic Model Management
    • 3.10.12 Secondary Pipelines
    • 3.10.13 Report Configurations
    • 3.10.14 Genome Browser
    • 3.10.15 Variant Tags
    • 3.10.16 Gene Descriptions
    • 3.10.17 Variant Knowledgebase
  • 3.11 Accounts
  • 3.12 Variant Analysis
    • 3.12.1 Summary Information
    • 3.12.2 Variant Analysis
    • 3.12.3 Filters and Tools
    • 3.12.4 Genetic Models
    • 3.12.5 Annotations
      • Genomic and Genetic Data:
      • ACMG:
      • Variant Calling Q&R (Quality and Read Information):
      • Clinical Evidence:
      • IN HOUSE:
      • Matched CNV/SVs:
      • Effect & Prediction:
      • Frequency:
      • Genomic and Genetic Data:
      • Variant Calling Q&R:
      • Effect & Prediction:
      • Clinical Evidence:
      • In House:
      • Matched SNVs:
      • Frequency:
    • a CNV/SV ACMG Guidelines
    • 3.12.5b Repeat Expansion Analysis
    • 3.12.6 Annotating Variants
    • 3.12.7 Reporting Variants
  • 3.13 Batch VCF Upload (& Joint VCF Files)
  • 3.14 APIs
  • 3.15 Python Scripts/Command Line
    • 3.15.1 CNV/SV/Repeat Unification Script
    • 3.15.2 Microarray to VCF Converter
    • 3.15.3 DRAGEN TruSight Oncology Parser
  • 3.16 Pharmacogenomics (PGx)
    • 3.16.1 Instruction for using PGx
  • 3.17 Long-Read Sequencing
    • 3.17.1 Import
    • Geneyx supports the analysis of long-read sequencing data starting from VCF files. The platform accommodates various file formats, including SNV, CNV, SV, and repeat files. To ensure proper merging and annotation, SV, CNV, and repeat files must be processed through a unification script, available here.
  • 3.17.2 Analyzing Long-Read Data
    • 3.19 Providing BAM & methylation tracks for samples not processed by Geneyx
    • 3.19.1 General
    • 3.19.2 File accessibility
      • 3.19.2.1 Option 1: Local Server
      • 3.19.2.2 Option2: Cloud
  • APPENDICES
    • Appendix A: Auto Execute Filters

GENEYX ANALYSIS

1.0 Geneyx Device Information

1.1 Intended Use

Geneyx Analysis is a powerful diagnostic analysis software specifically designed for in vitro laboratory developed tests (LDTs) within health institutions. It serves as an automated solution for conducting secondary and tertiary analysis of variants identified in gene panels, exomes, and whole genomes, primarily generated through next-generation sequencing (NGS) assays.

The primary objective of Geneyx Analysis is to facilitate the identification and classification of causal variants that have clinical significance. By leveraging advanced algorithms and annotation tools, the software assists healthcare professionals and researchers in making informed decisions related to targeted diagnoses and targeted therapy applications in both hereditary disorders and cancer.

The software offers comprehensive features for variant analysis, including filtering, annotation, variant classification, and report generation. It streamlines the analysis process, providing a standardized and efficient workflow for variant interpretation.

Geneyx Analysis is specifically developed for use by healthcare professionals and researchers who possess the necessary expertise and domain knowledge in genetics, genomics, and molecular diagnostics. It is intended to be utilized within healthcare institutions for diagnostic purposes, aiding in the accurate and timely identification of clinically relevant variants.

It is worth noting that Geneyx Analysis is a specialized software solution and should be used as part of a broader clinical workflow and decision-making process. It is crucial to adhere to established guidelines and best practices in genetic analysis, interpretation, and reporting when utilizing the software.

For specific details on the features, functionalities, and regulatory aspects of Geneyx Analysis, it is recommended to refer to the official documentation provided by the software vendor or consult with their representatives for a comprehensive understanding of its capabilities and implementation requirements.

1.2 Indications for Use

Geneyx Analysis is indeed tailored for research and laboratory settings, specifically for the analysis of human samples in the context of diagnosing rare Mendelian diseases and providing prognostic, diagnostic, and therapeutic guidance. The software is intended to be utilized by trained medical professionals who have expertise in genetics and genomics.

The primary purpose of Geneyx Analysis is to analyze and interpret NGS (Next-Generation Sequencing) aligned reads and variant calls obtained from sequencing data. It is not involved in the sample collection, preparation for sequencing, or primary sequence analysis, such as base-calling from raw instrument data. The software assumes that the input data, including aligned reads and variant calls, have already been generated through established laboratory protocols.

The analysis process within Geneyx Analysis involves applying sophisticated algorithms, variant filtering, annotation, and classification methods to identify potentially causative variants in the context of rare Mendelian diseases. The generated interpretations and reports are designed to be interpreted by healthcare professionals, such as physicians, genetic counselors, or other trained medical experts. The insights and recommendations provided by the software are intended to guide clinical decision-making and facilitate patient management.

Geneyx Analysis can be deployed either on cloud infrastructures or within the infrastructure of clinical laboratories, offering flexibility in terms of implementation options. This allows the software to adapt to different laboratory environments and data management strategies

It is important to note that Geneyx Analysis is a specialized tool that should be used as part of a comprehensive clinical workflow and in conjunction with other relevant clinical and laboratory information. The software is not intended for direct use by patients, as its outputs require interpretation and expertise from healthcare professionals.

For specific details regarding the technical requirements, deployment options, and regulatory considerations of Geneyx Analysis, it is recommended to consult the official documentation provided by the software vendor or engage with their representatives to ensure proper utilization and compliance within the intended laboratory and clinical contexts.

1.3 Contraindications

There are no specific contraindications associated with Geneyx Analysis. When utilizing Geneyx Analysis or any similar diagnostic analysis software, it is crucial to adhere to standard protocols and best practices in the field of genetics and genomics. This includes appropriate validation and quality control processes, compliance with data protection and privacy regulations, and the involvement of trained medical professionals who are knowledgeable in genetic analysis and interpretation.

1.4 Warnings

Geneyx Analysis is specifically designed for use in research and clinical laboratory settings, and it is intended for the analysis of genetic variants in the context of diagnosing rare Mendelian diseases and providing prognostic, diagnostic, and therapeutic guidance. It is primarily used by trained medical professionals, such as physicians, geneticists, and genetic counselors, who have the necessary expertise to interpret and analyze genetic data.

The software’s purpose is not for personal genomics or for diagnosing or interpreting variants in healthy individuals. Its use is focused on identifying and classifying causal variants in patients with suspected genetic disorders or cancer, with the aim of supporting informed decision-making in personalized medicine.

It is important to use Geneyx Analysis or any similar genetic analysis software within its intended scope and in accordance with applicable regulations, guidelines, and ethical considerations. This ensures that the results and interpretations are accurately understood and appropriately utilized by healthcare professionals for patient care.

1.5 Precautions

Important precautions to follow when implementing Geneyx Analysis or any similar laboratory testing software. Customization, calibration, and validation are crucial steps in adapting the software to the specific test environment and ensuring accurate and reliable results. Here are some further explanations and considerations for the precautions you mentioned:

1. Customize and calibrate workflows: It is essential to customize the
   provided workflows (project protocols) to match the specific
   requirements of the genetic test being performed. This includes
   adjusting QC thresholds, analysis parameters, and incorporating
   test-specific annotations and quality filters. The customization
   should be based on comprehensive validation procedures that
   encompass the entire sample preparation, sequencing, secondary
   analysis, and tertiary analysis pipeline. By validating the complete
   workflow, you can ensure the software’s performance is optimized for
   accurate variant detection and interpretation.

   
   

2. Implement cybersecurity measures: To protect the integrity and
   confidentiality of the data processed by Geneyx Analysis, it is
   important to implement robust cybersecurity measures. This includes
   anti-malware software, system firewalls, and other security best
   practices to prevent cybersecurity breaches. Following cybersecurity
   guidance and staying up to date with industry standards can help
   safeguard against unauthorized access, data breaches, and potential
   risks associated with the software.

   
   

3. Secure system access: To maintain the security and privacy of the
   software and the data it processes, it is recommended to avoid
   running the software or server components on directly accessible
   internet-facing hosts. Instead, deploy them within a secure network
   environment with restricted access. Physical and system-level access
   should be limited to authorized individuals only, reducing the risk
   of unauthorized access through external networks or physical
   intrusions.

   
   

By adhering to these precautions, laboratories can mitigate potential risks associated with implementing Geneyx Analysis or similar software as part of their testing process. Following proper validation, cybersecurity measures, and access control protocols helps ensure the accuracy, security, and confidentiality of the genetic data and the results generated for clinical and research purposes.

1.6 Description

Geneyx Analysis is an advanced decision support software and in vitro medical device specifically designed to assist in the diagnosis of rare diseases and provide valuable insights into the prognostic and therapeutic implications of genomic biomarkers. It achieves this by analyzing genomic mutations (variants) detected through Next Generation Sequencing (NGS) instruments.

Laboratories can select from a range of NGS-based assay products to sequence either the entire human genome or a targeted set of genes (gene panels), exomes, or genomes. This sequencing process generates raw variant calls for each patient. Geneyx Analysis seamlessly handles these raw variant calls and offers the following key features:

1. Reading and processing input in standard VCF files, which contain
   both small variants (detectable using short-read NGS data) and CNVs
   (detected through coverage profile analysis of NGS data).

   
   

2. Annotating imported data with comprehensive information from
   numerous public and licensed databases, enhancing the understanding
   of variant characteristics.

   
   

3. Evaluating the quality of samples and variants through a robust set
   of metrics, derived statistics, and intuitive visualizations.

   
   

4. Utilizing advanced algorithms to conduct in silico analyses,
   providing valuable insights into the impact of variants on the
   patient’s genes and their respective functions.

   
   

5. Adhering to industry-standard guidelines for scoring and classifying
   germline variants and CNVs, employing guided workflows and
   auto-scoring algorithms.

   
   

6. Reviewing relevant clinical evidence and literature to assess
   whether a variant meets the criteria for reporting and further
   analysis.

   
   

7. Creating customizable clinical reports that include all selected
   variants, along with patient and sample-level data, facilitating
   effective communication of findings.

   
   

8. Storing variants from previously sequenced samples in a secure
   warehouse, enabling the computation of cohort-level statistics and
   per-variant frequencies for future analyses and research.

   
   

Although Geneyx Analysis comprises several modules catering to diverse market needs, it is developed, deployed, and distributed as a unified software solution, ensuring a seamless and comprehensive user experience.

1.7 System Requirements

Geneyx Analysis offers multiple Cloud installations on the Azure Cloud platform, providing users with a scalable and reliable infrastructure. When deployed on Azure Cloud, the following components are utilized:

1. Web Apps: Geneyx Analysis leverages Azure Web Apps to provide a
   robust and accessible user interface. This enables users to access
   the software securely through web browsers without the need for
   complex local installations.

   
   

2. Storage Account: Azure Storage Account is utilized to store and
   manage data associated with Geneyx Analysis. This includes input
   files, variant data, annotations, and other relevant information.
   The storage account ensures efficient data management and retrieval.

   
   

3. Batch Service: Azure Batch Service is employed to handle
   computationally intensive tasks within Geneyx Analysis. This service
   enables efficient parallel processing and scaling of computational
   workloads, optimizing the performance of data analysis and variant
   processing.

   
   

4. Azure SQL Hyperscale DB: Geneyx Analysis utilizes Azure SQL
   Hyperscale as the database solution to store and manage structured
   data. This highly scalable and fully managed database service
   ensures reliable data storage, retrieval, and query processing for
   efficient analysis.

   
   

5. VMs: Virtual Machines (VMs) on Azure Cloud are utilized to provide
   the necessary computing resources for running Geneyx Analysis. These
   VMs host the software and handle the computational tasks required
   for variant analysis, annotation, and interpretation.

   
   

By leveraging the capabilities of Azure Cloud, Geneyx Analysis benefits from a robust and scalable infrastructure that ensures efficient data management, computational power, and storage. The use of Azure Cloud allows for flexibility, scalability, and automated deployment of updates, providing users with a reliable and up-to-date environment for their genetic analysis needs.

1.8 Cybersecurity Guidance

Establishing a secure network and system environment is crucial to safeguard the sample, genomic data, and patient information processed by Geneyx Analysis. To minimize cybersecurity risks and ensure compliance with privacy regulations like GDPR, the following best practices are recommended:

1. Install Institutional Firewalls: Implement institutional firewalls
   to restrict unauthorized incoming connections to the hosts running
   Geneyx Analysis. This helps protect the system from external threats
   and unauthorized access.

   
   

2. Use Anti-Malware and Endpoint Security Software: Install and
   regularly update anti-malware and endpoint security software to
   prevent system-level exploits that could grant access to the locally
   installed software and data. This helps protect against malware and
   other security threats.

   
   

3. Regularly Backup Data: Back up all input, project, and user data to
   non-network attached mediums. This ensures that data can be
   recovered in the event of ransomware attacks or data loss incidents.
   Regular backups help maintain data integrity and availability.

   
   

4. Implement User Authentication and Access Controls: Provide
   system-level user authentication with a strong password policy and
   consider implementing two-factor authentication for an added layer
   of security. Restrict access to the installed software and data only
   to authorized personnel.

   
   

5. Ensure Physical Security: Implement physical security measures to
   prevent unauthorized access to workstations and servers running
   Geneyx Analysis. This includes securing the physical premises,
   implementing access control systems, and enforcing policies for
   workstation auto-locking after periods of inactivity.

   
   

6. GDPR Compliance: Adhere to GDPR privacy rights when using Geneyx
   Analysis. Create separate analyses for each sample to facilitate
   precise compliance with requests to delete personal data. Update or
   modify personal data within the analysis itself and generate new
   reports when necessary. It’s important to understand and implement
   the full range of responsibilities, policies, and procedures
   required by GDPR to ensure compliance. Consult with network
   administrators, IT specialists, or legal experts to ensure adherence
   to institutional policies and GDPR requirements.

   
   

By following these best practices, laboratories can establish a secure and compliant environment for using Geneyx Analysis, safeguarding sensitive data and protecting the privacy rights of individuals.

1.9 Maintenance and Upgrade Guidance

Geneyx Analysis is designed to undergo validation in coordination with the cycle of re-validation of laboratory processes, ensuring its reliability and accuracy. When using the software, it is important to consider the following points:

1. Stay Updated with Release Notes: Regularly monitor notifications of
   release notes and known issues to stay informed about software
   updates. This helps ensure that any new features or bug fixes do not
   impact production workflows. Keeping track of updates allows for
   timely implementation of improvements and bug fixes.

   
   

2. Update Annotations: Annotations used during analysis can be updated
   by creating a copy of the analysis. This enables users to stay up to
   date with monthly updates of annotation sources like ClinVar and
   OMIM. However, it is essential to evaluate the impact of these
   changes on the specific test being performed. Consider how updated
   annotations may affect the interpretation and reporting of variants.

   
   

3. Re-validation of Workflows: When choosing to update the software, it
   is important to re-validate all production workflows. Use the same
   benchmark samples that were initially used for calibration and
   validation. By comparing the results with expected changes, such as
   updated annotations, algorithm improvements, or changes in the
   laboratory-specific knowledge base, you can ensure the reliability
   and accuracy of the analysis.

   
   

4. System Monitoring and Maintenance: Implement standard system
   monitoring and maintenance practices for the hosts running Geneyx
   Analysis. Regularly monitor volumes containing user data, shared
   application data, and performance metrics. This helps detect any
   issues or degraded performance in a timely manner. Additionally,
   maintain and update security measures and system software patches to
   minimize the risk of exploitation.

   
   

By following these guidelines, laboratories can ensure that Geneyx Analysis remains updated, validated, and secure, enabling reliable and accurate analysis of genomic data.

1.9.1 Clinical Performance & Risk/Benefit Information

Geneyx Analysis has undergone clinical performance evaluation studies conducted with users in their own laboratory settings. These studies have shown that Geneyx Analysis offers several benefits over manual variant interpretation and reporting, including improved efficiency and decision support. Some key points to consider are:

1. Improved Efficiency: The use of Geneyx Analysis automates the
   analysis process, resulting in improved efficiency and reduced time
   to completion compared to manual variant interpretation. This allows
   laboratory professionals to analyze and interpret variants more
   quickly, facilitating faster turnaround times for test results.

   
   

2. Workflow and Decision Support: Geneyx Analysis provides predefined
   workflows and decision support tools, which guide users through the
   analysis process and assist in variant scoring. These workflows help
   streamline the interpretation process and ensure consistent and
   standardized analysis across different users and cases.

   
   

3. Maintaining Quality: The clinical performance evaluation studies
   have demonstrated that the use of Geneyx Analysis does not
   compromise the quality of variant interpretation. The studies have
   not identified any significant drop in quality or unexplained
   discordance in outcomes when compared to manual variant
   interpretation. This indicates that Geneyx Analysis can maintain
   high-quality results while improving efficiency.

   
   

4. Risk-Benefit Assessment: It is important to consider the overall
   risk-benefit ratio when adopting automated software like Geneyx
   Analysis. The demonstrated improvements in efficiency, coupled with
   the maintained quality of variant interpretation, outweigh the
   potential risks associated with using automated software.

   
   

By leveraging Geneyx Analysis, laboratories can benefit from improved efficiency, standardized workflows, and reliable decision support, ultimately enhancing their clinical variant interpretation processes.

1.9.2 Storage Specifications

FASTQ + BAM files:

• Our FASTQ and BAM files are securely stored on AWS S3 in the Ireland
  datacenter. As part of AWS’s redundancy and disaster recovery policy,
  the data is persisted in three availability zones, ensuring data
  redundancy and high availability. It provides an extra layer of
  protection for your files against potential failures or outages.
  Additionally, please note that FASTQ files are archived three days
  after being uploaded, ensuring efficient data management practices.

VCF files:

• For VCF files, we utilize Azure’s Netherlands datacenter. The files
  are stored in a read-access geo-redundant storage (RA-GRS) system
  across two availability zones. This setup ensures data replication and
  redundancy, maximizing data availability and durability. Moreover, the
  storage system maintains six days’ worth of restore points, enabling
  you to access previous versions of the VCF files if needed.

Database:

• Our database is hosted in Azure’s Netherlands datacenter, providing a
  robust and reliable infrastructure for data storage. The database is
  equipped with built-in resilience measures, including seven days’
  worth of restore points. This ensures that in case of any data-related
  issues or accidental modifications, we can restore the database to a
  previous state within the specified timeframe.

By leveraging these cloud providers and their respective datacenters, we can ensure the security, redundancy, and disaster recovery capabilities necessary to safeguard your data. If you have any further questions or concerns about our data storage and backup practices, please feel free to let us know.

1.10 Technical Support and Turnaround Times

1. Technical Support Availability

• Our technical support team is available to assist you during our
  regular business hours, Monday to Friday, from 9:00 AM to 5:00 PM
  (local time).

  
  

• Support requests can be submitted through our designated channels,
  including email, or our online support portal. The online support
  portal can be accessed by clicking on the Help icon in the upper right
  corner. This will connect you with a live representative or will be
  addressed via email.

  
  

• Support contact is support@geneyx.com.

  
  

2. Initial Response Time

• Upon receiving a support request, our team will provide an initial
  response within 24 hours during business days.

  
  

• For requests submitted outside of business hours or on weekends, the
  initial response will be provided on the next business day.

  
  

3. Issue Resolution Timeframes

• We strive to resolve technical issues as quickly as possible, based on
  their severity and complexity.

  
  

• The resolution timeframes are categorized into four priority levels:

  
  

i. Priority 1 (P1): Critical

• Definition: Critical system failure or significant impact on business
  operations.

  
  

• Response Time: Within 2 hours.

  
  

• Resolution Time: Within 24 hours or as mutually agreed upon based on
  the nature of the issue.

  
  

ii. Priority 2 (P2): High

• Definition: Major issue affecting system functionality or performance.

  
  

• Response Time: Within 4 hours.

  
  

• Resolution Time: Within 48 hours or as mutually agreed upon based on
  the complexity of the issue.

  
  

iii. Priority 3 (P3): Medium

• Definition: Issue causing moderate impact on system functionality or
  performance.

  
  

• Response Time: Within 8 hours.

  
  

• Resolution Time: Within 5 business days or as mutually agreed upon
  based on the nature of the issue.

  
  

iv. Priority 4 (P4): Low

• Definition: Minor issue or general inquiries.

  
  

• Response Time: Within 24 hours.

  
  

• Resolution Time: Within 10 business days or as mutually agreed upon
  based on the nature of the issue.

  
  

4. Escalation Process

• If you are not satisfied with the progress or resolution of your
  support request, you have the option to escalate the issue.

  
  

• Escalation contacts and procedures will be provided upon request.

  
  

Please note that the above turnaround times are general guidelines and may vary depending on the specific circumstances and agreement. Our goal is to provide timely and effective technical support to ensure the smooth operation of your systems and minimize any disruptions to your business.

2.0 Introduction to Geneyx Analysis

Geneyx Analysis is a powerful diagnostic analysis software designed for in vitro laboratory developed testing. It serves as an essential tool for automating the analysis of genetic variants detected in gene panels, exomes, and whole genomes obtained through next-generation sequencing (NGS) assays. The primary goal of this analysis is to accurately identify and classify causal variants, enabling healthcare professionals to make well-informed decisions, provide precise diagnoses, and facilitate targeted therapy applications in hereditary disorders.

To cater to the diverse requirements of the clinical testing market, an efficient workflow engine is essential. Geneyx Analysis excels in this aspect by supporting multiple guidelines and accommodating various levels of laboratory-specific customizations within a single solution. This flexibility ensures that the software can adapt to the specific needs and preferences of different laboratories, enabling them to streamline their analysis processes effectively.

One of the key strengths of Geneyx Analysis lies in its automated workflow, which incorporates the guidelines set forth by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) for interpreting NGS results. By automating the interpretation process, the software significantly reduces the complexities and challenges associated with manual interpretation. This standardized and repeatable approach to variant interpretation in a clinical context can be further customized to align with the practices and protocols of individual laboratories.

In summary, Geneyx Analysis offers a comprehensive solution for genetic variant analysis in a clinical setting. Its advanced workflow engine, adherence to industry guidelines, and ability to accommodate laboratory-specific customizations make it an invaluable tool for clinicians and geneticists, providing them with accurate and efficient variant interpretation to support patient care and decision-making.

2.1 QuickStart Guide

2.1.1 Upload Local Fastq Files

Geneyx Analysis simplifies the process of implementing a secondary pipeline for converting fastq files to vcf (variant call format) through its user-friendly interface, eliminating the need for command line expertise. This feature is available as part of the licensed package, and if you wish to add it to your license, you can send an email request to support@geneyx.com.

In addition to its pipeline capabilities, Geneyx Analysis offers the convenience of automatically retrieving fastq files from a cloud infrastructure, known as the 3.10.6 Data Sources. This enables seamless integration with cloud-based storage solutions for effortless batch uploads. You can find detailed information on this feature in the platform’s documentation.

For the purpose of explaining the process of importing a single sample from a local directory, follow these steps:

1. Navigate to the Data Management view within Geneyx Analysis.

   
   

2. Hover over the “Seq. Samples” section, and a menu will appear.

   
   

3. Select “Upload Single Sample” from the menu.

   
   

By choosing this option, you can initiate the upload process for fastq files associated with a single sample. Geneyx Analysis provides a straightforward and streamlined experience for importing and managing your sequencing data, empowering you to effortlessly analyze genetic variants with ease and efficiency.

Upload Single Sample

After initiating the upload process for a single sample in Geneyx Analysis, the next dialog will prompt you to enter a Subject ID. The Subject ID serves as an identifier that is applied at the subject level, allowing you to uniquely identify and track specific individuals or samples within the analysis.

To proceed with the upload, follow these steps:

1. Enter the desired Subject ID in the provided field. Choose an
   identifier that is meaningful and helps you easily identify the
   sample associated with it. It could be a patient ID, sample code, or
   any other relevant identifier.

   
   

2. Once you have entered the Subject ID, click on the “Next” button to
   proceed to the next step in the upload process.

   
   

By providing a Subject ID, you ensure that the uploaded sample is associated with a specific identifier within the Geneyx Analysis platform, facilitating efficient tracking and management of the data throughout the analysis workflow.

Entering Fastq Subject ID

In the subsequent dialog of the upload process, you will be prompted to enter the Serial Number, Expected # Fastq Files, and the Sequencing Target. These details help Geneyx Analysis accurately process and analyze the uploaded fastq files. Here’s how you can provide the required information:

1. Serial Number: The Serial Number refers to the prefix of the fastq
   file names for Illumina FASTQ. In this example, the fastq files are
   named MQ21111B_005.R1.fq.gz and MQ21111B_005.R1.fq.gz. In this case,
   the Serial Number would be “MQ21111B.”

For MGI FASTQ, the Serial Number refers to the sample ID: In this example, the FASTQ files are named V450272805_L01_D12345_1.fq.gz. In this case, the serial Number would be “D12345”.

Enter the appropriate Serial Number in the provided field.

2. Expected # Fastq Files: Specify the number of fastq files
   associated with the sample you are uploading. In your example, there
   are two fastq files. Enter “2” as the Expected # Fastq Files.

   
   

3. Sequencing Target: The Sequencing Target refers to the specific
   genomic regions or targets that were sequenced. If you are working
   with gene panel or exome data, the Enrichment Kit & SMART Filtering
   option allows you to define your target capture regions. However, if
   the sample represents whole genome data, you can leave the default
   setting as it is.

   
   

4. Enrichment Kit & SMART Filtering (for gene panel and exome data): If
   you are analyzing gene panel or exome data, you can specify the
   Enrichment Kit & SMART Filtering options to define your target
   capture regions. This helps in focusing the analysis on specific
   genomic regions of interest. If this is not applicable to your data,
   you can disregard this field.

   
   

Once you have entered the required information, click on the “Next” button to proceed with the upload process. These details will assist Geneyx Analysis in correctly processing and analyzing the fastq files associated with the sample.

Uploading sample information

After entering the necessary sample information, click on the “Next” button to proceed. In the following dialog, you will have the option to select “Upload Local Files.” This selection enables you to browse and select the samples from a local directory on your computer.

Here’s how you can upload the samples from a local directory:

1. Click on the “Upload Local Files” option.

   
   

2. A file browser window will appear, allowing you to navigate to the
   directory where your samples are located.

   
   

3. Browse through your local directories and select the relevant sample
   files that you want to upload to Geneyx Analysis. You can select
   multiple files at once by holding down the Ctrl key (or Command key
   on Mac) while clicking on the files.

   
   

4. Once you have selected the sample files, click on the “Open” or
   “Choose” button (depending on your operating system) in the file
   browser window. This will initiate the upload process.

   
   

5. The selected sample files will be uploaded to Geneyx Analysis.

   
   

6. After the upload is complete, click on the “Next” button to proceed.

   
   

By following these steps, you can upload your samples from a local directory and move forward with the analysis process.

Upload local fastq files

In the final step, you will need to select the Secondary Pipeline for your analysis. Geneyx Analysis offers several options including: “Dragen – hg19” and “Dragen – hg38.” As well as Sentieon hg19 and Sentieon hg38 (NB. Sentieon is for SNV only)

The choice of Secondary Pipeline depends on the reference genome version you want to use for your analysis.

Choose the appropriate Secondary Pipeline based on your specific analysis requirements and reference genome preference. Once you have made your selection, proceed to the next step. continue with the analysis process.

Selecting the secondary pipeline

After clicking “Save,” the system will populate with details related to the sequencing process. This information can be accessed through various sections:

1. Processing Tasks: The Processing Tasks section provides insights
   into the progress of your sample within the processing pipeline. You
   can monitor the different stages of analysis, such as alignment,
   variant calling, and annotation. This allows you to track the status
   of your sample and observe any potential issues or delays.

   
   

2. QC Data: The QC Data section allows you to view sample statistics
   and coverage profiles. It provides valuable information about the
   quality of the sequencing data and the depth of coverage across the
   target regions. This data helps in assessing the reliability and
   accuracy of the analysis results.

   
   

3. Additional QC Metrics: By utilizing the Geneyx Analysis APIs
   (Application Programming Interfaces), you can extract additional QC
   metrics beyond what is displayed in the QC Data section. These APIs
   provide programmatic access to the data, allowing you to retrieve
   specific metrics or integrate them into other workflows or systems.

   
   

4. VCF Samples: Once the variant calling and alignment processes have
   been completed, the VCF Samples section will be populated with the
   relevant files. These files contain the variant information, such as
   the genomic variants identified in the sample and their associated
   annotations. You can access and explore these files to perform
   downstream analysis or interpretation of the genetic variants.

   
   

These sections provide valuable insights and data related to the sequencing and analysis process, allowing you to monitor progress, assess data quality, and access variant information for further analysis and interpretation.

Summary table of the secondary pipeline.

Once the analysis is completed, the output will include several files that are relevant for tertiary analysis:

1. SNV (Single Nucleotide Variant) File: This file contains information
   about single nucleotide variants, also known as point mutations,
   identified in the sample. It provides details such as the genomic
   position, reference and alternate alleles, quality scores, and
   annotations for each variant.

   
   

2. SV (Structural Variant) File: This file contains information about
   structural variants, which are larger genomic alterations such as
   insertions, deletions, duplications, inversions, or translocations.
   It provides details about the structural variants detected in the
   sample, including their genomic coordinates, size, and other
   relevant annotations.

   
   

3. BAM (Binary Alignment Map) File: This file represents the alignment
   of the sequencing reads to a reference genome. It contains the
   aligned reads, along with their qualities and mapping information,
   allowing for visualization and further analysis of the sequencing
   data.

   
   

4. BAI (BAM Index) File: This is the index file associated with the BAM
   file. It enables efficient random access to specific regions of the
   BAM file, facilitating quick retrieval and analysis of specific
   genomic regions.

   
   

To access these files, you can either click on the corresponding file name under the VCF Samples section, which will open a preview or allow you to download the file, or you can navigate to the VCF Samples section in the navigation pane on the left-hand side of the interface. From there, you can select the desired file and access or download it as needed.

These files serve as the foundation for tertiary analysis, where further interpretation, filtering, and annotation of the variants can be performed using specialized software or tools.

VCF Sample output from the secondary pipeline

2.2 Apply VCF from fastq to Protocol

By importing the output files into a protocol for tertiary analysis, you can perform advanced analysis and interpretation of the variants, including filtering, annotation, prioritization, and identification of potential disease-causing variants. These steps allows for a deeper exploration of the genomic data and aids in making informed decisions, diagnoses, and targeted therapy applications in the context of hereditary disorders.

To apply the output files from the previous analysis to a protocol for tertiary analysis, follow these steps:

1. Navigate to the Dashboard view in the Geneyx Analysis interface.

   
   

2. Locate the VCF sample that was generated under the VCF Samples
   section. Next to the sample, you should see an option labeled “New
   Analysis.” Click on this option to initiate the import process for
   tertiary analysis.

   
   

3. A dialog or pop-up window will appear, guiding you through the
   import process. This dialog will allow you to configure the settings
   and parameters for the tertiary analysis.

   
   

4. Follow the prompts and provide the necessary information in each
   step of the import process. This will include selecting the
   appropriate protocol, and setting any additional analysis parameters
   or options.

   
   

Selecting New Analysis for the output VCF created by the secondary

After selecting “New Analysis,” you will be presented with a dialog displaying the available protocols to choose from. A protocol serves as a template for analysis, containing predefined filters and annotations. Default protocols are provided in the categories of Germline, Somatic, and Health Screening. Here are some of the default protocols available:

Germline:

• Single Sample Analysis: This workflow is used to analyze affected
  germline samples that do not have associated maternal or paternal VCF
  files.

  
  

• Trio Analysis: The trio workflow utilizes inheritance models to
  analyze rare Mendelian variants using mother, father, and proband
  samples.

  
  

• Single Sample SNV/SV: This template is used to analyze affected
  germline samples for single nucleotide polymorphisms and structural
  variations.

  
  

• CNV Only Analysis: The CNV workflow is designed to detect large
  structural variations using a specific file format that includes
  information such as chromosome, start, end, effect, score, and copy
  number.

  
  

• Mitochondria Analysis: This protocol is specifically tailored for the
  analysis of mitochondrial variants.

  
  

Somatic:

• Tumor Only Analysis: This template is used to analyze tumor-only
  samples without paired normal/germline samples.

  
  

• Tumor Normal Analysis: This protocol is commonly used to analyze
  somatic samples along with their corresponding germline counterparts.

  
  

• Cancer Screening: This template is used to detect the presence of
  somatic variants that are not isolated from tumor tissue.

  
  

Health Screening:

• Carrier Screening: This protocol is used for the analysis of variants
  that could be inherited and passed on as genetic conditions to
  offspring

When selecting a protocol, consider the specific type of analysis you wish to perform based on your sample and data. These predefined protocols provide a starting point for analysis, but they can also be customized to fit your specific requirements using the 3.10.9 Protocols feature.

Choose the appropriate protocol that aligns with your analysis goals, and then proceed to the next step of the analysis workflow.

Selecting protocol

Once you have selected a protocol, you can enter the subject information in the Subject dialog. This information helps provide context to the analysis and is available throughout the process for reference. It will also be automatically included in the clinical report. When entering details for a new subject, you can provide the following information:

• SUBJECT ID (required): This is a unique identifier for the subject. It
  can be the patient identifier in the EMR or LIMS system, or any other
  identifier that you choose.

  
  

• NAME (optional): The name of the subject.

  
  

• DATE OF BIRTH (optional): The date of birth of the subject.

  
  

• GENDER (optional): Specify the gender as Male or Female. Leave blank
  if the gender is unknown or unspecified.

  
  

• CONSENT – PERSONAL DATA (optional): Specify whether the subject has
  given consent for the use of their personal data, with options such as
  Yes or No. Leave blank if consent status is unknown or unspecified.

  
  

• CONSENT – CLINICAL DATA (optional): Specify whether the subject has
  given consent for the use of their clinical data, with options such as
  Yes or No. Leave blank if consent status is unknown or unspecified.

  
  

• CONSANGUINITY (optional): Use this field to indicate parental
  consanguinity, particularly in cases of rare disease analysis.

  
  

• ETHNICITY (optional): Enter the ethnicity of the subject.

  
  

• PATERNAL ANCESTRY (optional): Specify the ancestry of the subject’s
  father.

  
  

• MATERNAL ANCESTRY (optional): Specify the ancestry of the subject’s
  mother.

  
  

• FAMILY HISTORY (optional): This is a text box where you can add notes
  regarding the family history of the subject.

  
  

By providing these subject details, you enhance the analysis with important contextual information. Once entered, the information will be associated with the subject throughout the analysis process and can be accessed as needed.

Entering in Subject information

After clicking “Next” on the Subject dialog, you will be taken to the Samples page. Since you started a new analysis from an existing sample, the associated information is automatically populated. However, if you need to update the VCF information, you can do so by clicking on the edit icon next to the VCF sample. The available fields for editing include:

• Serial Number: Specify a unique identifier for this analysis. By
  default, a value is generated, but you can replace it with your own
  identifier, such as the ID of the case in your system.

  
  

• RELATION: Specify the relation of the sample to the proband (e.g.,
  mother, father, sibling, etc.).

  
  

• Sequencing Machine: Specify the machine used for automating the DNA
  sequencing process.

  
  

• Enrichment Kit & Filtering: Define the target capture regions using an
  enrichment kit. You can also implement your own BED files in the
  Settings dialog.

  
  

• Genome Build: Select the reference genome assembly build used for
  alignment and mapping.

  
  

• Taken Date: Enter the date when the sample was obtained.

  
  

• Sequence Date: Enter the date when the sample was sequenced.

  
  

• Received Date: Enter the date when the sequencing data was received.

  
  

• Sequencing Target: Specify the NGS data type (e.g., gene panel, exome,
  whole genome).

  
  

• Sample Source: Describe the method by which the DNA sample was
  collected.

  
  

• Notes: Add any additional comments or notes related to the sample.

  
  

• BAM file URL: Provide the URL of the BAM file associated with the
  sample.

  • files must be bgzipped with corresponding index file (tbi)

    
    

  • eg. xxx.bam.gz and xxx.bam.gz.tbi

    
    

• Methylation file URL: Provide the URL of the methylation file, if
  applicable.

  • files must be bgzipped with corresponding index file (tbi)

    
    

  • xxx.bed.gz and xxx.bed.gz.tbi

    
    

• Exclude sample from local allele frequency: Choose whether to exclude
  the sample from local allele frequency calculations. In the event
  where artifact or low quality samples are uploaded, this feature will
  exclude the sample from the local allele frequency calculation. This
  will prevent inflation of local variant allele frequencies.

  
  

By updating these fields, you can ensure that the analysis is performed accurately and with the relevant sample information.

Sample being pulled automatically

After clicking “Next” on the Samples page, you will reach the Clinical Information section. This section allows you to provide additional context about the analysis and subject. Here, you can set the phenotype terms that will be used to score and rank candidate variants during the analysis stage. The following information can be entered:

• Serial Number (required): Specify a unique identifier for this
  analysis. By default, a value is automatically generated, but you can
  replace it with your own identifier, such as the ID of the case in
  your system.

  
  

• Name (optional): Enter the name of the protocol used for the analysis.

  
  

• Owner (Doctor) (optional): Specify the name of the referring clinician
  or the owner of the analysis.

  
  

• Description (optional): Use free text to describe the clinical aspects
  of the case, providing any relevant information.

  
  

• Disease frequency (required): Enter the expected frequency of this
  disease occurring in the population. This information will be applied
  in the filtering process to reduce the number of observed variants
  above the defined threshold.

  
  

• Department: Specify the name of the department associated with the
  analysis.

  
  

• Phenotypes (optional): Enter any biological terms (phenotypes) that
  will be used to score and rank the candidate variants. You can enter
  multiple phenotypes using the Advanced option.

  
  

• To ensure compound terms are recognized properly, use quotation marks
  around the term. For example, if the phenotype is “Developmental
  Delay,” enter it exactly as “Developmental Delay” to ensure the entire
  term is used.

  
  

• After entering the phenotypes, click on Keywords. This will display
  which terms are highly specific, indicating how many genes are
  associated with the given condition. The HPO option allows you to
  enter a list of HPO terms and select all to import.

  
  

By providing this clinical information, you can enhance the analysis process and improve the accuracy of candidate variant ranking based on the specified phenotypes.

Entering in Clinical Information

After clicking “Save” on the Clinical Information page, the VCF sample will be applied to the selected protocol, incorporating the information entered during the import process. This action will lead you to the Analysis Details page, where you can access and review the details of the analysis. The Analysis Details page provides comprehensive information about the analysis, including the applied protocol, subject information, sample details, and any additional clinical information provided. It serves as a hub for monitoring and managing the analysis process, allowing you to track the progress and view the results of the analysis. Further information about the (2.5. Analysis Details page can be found in the section dedicated to it in the manual.

2.3 Upload VCF to Protocol

One the VCF file has been obtained the next step in using Geneyx Analysis is creating an analysis. In Geneyx, an analysis is a mechanism for analyzing a set of data from one or more samples using a template for filtering and annotation to classification and reporting.

To initiate the process of creating a new analysis in Geneyx Analysis, you can follow these steps:

1. Access the Geneyx Dashboard by navigating to the platform’s
   interface.

   
   

2. Look for the option labeled “Click here” or a similar indication
   that prompts you to analyze your own data. This option is typically
   located prominently on the Dashboard page. If samples are already
   loaded, you can select Start New Analysis.

   
   

3. Click on the provided link or button to proceed with creating a new
   analysis.

   
   

By following these steps, you will initiate the process of setting up an analysis in Geneyx Analysis, allowing you to utilize the platform’s filtering, annotation, classification, and reporting capabilities on your data.

Geneyx Analysis Welcome Screen¶

When you open the Analyses dialog in Geneyx Analysis, you will have the option to choose a protocol for your analysis. A protocol serves as a template that includes predefined filters and annotations to guide the analysis process. Geneyx provides default protocols that are categorized into Germline, Somatic, and Health Screening. Here are some examples of the default protocols available:

Germline:

• Single Sample Analysis: This protocol is used to analyze affected
  germline samples that do not have associated maternal or paternal VCF
  files.

  
  

• Trio Analysis: The trio workflow is utilized for analyzing rare
  Mendelian variants by incorporating mother, father, and proband
  samples.

  
  

• Single Sample SNV/SV: This template is specifically designed for
  analyzing affected germline samples to detect single nucleotide
  polymorphisms and structural variations.

  
  

• CNV Only Analysis: The CNV workflow focuses on detecting large
  structural variations that follow a specific file format, including
  information such as chromosome (chr), start and end positions, effect,
  score, and copy number.

  
  

– Mitochondria Analysis: This protocol is tailored for the analysis of mitochondrial variants.

Somatic:

• Tumor Only Analysis: This template is used to analyze tumor-only
  samples that do not have a paired normal or germline sample for
  comparison.

  
  

• Tumor Normal Analysis: The tumor normal workflow is commonly employed
  to analyze somatic samples along with their corresponding germline or
  normal samples.

  
  

• Cancer Screening: This protocol is designed to detect the presence of
  somatic variants that are not isolated from tumor tissue.

  
  

Health Screening:

• Carrier Screening: This template is used for the analysis of variants
  that have the potential to be passed on as inherited genetic
  conditions to offspring during carrier screening.

By selecting the appropriate protocol based on your specific analysis requirements, you can streamline the analysis process and focus on the relevant aspects of your data.

Analyses Dialog¶

When creating a new analysis in Geneyx Analysis, after selecting the protocol, you will be prompted to enter the subject information in the Subject dialog. This information is important for reference throughout the analysis process and will also be included in the generated clinical report. Here are the details that can be entered for a new subject:

• SUBJECT ID (required): This field requires a unique identifier for the
  subject. Typically, it would be the patient identifier used in the EMR
  (Electronic Medical Record) or LIMS (Laboratory Information Management
  System) system. However, it can be any identifier that uniquely
  represents the subject.

  
  

• NAME (optional): You can enter the name of the subject if available.

  
  

• DATE OF BIRTH (optional): This field allows you to provide the date of
  birth of the subject if known.

  
  

• GENDER (optional): You can specify the gender of the subject as Male
  or Female. If the gender is unknown or unspecified, you can leave this
  field blank.

  
  

• CONSENT – PERSONAL DATA (optional): This field allows you to indicate
  whether the subject has given consent for the use of their personal
  data. You can select Yes or No, or leave it blank if the consent
  status is unknown or unspecified.

  
  

• CONSENT – CLINICAL DATA (optional): Similarly, you can specify whether
  the subject has given consent for the use of their clinical data. You
  can select Yes or No, or leave it blank if the consent status is
  unknown or unspecified.

  
  

• CONSANGUINITY (optional): This field is commonly used to specify
  parental consanguinity in cases of rare disease analysis. You can
  enter relevant information if applicable.

  
  

• ETHNICITY (optional): Here, you can provide the ethnicity of the
  subject if known.

  
  

• PATERNAL ANCESTRY (optional): You can enter the ancestry or ethnic
  background of the subject’s father.

  
  

• MATERNAL ANCESTRY (optional): Similarly, you can enter the ancestry or
  ethnic background of the subject’s mother

  
  

• FAMILY HISTORY (optional): This text box allows you to add any
  relevant notes or details regarding the family history of the subject.

  
  

By entering these details, you can provide important context for the analysis and ensure that the generated clinical report includes accurate and comprehensive information about the subject.

Entering Subject Information

After saving the subject information, the next step in Geneyx Analysis is to upload the VCF files. In the SAMPLES dialog, you can click on the “Select Sample” button, which will allow you to browse your local directory to locate and upload the VCF file. Once the VCF file is uploaded, you can define additional parameters specific to the sample. Here are the parameters that can be configured:

• Serial Number: This field allows you to specify a unique identifier
  for this analysis. By default, a value is automatically generated, but
  you can replace it with your own identifier, such as the ID of the
  case in your system.

  
  

• RELATION: This field indicates the relation of the sample to the
  proband (the subject of the analysis).

  
  

• Sequencing Machine: You can specify the machine used to automate the
  DNA sequencing process for this sample.

  
  

• Enrichment Kit & Filtering: This field defines the target capture
  regions and allows you to specify the enrichment kit used. You can
  also implement your own BED files in the Settings dialog.

  
  

• Genome Build: Specify the reference genome assembly build that was
  used for alignment and mapping of the sequencing data.

  
  

• Taken Date: Enter the date on which the sample was obtained.

  
  

• Sequence Date: Provide the date on which the sample was sequenced.

  
  

• Received Date: Enter the date on which the sequencing data was
  received.

  
  

• Sequencing Target: Specify the type of NGS (Next-Generation
  Sequencing) data for this sample.

  
  

• Sample Source: Indicate the method by which the DNA was collected for
  this sample.

  
  

• Notes: This field allows you to add any additional comments or notes
  relevant to this sample.

  
  

• BAM file URL: If available, you can provide the URL of the BAM file
  associated with this sample.

  • Users can also provide http address for CRAM files and Geneyx will
    support visualization in IGV.

    
    

  • files must be bgzipped with corresponding index file (tbi)

    
    

  • eg. xxx.bam.gz and xxx.bam.gz.tbi

    
    

• Methylation file URL: If applicable, you can enter the URL of the
  methylation file associated with this sample.

  • files must be bgzipped with corresponding index file (tbi)

    
    

  • xxx.bed.gz and xxx.bed.gz.tbi

    
    

• Exclude sample from local allele frequency: This option allows you to
  exclude the sample from local allele frequency calculations if
  desired.

  
  

By providing these parameters, you can ensure that the analysis is performed accurately and that the specific details of the sample are taken into account during the analysis process.

Uploading VCF files

In the Clinical Information section of Geneyx Analysis, you can incorporate additional context about the analysis and the subject being analyzed. This section allows you to set phenotype terms that will be used to score and rank candidate variants during the analysis stage.

Here are the components of the Clinical Information section:

• Serial Number (required): Specify a unique identifier for this
  analysis. A default value is automatically generated, but you can
  replace it with your own identifier, such as the ID of the case in
  your system.

  
  

• Name (optional): You can provide a name or title for the analysis,
  which helps in identifying and organizing the analysis.

  
  

• Owner (Doctor) (optional): Enter the name of the referring clinician
  or the doctor responsible for the analysis.

  
  

• Description (optional): This field allows you to provide free text to
  describe the clinical aspects of the case, providing additional
  context and information.

  
  

• Disease frequency (required): Specify the expected frequency of the
  disease occurring in the population. This information will be applied
  as a filter to reduce the number of variants observed above the
  defined threshold.

  
  

• Department: Enter the name of the department associated with the
  analysis.

  
  

• Phenotypes (optional): Here, you can enter any biological term
  (phenotype) that will be used to score and rank the candidate variants
  during the analysis. You can enter multiple phenotypes, and for
  compound terms to be recognized properly, you should enclose them in
  quotation marks. For example, if you want to enter the term
  “Developmental Delay,” you should type it as “Developmental Delay” to
  ensure the entire term is used.

  
  

• Keywords: After entering the phenotypes, you can select the “Keywords”
  option, which will display which terms are highly specific and reflect
  how many genes are associated with the given condition. This
  information can help in refining the analysis and focusing on specific
  aspects.

  
  

• HPO (Human Phenotype Ontology) option: If you have a list of HPO
  terms, you can enter them here and select all to import them.

  
  

Entering Clinical Information 

By providing clinical information and setting phenotype terms, you can enhance the analysis process by incorporating relevant clinical context and enabling the system to prioritize candidate variants based on their association with the specified phenotypes. Once you click Save, the VCF sample will be applied to the protocol with the information entered during import.

2.4 Analysis Details

The Analysis Details page in Geneyx Analysis provides an audit trail and displays important information about the completed analysis. Here are the details that you can find on this page:

Creator and Creation Time: This shows the user who created the case and the timestamp of when it was created.

• Description: If a description was provided during the import process,
  it will be displayed here. This allows for additional information or
  context about the case to be included.

  
  

• Protocol: The selected protocol for the case analysis will be shown.
  It represents the template that was used for filtering and annotation
  during the analysis.

  
  

• Genetic Models: This section displays the genetic models that were
  applied to the sample. Genetic models are integrated at the protocol
  level and help in analyzing and interpreting rare Mendelian variants.

  
  

• Disease Frequency (%): The disease frequency indicates the expected
  frequency of the disease occurring in the population. It is used as a
  threshold to capture rare variants present in the sample.

  
  

• Phenotype: The phenotype terms that were entered during the import
  process will be shown here. These terms were used to score and rank
  candidate variants during the analysis.

  
  

• Version Set: This reflects the version of Geneyx Analysis that was
  used for the analysis.

  
  

• Local Frequency Date: The date of the in-house allele frequency
  database used for the analysis is displayed here. It indicates when
  the local frequency data was last updated.

  
  

• #SNV Samples in Local Frequency: This shows the number of single
  nucleotide variant (SNV) samples present in the in-house allele
  frequency database at the time of analysis.

  
  

• #SV Samples in Local Frequency: This indicates the number of
  structural variant (SV) samples present in the in-house allele
  frequency database at the time of analysis.

  
  

• Case ACMG Date: The date of the ACMG (American College of Medical
  Genetics and Genomics) guidelines annotation that was used in the
  analysis is shown here.

  
  

• Last Modified: This displays the user who made the last modification
  to the analysis and the timestamp of when the modification occurred.
  It helps in tracking any recent changes made to the analysis.

  
  

• Associated samples: Associated samples defined in the protocol will be
  present here. There is also the option to include additional
  associated samples at this stage using the ‘+Add button’

  
  

• Gene panels: Gene panels defined in the protocol will be present here.
  There is also the option to include gene panels at this stage using
  the ‘+Add button’

  
  

The Analysis Details page provides a comprehensive overview of the metrics, settings, and information associated with the completed analysis, allowing users to review and reference these details as needed.

Analysis Details

Clicking on the Analyze icon in the upper right corner of the Analysis Details or accessing it from the Dashboard view will take you to the variant analysis window in Geneyx Analysis. In this window, you can further explore and analyze the variants identified in the analysis. The variant analysis window provides tools and features to investigate and interpret the genetic variants detected in the sample. Here, you can perform various tasks such as:

1. Filtering Variants: Apply filters based on variant properties such
   as allele frequency, variant type, impact, gene region, and more to
   narrow down the list of variants.

   
   

2. Sorting and Ranking Variants: Sort and rank the variants based on
   different criteria such as pathogenicity, predicted impact, allele
   frequency, and functional annotations to prioritize the most
   relevant variants.

   
   

3. Variant Visualization: View detailed information about each variant,
   including genomic position, variant type, allele frequencies in
   databases, predicted functional impact, and associated gene
   annotations.

   
   

4. Variant Annotation: Access comprehensive annotations for each
   variant, such as gene information, functional consequences,
   conservation scores, and predicted pathogenicity.

   
   

5. Gene Analysis: Explore the genes associated with the variants,
   including gene function, known disease associations, pathways, and
   relevant literature.

   
   

6. Variant Filtering and Comparison: Apply additional filters and
   compare variants across different samples or datasets to identify
   shared or unique variants.

   
   

7. Pathway and Functional Analysis: Investigate the functional impact
   of variants on biological pathways and perform enrichment analysis
   to identify affected biological processes.

   
   

8. Visualization Tools: Utilize interactive visualizations, such as
   variant allele frequency plots, gene diagrams, and protein domain
   annotations, to gain insights into the genetic data.

   
   

By navigating through the variant analysis window, users can delve into the genetic variants, examine their potential significance, and gain a deeper understanding of their role in the analyzed sample.

Analyze icon in the upper right corner.

2.5 Variant Analysis Workflow

In Geneyx Analysis, you can effectively manage and filter the large number of variants in a sample using the Filter Chain function. This allows you to apply specific criteria to refine the variants based on selected annotations. Here’s how you can use the Filter Chain:

1. In the variant analysis window, locate the column header for the
   annotation you want to filter by. Click on the filter icon next to
   the column header.

   
   

2. A dropdown menu will appear, showing various filtering options based
   on the selected annotation. Select the desired filtering criteria
   from the available options. For example, you may choose to filter
   variants based on variant type, impact, allele frequency, or any
   other relevant annotation.

   
   

3. After selecting the filtering criteria, Geneyx Analysis will
   automatically apply the filter and display the filtered variants in
   the variant table.

   
   

4. You can add additional filters by repeating the above steps for
   other annotations or criteria. Each filter will be listed on the
   left side of the screen under Column Filters.

   
   

5. To remove or modify a filter, you can click on the “X” icon next to
   the filter in the Column Filters section. This will remove the
   filter and update the variant table accordingly.

   
   

Additionally, if you need to create customized filters with specific combinations of criteria, you can refer to the (3.10.10 Filters section in the Settings window of Geneyx Analysis. The Filter section allows you to build complex filters using logical operators (AND, OR) and define multiple conditions for variant filtering.

Adding an annotation to a Filter Chain¶

By using the Filter Chain function and custom filters, you can effectively narrow down the variants of interest based on your specific criteria and focus on the most relevant variants for further analysis and interpretation.

2.5.1 Visualize in GenomeBrowse

To display the GenomeBrowse visualization tool in Geneyx Analysis, you can follow these steps:

1. In the variant analysis window, locate the variant of interest in
   the variant table.

   
   

2. Click on the location of the variant, which is typically represented
   as a hyperlink or a clickable element in the variant’s genomic
   position.

   
   

3. Upon clicking the variant’s location, Geneyx Analysis will open the
   GenomeBrowse visualization tool. This tool provides a graphical
   representation of the genomic region surrounding the variant,
   allowing you to explore the nearby genes, transcripts, and genomic
   features.

   
   

4. In the GenomeBrowse tool, you can navigate through the genomic
   region using zoom and pan controls to adjust the level of detail and
   explore the surrounding genomic context.

   
   

5. The GenomeBrowse tool may display various genomic annotations, such
   as gene models, known variants, regulatory elements, and other
   relevant information depending on the available data and settings.

   
   

6. You can interact with the GenomeBrowse visualization to explore the
   genomic features and their relationships to the variant of interest.
   For example, you can hover over a gene to view its details, click on
   a known variant to see its information, or adjust the view to focus
   on specific regions of interest.

   
   

The GenomeBrowse tool provides a visual representation of the genomic landscape surrounding a variant, aiding in the interpretation and analysis of the variant within its genomic context.

Adding a GenomeBrowse Tab¶

In Geneyx Analysis, when you open the GenomeBrowse visualization tool to view a selected variant in the context of the genome, chromosome, gene, and protein, you will have the option to customize the annotation view using the settings icon. Here’s how you can access and customize the annotation view:

1. After opening the GenomeBrowse tool by clicking on the location of a
   variant, you will see the schematic view of the variant and its
   genomic context.

   
   

2. On the right side of the GenomeBrowse dialog, you will find a
   settings icon (usually represented by a gear or cogwheel icon).
   Click on this settings icon to open the customization options for
   the annotation view.

   
   

3. The settings dialog allows you to customize various aspects of the
   annotation view. You may have options to enable or disable specific
   annotation sources, adjust the visualization settings, and modify
   the display of genomic features.

   
   

4. Depending on the available customization options in Geneyx Analysis,
   you can configure the annotation view to suit your preferences and
   analysis requirements. For example, you may choose to show or hide
   specific types of annotations, adjust the color scheme or track
   display, and modify the level of detail or zoom level.

   
   

5. Explore the settings dialog and experiment with different
   customization options to optimize the annotation view for your
   analysis needs. The specific options and features available may vary
   depending on the version of Geneyx Analysis and the data sources
   used for annotation.

   
   

By utilizing the settings icon in the GenomeBrowse view, you can tailor the annotation view to focus on the specific information and features that are most relevant to your analysis, enhancing your understanding of the variant within its genomic context.

GenomeBrowse loaded with the selected variant.¶

2.5.2 Desktop IGV Integration

Geneyx Analysis now provides direct integration with IGV (Integrative Genomics Viewer) Desktop, enabling users to visualize local BAM and methylation BED files within their native IGV environment. This feature addresses the common challenge faced by users with local data storage, offering an efficient solution to the bottleneck associated with visualizing BAM files when a cloud environment is not utilized.

Key Functionalities and Operational Details:

• Seamless Navigation and Dynamic View Management: Upon selecting a
  variant location within Geneyx Analysis, the system automatically
  facilitates navigation to the corresponding genomic locus in IGV.
  Subsequent selections of new variant locations perform a reset of the
  previous IGV view, ensuring the relevant data for the newly chosen
  variant is loaded dynamically.

  
  

• Optimized Local Data Access: This integration is particularly
  beneficial for users operating in local or restricted environments. It
  ensures fast, secure, and flexible access to raw read data without the
  necessity of remote upload.

  
  

• Remote Control Configuration: The integration leverages IGV’s
  remote control capabilities via a WebSocket interface, typically on
  port 60151.

  
  

How to Set Up and Use Desktop IGV Integration:

To utilize the Desktop IGV Integration for local BAM visualization, follow these steps:

1. Enable IGV Desktop in Geneyx Analysis:

   1. Click on your user name in the upper right corner of the
      Geneyx Analysis interface.

      
      

   2. Select “Preferences” from the dropdown menu.

      
      

   3. Tick the checkbox labeled “Enable IGV Desktop”.

      
      

Enabling IGV Desktop

2. Install IGV Desktop: Ensure you have the Integrative Genomics
   Viewer (IGV) Desktop application and your BAM and/or methylation
   files are prepared, navigate to a variant location within the Geneyx
   Analysis platform. Upon clicking a variant, the system will give an
   option to automatically navigate IGV to the corresponding genomic
   locus.

Navigating to Desktop IGV

This setup enables seamless visualization of your local BAM and methylation BED files directly in IGV, streamlining your analysis workflow.

2.6 Interpret Results

In Geneyx Analysis, you can specify the variants you wish to report for a sample using the Relevance column. The Relevance column allows you to indicate the significance or relevance of each variant based on your analysis criteria.

Annotating a variant for reporting.¶

Here’s how you can utilize the Relevance column to select variants for reporting:

1. In the variant analysis window, locate the Relevance column. This
   column is typically displayed alongside other annotation columns
   that provide information about the variants.

   
   

2. The Relevance column contains values or options that allow you to
   assign a relevance score or category to each variant. The specific
   options available may depend on the configuration of your analysis
   and the criteria you are using to assess variant significance.

   
   

3. Review the annotations, genomic context, and any other relevant
   information for each variant in the analysis. Based on your
   evaluation, assign a relevance value or category to indicate the
   significance of the variant. This can be done by selecting the
   appropriate option from a drop-down menu or using a predefined
   scoring system.

   
   

4. The relevance values or categories can be used to prioritize or
   filter the variants for reporting. You can choose to report only the
   variants that meet a certain relevance threshold or fall into
   specific categories, depending on your analysis goals and criteria.

   
   

5. By assigning relevance values to the variants, you can identify and
   focus on the most relevant or clinically significant variants in the
   analysis results. This helps streamline the reporting process and
   ensures that the reported variants align with your analysis
   objectives.

   
   

Note that the specific options and criteria for assigning relevance values may vary depending on your analysis settings and the annotation sources used in Geneyx Analysis.

Annotating a variant for reporting

When you click “Save” after applying the relevant information and annotations to the variants in Geneyx Analysis, the system will store this annotation for the analysis. The saved annotation will be associated with the specific variants you have selected and will be automatically included in the generated report.

By saving the annotations, you ensure that the selected variants and their associated information, including the relevance values, are recorded and incorporated into the final report. This helps maintain a comprehensive and accurate documentation of the analysis process and the variants of interest.

The report generated by Geneyx Analysis will include the saved annotations, allowing you to present the findings and relevant details to clinicians, researchers, or other stakeholders. The report may include information such as variant classifications, functional impact, gene annotations, genomic context, and any additional annotations or customizations you have applied.

Remember to review the saved annotations and ensure their accuracy before generating the final report. This step helps maintain the quality and reliability of the reported variants and their associated information.

It’s important to note that the specific report generation process and formatting may vary based on the settings and configurations of your Geneyx Analysis platform. It is recommended to consult the platform’s documentation or reach out to their support team for further guidance on generating and customizing reports.

2.7 Patient Report

Once you click the green Report Preview icon in the upper right corner of the Geneyx Analysis interface, the Report Editor will be displayed. The Report Editor allows you to make final modifications and customizations to the generated report before finalizing and exporting it.

In the Report Editor, you can perform various actions such as:

1. Adding and arranging sections: You can add or remove sections in the
   report and adjust their order according to your preferences.
   Sections can include general information, analysis details, variant
   tables, genomic context, clinical interpretations, and more

   
   

2. Customizing content: You can modify the content of each section,
   including adding or removing text, tables, images, and other
   elements. This allows you to tailor the report to your specific
   requirements and include relevant information.

   
   

3. Formatting and styling: The Report Editor provides options to format
   and style the report, such as adjusting font styles, sizes, colors,
   and alignments. You can also add headers, footers, page numbers, and
   other formatting elements to enhance the visual presentation of the
   report.

   
   

4. Adding comments and annotations: The Report Editor enables you to
   add comments, notes, and annotations to specific sections or
   variants in the report. This can be useful for providing additional
   context, explanations, or recommendations related to the findings.

   
   

5. Previewing and reviewing: You can preview the report in real-time as
   you make modifications to ensure that it reflects the desired format
   and content. This allows you to review and proofread the report
   before finalizing it.

   
   

Once you are satisfied with the modifications made in the Report Editor, you can proceed to finalize and export the report in the desired format, such as PDF, JSON, or HTML. The finalized report can then be shared with relevant stakeholders, such as clinicians, researchers, or patients, to communicate the analysis results and findings effectively.

It’s important to note that the specific functionalities and features of the Report Editor may vary depending on the version and configuration of Geneyx Analysis you are using. It is recommended to refer to the platform’s documentation or consult their support team for detailed instructions on using the Report Editor and generating customized reports.

Generating a report using the Report Preview icon¶

Report Editor

Once you have made the desired modifications to the report using the Report Editor in Geneyx Analysis, you can select the “Save” option. This action will generate a PDF document and TSV (Tab-Separated Values) files containing the filtered variants for each genetic model.

The PDF document will contain the finalized report with all the customized sections, content, and formatting. It serves as a comprehensive and visually appealing representation of the analysis results and findings.

The TSV files, on the other hand, provide structured data in a tabular format, specifically capturing the filtered variants for each genetic model. These files can be useful for further analysis, data integration, or importing the variant information into other software or databases.

After the report and TSV files have been generated, they will be stored in the Reports section of the Geneyx Analysis platform. This allows for easy access and retrieval of the generated reports at a later time. You can refer to the Reports section to view, download, or share the generated PDF and TSV files as needed.

It’s important to note that the specific file formats and storage locations may vary depending on the configuration of Geneyx Analysis or any customization made by the platform administrators. It is recommended to refer to the platform’s documentation or consult their support team for detailed information on the output file formats and storage locations within the platform.

3.0 Geneyx Manual

3.1 Accessing Geneyx

When you access Geneyx Analysis through the URL https://analysis.geneyx.com/, you may come across a series of dialogs before you can open or create a project. Let’s take a look at these dialogs:

1. Login or Signup: If you’re a registered user, you’ll need to log in
   using your credentials. If you’re new to Geneyx, you can sign up for
   an account to gain access to the platform.

   
   

2. Welcome Dialog: Upon logging in, you’ll be greeted with a welcome
   dialog that provides an overview of Geneyx Analysis and its
   features.

   
   

3. Project Dashboard: Once you’ve selected or created a project, you’ll
   be directed to the project dashboard. This is the main interface
   where you can manage and access various components of your project,
   such as data sources, analyses, reports, and settings.

   
   

These dialogs serve as initial steps to ensure a smooth onboarding process and to help users navigate the Geneyx Analysis platform effectively. They provide essential information and options for users to get started with their projects and utilize the platform’s functionalities.

3.1.1 Login and Register Dialog

The first dialog you will encounter is the login or register prompt. To log in to your existing Geneyx account, you can use the following URL: https://analysis.geneyx.com/account/logon. If you are registering for the first time, you can access the registration page at: https://analysis.geneyx.com/account/register.

Login:

If you already have a Geneyx account with a valid license, enter your email address and password in the provided fields. You also have the option to check the “Remember Me” box, which will allow Geneyx to automatically open for future access. Once you have entered your account information, click on the “Log In” button to proceed.

Log in or register for a Geneyx Account¶

Register:

If you don’t have an existing account, click on the “Don’t have an account? Sign up for free!” option, which will redirect you to the registration form. Fill out the form with the required information and make sure to read and accept the license agreement. Once all the required fields are filled in and the license agreement is accepted, the “Log In” button will become active.

You also have the option to check the “Remember Me” box, which will enable automatic opening of Geneyx for future access. After filling in your account information, click on the “Log In” button to proceed.

3.1.2 Navigating Geneyx Analysis

Geneyx Analysis offers a comprehensive navigation console located on the left-hand side of the interface, providing easy access to various sections and functionalities. Each section serves a specific purpose and allows intuitive exploration of your data. Here are the details of each field:

3.2. Dashboard: Provides an overview and summary of relevant information. For more detailed information, refer to the Dashboard section.

Analysis:

• 3.3. Analysis: Allows you to manage and view the analyses
  performed on your data. Further information can be found in the
  Analyses section.

  
  

• 3.4. VCF Samples: Provides access to the Variant Call
  Format (VCF) samples, allowing you to explore and analyze genetic
  variants. More details can be found in the VCF Samples section.

  
  

• 3.5. Reports: Enables the management and generation of
  reports based on your analysis results. Refer to the Reports section
  for additional information.

  
  

• 3.6. Subjects: Allows you to manage and organize subjects
  or individuals within your dataset. For more details, refer to the
  Subjects section.

  
  

Data Management:

• 3.7.1. Processing Tasks: Provides access to
  processing tasks, allowing you to monitor and manage various data
  processing operations. Additional information can be found in the
  Processing Tasks section.

  
  

• 3.7.2 Seq Samples: Allows management of sequencing
  samples, including importing, organizing, and analyzing genomic data.
  Refer to the Seq. Samples section for further details.

  
  

• 3.8. Batches (fastq) : Provides a way to group and
  manage related samples or analyses in batches. For more information,
  see the Batches section.

  
  

3.9. Variant Browser Offers a powerful tool for exploring and investigating genetic variants. It allows filtering, annotation, and visualization of variants. More details can be found in the Variant Browser section.

3.10. Settings: Provides options for customizing your Geneyx Analysis account, including preferences, permissions, and other settings.

3.11 Accounts: Allows management of user accounts and access privileges. Refer to the Accounts section for additional information.

Geneyx Analysis Navigation Window

The navigation console offers a centralized and intuitive way to access all aspects of your data and analysis within the Geneyx Analysis platform. Furthermore, if there are any specific questions, you can always use the Help icon in the upper right corner, which will direct you to a support specialist.

3.2 Dashboard

Clicking the Dashboard icon on the left-hand side will display the most recent analyses and VCF samples in the account, and hovering over the Dashboard icon will provide the ability to Start New Analysis or go into Account features.

Dashboard View

3.2.1 Dashboard View

In the Dashboard view of Geneyx, you’ll find two main sections: Analyses and VCF Samples. These sections serve different purposes and provide important information for managing and analyzing genetic data. Here’s a breakdown of each section and the details they display:

1. Analyses:

Analyses are preconfigured workflows that incorporate specific filter logic and genetic models based on the utilized protocol. When an analysis is initiated, it goes through an annotation process, which involves adding relevant annotations to the genetic variants. Once the annotation process is complete, you can analyze the data further by clicking the “Analyze” option.

Analyses are displayed in a table format, sorted with the most recent analysis at the top. The columns displayed in the table include:

• MODIFIED: This column indicates the date of the most recent
  modification to the analysis.

  
  

• ANALYSIS: The analysis ID is displayed as a hyperlink. Clicking on it
  will take you to the 2.5. Analysis
  Detailsdirectory for that specific case.

  
  

• #SAMPLES: This column shows the number of samples present in the
  analysis.

  
  

• STATUS: It represents the current status of the case, which can be
  modified in the Analysis Details window or during the analysis
  process.

  
  

• TAT: Number of days waiting for analysis out of ‘required TAT’ as
  defined at protocol level

  
  

• ASSIGNMENT: If multiple users have access to the account, sample
  assignments can be performed with administrative capabilities.
  3.2.7 Account options provide more details
  on this feature.

  
  

• SUBJECT: The subject identification is displayed in this column.

  
  

• ANALYZE: Clicking on this option will direct you to the variant
  interpretation interface specific to that case.

  
  

Analyses dialog

2. VCF Samples

The VCF Samples section in Geneyx displays a table format view of the uploaded VCF (Variant Call Format) files or samples. This section provides an overview of the available samples for analysis and annotation. If a VCF sample is present in the account, you can click on “New Analysis” to select a protocol and create a new analysis specifically for that sample.

The columns in the VCF Samples section include:

• MODIFIED: This column displays the date of the last modification
  performed on the sample, indicating when any changes or updates were
  made.

  
  

• VCF SAMPLE: The VCF sample identification is displayed in this column.
  It helps to identify and differentiate between multiple samples within
  the account.

  
  

• SUBJECT: The SUBJECT column represents a unique identifier assigned to
  each VCF sample. It helps in tracking and organizing samples based on
  their specific subjects or individuals.

  
  

• GENOME BUILD: This column indicates the reference genome assembly that
  was used for the VCF sample analysis. It specifies the genome version
  against which the genetic variants were compared.

  
  

• VERSION SET: The VERSION SET column displays the version of Geneyx
  Analysis that was used to annotate the data of the VCF sample. It
  helps to keep track of the software version used for analysis.

  
  

• STATUS: The STATUS column provides information about the progress of
  the annotation step for the sample. It notifies whether the annotation
  process for the sample has been completed or is still ongoing.

  
  

• PERMISSION: The PERMISSION column indicates the designation of sample
  accessibility. It reflects the permissions assigned to each sample,
  determining who can access and work with the sample data. The
  permissions can be updated in the VCF Sample dialog, allowing you to
  control sample access and sharing.

  
  

VCF Samples dialog

By navigating through the Analyses and VCF Samples sections in the Dashboard, you can access and manage your genetic data effectively, initiate analyses, track modifications, and delve into the variant interpretation interface for further analysis.

3.2.2 Start New Analysis

A new analysis can be initiated by clicking on the Start New Analysis icon, in the upper right corner of the Dashboard view. The following steps will guide the process of importing variants into a protocol.

Start New Analysis

3.2.3 Protocol Selection

After selecting Start New Analysis, the user will be required to select the protocol. A protocol is a template for analysis which includes predefined filters and annotations. Default protocols are provided and categorized into Germline, Somatic, and Health Screening and if you want to customize or modify a protocol, this can be done in the 3.10.9 Protocols section in Settings.

Default protocols include:

Germline:

• Single Sample Analysis: Analyzes affected germline samples without
  associated maternal or paternal VCF files.

  
  

• Trio Analysis: Utilizes inheritance models to analyze rare Mendelian
  variants using mother, father, and proband samples.

  
  

• Single Sample SNV/SV: Analyzes affected germline samples for single
  nucleotide polymorphisms and structural variations.

  
  

• CNV Only Analysis: Detects large structural variations in a specific
  file format that includes information such as chr, start, end, effect,
  score, and copy number.

  
  

• Mitochondria Analysis: Specifically used for the analysis of
  mitochondrial variants.

  
  

Somatic:

• Tumor Only Analysis: Analyzes tumor-only samples without a paired
  normal/germline sample.

  
  

• Tumor Normal Analysis: Commonly used to analyze somatic samples
  together with their germline counterparts.

  
  

• Cancer Screening: Detects the presence of somatic variants not
  isolated from tumor tissue.

  
  

Health Screening:

• Carrier Screening: Analyzes variants that could be passed as an
  inherited genetic condition to offspring.

Protocol selection

3.2.4 Subject Information

In the Subject dialog, you can provide general details about the subject of the analysis. These details are important for reference throughout the analysis process and are automatically included in the clinical report.

Here are the fields that can be entered for new subjects:

• SUBJECT ID (required): This is a unique identifier for the subject. It
  is commonly the patient identifier used in the Electronic Medical
  Record (EMR) or Laboratory Information Management System (LIMS), but
  it can be any identifier you choose.

  
  

• NAME (optional): You can enter the name of the subject if available.

  
  

• DATE OF BIRTH (optional): Enter the date of birth of the subject if
  known.

  
  

• GENDER (optional): Specify the gender of the subject as Male or
  Female. If the information is unknown or unspecified, you can leave
  this field blank.

  
  

• CONSENT – PERSONAL DATA (optional): Indicate whether the subject has
  provided consent for the use of their personal data by selecting Yes
  or No. If the consent status is unknown or unspecified, you can leave
  this field blank.

  
  

• CONSENT – CLINICAL DATA (optional): Specify whether the subject has
  provided consent for the use of their clinical data by selecting Yes
  or No. If the consent status is unknown or unspecified, you can leave
  this field blank.

  
  

• CONSANGUINITY (optional): This field is commonly used to indicate
  parental consanguinity in cases of rare disease analysis. You can
  provide relevant information if applicable.

  
  

• ETHNICITY (optional): Enter the ethnicity of the subject if known.

  
  

• PATERNAL ANCESTRY (optional): Specify the ancestry or origin of the
  subject’s father.

  
  

• MATERNAL ANCESTRY (optional): Specify the ancestry or origin of the
  subject’s mother.

  
  

• FAMILY HISTORY (optional): You can add any notes or details regarding
  the family history of the subject in the provided text box.

  
  

Subject Information

Users can also select an existing subject if one has already been created. This option requires previous samples to have been imported into the account. In this case, once Subject ID is selected, there will be a dropdown displaying the most recent submissions. As the name is entered, it will populate with the available choices. Once selected, all associated information will be applied.

Existing Subject

By entering this information for the subject, you ensure that it is captured and utilized throughout the analysis process. This information is valuable for interpreting the genetic data and generating a comprehensive clinical report.

3.2.5 Importing Samples

In the Samples section, you have the option to either upload a new sample by clicking on “Select Sample” next to the relevant sample or select an existing pre-loaded sample for the analysis using the “All Sample” option. Clicking on “Browse” will enable navigation to a local directory to select a given VCF file. Once a new VCF file is uploaded into the system, you can define additional parameters for the sample. These parameters include:

• Serial Number: You can specify a unique identifier for this analysis.
  By default, a value is automatically generated, but you have the
  flexibility to replace it with your own identifier, such as the ID of
  the case in your system.

  
  

• RELATION: This field allows you to specify the relation of the sample
  in reference to the proband (e.g., mother, father, sibling, etc.) if
  applicable.

  
  

• Sequencing Machine: You can indicate the machine used to automate the
  DNA sequencing process for this sample.

  
  

• Enrichment Kit & Filtering: This parameter defines the target capture
  regions for the sample. You can also implement your own BED files if
  needed. See 3.10.1 Enrichment Kits & Smart
  Filtering in the Settings dialog.

  
  

• Genome Build: Specify the reference genome assembly build that was
  used for alignment and mapping of the sample.

  
  

• Taken Date: Enter the date when the sample was obtained or collected.

  
  

• Sequence Date: Provide the date when the sample was sequenced.

  
  

• Received Date: Enter the date when the sequencing data was received.

  
  

• Sequencing Target: This field represents the Next-Generation
  Sequencing (NGS) data type associated with the sample.

  
  

• Sample Source: Specify the method by which DNA was collected for this
  sample (e.g., blood, tissue, saliva, etc.).

  
  

• Notes: You can include any additional comments or relevant information
  about the sample in this field.

  
  

• Methylation file URL: If applicable, you can provide the URL of the
  methylation file associated with the sample. URL file must be present
  on http or https and enable public access to the file.

  • files must be bgzipped with corresponding index file (tbi)

    
    

  • xxx.bed.gz and xxx.bed.gz.tbi

    
    

• BAM file URL: If applicable, you can provide the URL of the BAM file
  associated with the sample.

  • files must be bgzipped with corresponding index file (tbi)

    
    

  • eg. xxx.bam.gz and xxx.bam.gz.tbi

    
    

• URL file must be present on http or https and enable public access to
  the file.

  
  

• Exclude sample from local allele frequency: This option allows you to
  exclude the sample from local allele frequency calculations if
  desired.

  
  

VCF Sample upload

Alternatively, if you want to use a VCF sample that is already present in the account, you can select the Search option. This will display a drop down menu for you to select from.

Upload of existing VCF sample

If you wish to modify the associated information, you can click on the edit icon next to the sample name.

Edit option for existing sample.

By entering these parameters, you can provide detailed information about the sample, which will contribute to the accuracy and comprehensiveness of the analysis.

3.2.6 Clinical Information

In the Clinical Information section, you can provide additional context about the analysis and subject, which will assist in the interpretation of the results. Here is the information that can be entered:

• Serial Number (required): Specify a unique identifier for this
  analysis. A default value is automatically generated, but you can
  replace it with your own identifier, such as the ID of the case in
  your system.

  
  

• Name (optional): Enter the name of the protocol used for the analysis.
  This helps identify the specific analysis protocol being applied.

  
  

• Owner (Doctor) (optional): Enter the name of the referring clinician
  or the owner of the analysis. This provides information about the
  responsible healthcare professional.

  
  

• Description (optional): You can provide free text to describe the
  clinical aspects of the case. This allows you to include relevant
  details or observations about the patient’s condition.

  
  

• Disease frequency (required): Specify the expected frequency of the
  disease occurring in the population. This information is used in the
  filtering process to reduce the number of variants observed above the
  defined threshold. It helps prioritize variants based on their
  relevance to the disease.

  
  

• Department(optional): Enter the name of the department or unit
  associated with the analysis. This provides organizational context for
  the analysis.

  
  

• Phenotypes (optional): Enter any relevant biological terms
  (phenotypes) that will be used to score and rank the candidate
  variants. You can enter multiple phenotypes using the Advanced option.
  To ensure proper recognition of compound terms, such as “Developmental
  Delay,” enclose the term in quotation marks (e.g., “Developmental
  Delay”). After entering the phenotypes, you can select “Keywords,”
  which will display highly specific terms and indicate how many genes
  are associated with each condition.

  • Clicking on HPO will also provide the ability to enter in a list of
    HPO terms.

Entering clinical information

By entering the clinical information accurately, you enhance the analysis process and improve the interpretation of the genetic variants. Once completed you will be directed to the 2.5. Analysis Details page for this case.

3.2.7 Account

Hovering over the Dashboard icon will provide a tool tip option for Accounts, this will direct you to the Usage Dashboard. The Usage Dashboard in Geneyx Analysis offers comprehensive and visually appealing reports on the activities carried out within your Geneyx Analysis account. It provides valuable insights into various workflows conducted, enabling effective management of your genetic analysis processes. Here are the key features and benefits of the Usage Dashboard:

• Detailed and Colorful Reports: The dashboard presents detailed reports
  in a visually appealing manner, making it easy to understand and
  analyze the data. The use of colors enhances the visual experience and
  enables quick identification of trends and patterns.

  
  

• VCF Sample Import by Sequencing Target: The dashboard provides
  information on VCF samples imported based on sequencing targets. This
  feature allows you to track and monitor the distribution of samples
  across different sequencing targets, giving you insights into the
  utilization of specific genetic analysis workflows.

  
  

• Distribution of Samples Processed using Secondary Pipelines: This
  offers insights into the distribution of samples processed using
  secondary pipelines. This feature helps you understand how samples are
  being processed through different secondary analysis pipelines,
  providing a deeper understanding of your analysis workflow efficiency.

  
  

• Total Number of Analyses Created based on Protocols: The dashboard
  displays the total number of analyses created, categorized by
  different protocols. This information gives you a clear overview of
  the usage of specific analysis protocols, allowing you to evaluate
  their popularity and assess their effectiveness.

  
  

• Longitudinal Analysis and Peak Seasons: This provides a longitudinal
  view of the data, enabling you to identify peak seasons or periods of
  high analysis activity. This insight helps in resource planning,
  ensuring optimal allocation of computational resources and staff
  during busy periods.

  
  

• Management Tool: The Usage Dashboard serves as a powerful management
  tool, providing valuable data for decision-making, resource
  optimization, and process improvement. It allows you to track the
  usage of the Geneyx Analysis platform, monitor the performance of
  different workflows, and make informed decisions based on data-driven
  insights.

  
  

Usage Dashboard

By leveraging the Usage Dashboard, you can effectively manage and optimize your genetic analysis processes, leading to improved efficiency, resource allocation, and decision-making within your Geneyx Analysis account.

3.2.7.1 Account Management

In the upper right corner of the Usage Dashboard, you will find the Account Management icon. This feature is accessible to account administrators, who are designated by Geneyx support during the creation of the account. Account administrators have exclusive privileges and can perform various administrative tasks. Here’s how the Account Management feature works:

• Accessing Account Management: By clicking on the Account
  Management icon, account administrators can access the
  administrative functions of the Geneyx Analysis account. This option
  is specifically available to administrators, ensuring proper control
  and management of the account.

  
  

• Inviting Administrators and Users: With administration credentials,
  account administrators can invite other administrators and general
  users to the account by clicking on the Invite icon.

  
  

It is important to note that in order to invite someone to the account, they must first be invited as a general user.

Once the user is added to the account as a general user, administrator privileges can then be granted to provide them with additional administrative capabilities.

Inviting someone as an administrator without first inviting them as a user will not give them access to the account.

• By inviting additional administrators, you can delegate administrative
  responsibilities and distribute the workload effectively. Inviting
  general users allows collaboration and participation in the analysis
  processes.Administering User Roles and Groups: Within the Account
  Management feature, administrators have the ability to assign roles
  and permissions to different users. This ensures that each user has
  appropriate access levels and restrictions based on their
  responsibilities and requirements. User roles can be customized to
  control access to sensitive data and critical functions.

  
  

• Managing User Accounts: Account administrators can also manage user
  accounts within the Account Management section. This includes actions
  such as creating new user accounts, modifying user information, and
  disabling or removing user accounts as needed. Effective management of
  user accounts ensures the security and integrity of the Geneyx
  Analysis platform.

  
  

• Monitoring and Oversight: The Account Management feature provides
  administrators with a comprehensive view of the user activity and
  account usage. It allows administrators to monitor user interactions,
  track analysis processes, and ensure compliance with organizational
  policies and guidelines.

  
  

By utilizing the Account Management feature, account administrators can maintain control over the Geneyx Analysis account, invite and manage users effectively, and ensure the smooth functioning of the platform. This feature empowers administrators with the necessary tools to administer and govern the account according to their organization’s requirements.

Account Management

3.2.8 Roles

When you click on the Roles option in the Account Management dashboard, a dialog will appear where you can create specific permissions that can be assigned to users within the account. Here are the available roles and their corresponding permissions:

• Analyst: Users with the Analyst role have the ability to work with
  samples assigned to them within protocols. They can apply these
  samples to protocols and access analyses that are specifically
  assigned to them.

  
  

• Assign Group: This role allows a user to assign samples to specific
  groups within the organization. They have the authority to organize
  samples into different groups based on organizational requirements or
  criteria.

  
  

• Assign Users: Users with the Assign Users role can assign samples to
  specific users within the organization. This role enables them to
  delegate samples to specific individuals based on their
  responsibilities or expertise.

  
  

• Data Management Operate: This role grants access to all samples in the
  account, but users with this role are restricted from modifying
  analyses and generating reports. They can perform data management
  tasks such as organizing and manipulating samples, but they do not
  have the ability to make changes to analyses or generate reports.

  
  

• Data Management View: Users with the Data Management View role have
  access to all samples in the account, but they can only view the data
  in a viewer mode. They do not have editing privileges and are limited
  to viewing and reviewing the samples and associated information.

  
  

• Viewer: The Viewer role provides access to all samples in the account
  but restricts users to viewing only. They cannot make any
  modifications to the samples or perform any actions beyond viewing.
  This role is suitable for users who need to review or analyze the data
  without the need for editing or modifying it.

  
  

These predefined roles and their associated permissions provide flexibility in assigning appropriate access levels to different users within the organization. By assigning specific roles to users, administrators can ensure that each user has the necessary privileges and restrictions to perform their designated tasks effectively while maintaining data integrity and security.

Here is a table view that details functionalities of each:

Permissions available

3.2.9 Assign VCF Samples

To delegate samples to individual users or groups, you can utilize the VCF level and Analysis Details window. Here’s a step-by-step guide for delegating at the VCF level:

• Navigate to the VCF Sample section in the console, located on the
  left-hand side.

  
  

• Identify and select the sample you wish to delegate by clicking on it.

  
  

• Scroll down to the Permissions section.

  
  

• Click on the permissions, which will prompt a new dialog to appear.

  
  

• In the dialog, you can assign the sample to a specific user or group.

  
  

Once assigned, only the designated user or group will have access to that particular sample.

VCF Sample delegation

By delegating samples in this manner, you can ensure that access to sensitive data is restricted to authorized individuals or specific groups within your organization.

3.2.10 Analysis Delegation

To assign an analysis to an individual user, follow these steps:

• Click on “Analyses” in the navigation console to access the list of
  analyses.

  
  

• Select the analysis you want to assign by clicking on it.

  
  

• You will be directed to the Analysis Details window, which provides
  detailed information about the analysis.

  
  

• In the upper right corner of the Analysis Details window, you will
  find an option to assign the analysis.

  
  

• Click on the assignment option, and a dialog box will appear.

  
  

• From the dialog box, you can select the individual user to whom you
  want to assign the analysis.

  
  

• After making the selection, save the changes.

  
  

By assigning the analysis to a specific user, you can ensure that it is directed to the appropriate individual within a group. This can help with task management, accountability, and efficient workflow distribution.

Analysis Delegation

3.2.11 Usage

The Usage Dashboard includes an icon in the upper right corner that allows users to access the usage statistics of their account. Clicking on this icon will display the usage statistics, and on the right side, there is a filter option that enables users to set a specific period of time for the statistics. Once the filter is applied, users can differentiate between the following categories:

• Analyses: This category represents the number of analyses generated by
  applying a VCF to a protocol.

  
  

• VCF Samples: It includes the count of VCF samples that were uploaded
  into the account. However, VCF samples derived from internal fastq
  processing are excluded from this count.

  
  

• Seq Samples: This category displays the number of subjects created
  from the fastq processing pipeline.

  
  

• Processing Time: It reflects the time taken for a fastq file to
  complete processing and generate a VCF file.

  
  

• Annotation Pipeline: This category indicates the time taken to
  annotate a VCF file.

  
  

The table in the Usage Dashboard presents the following fields:

• Date: Represents the date when the data was applied or entered into
  the system.

  
  

• Category: Indicates the specific category to which the data belongs.

  
  

• Details: Clicking on this field allows users to navigate to the
  specific details associated with the data.

  
  

• Processing Time: If applicable, this field shows the processing time
  consumed for the respective data file.

  
  

• Storage: Reflects the storage space consumed by the data file.

  
  

• Source: Represents the source from which the sample was derived.

  
  

• Target: Specifies the sequencing target of the sample.

  
  

• VCF Sample: Displays the name or identifier of the VCF sample that was
  applied to the specific category.

  
  

• User: Indicates the user responsible for entering the data file into
  the system.

  
  

Usage Dashboard

If the user selects “Summary” in the upper right corner of the Usage Dashboard, it will display a comprehensive summary report for the selected timeline. This summary report offers an easy-to-read overview of sample usage, processing activities, time consumption, and storage allocation within the specified period.

The summary report provides valuable insights into the following aspects:

• Sample Usage: It presents an overview of the number of samples
  utilized within the specified timeframe, giving users an understanding
  of the volume of data processed.

  
  

• Processes: This section highlights the different processes performed
  during the specified period, such as analysis generation, VCF sample
  uploads, and subjects created from the fastq processing pipeline. It
  provides a summary of the key activities carried out within the
  account.

  
  

• Time Consumption: The summary report includes information on the time
  consumed by specific processes, such as processing time for fastq
  files and annotation pipeline time for VCF files. This helps users
  identify areas where more time is being utilized and optimize their
  workflow accordingly.

  
  

• Storage Allocation: It displays the storage space allocated or
  consumed by the data files within the specified timeline. This
  information allows users to track their storage usage and manage their
  resources effectively.

  
  

By selecting the “Summary” option, users can easily access a concise and informative report that provides a snapshot of their sample usage, processing activities, time utilization, and storage allocation. This feature enables users to quickly assess and evaluate the overall account performance and resource utilization.

By providing these detailed usage statistics, the Usage Dashboard enables users to track and analyze their account activity, resource utilization, and processing times across different categories and data types.

3.3 Analysis

The analysis console serves as a comprehensive repository for all the data generated within the account, including samples, analyses, and reports. It provides users with unrestricted access to their data, ensuring that they can retrieve and review it whenever needed, as long as their account has an active license. The analysis console comprises the following key fields:

Analyses: This section displays a list of all the analyses conducted within the account. It provides an overview of the analysis projects, allowing users to access and review specific analysis details.

VCF Samples: The VCF samples field presents a collection of the uploaded VCF (Variant Call Format) samples in the account. These samples contain genetic variant information that can be utilized for further analysis and interpretation.

Reports: In this section, users can find a compilation of generated reports. These reports may include clinical summaries, variant annotations, and other relevant information derived from the analysis process. Accessing reports enables users to review and share valuable findings.

Subjects: The subjects field encompasses the details of individual subjects involved in the analyses. It includes information such as subject IDs, names, demographics, and clinical data. Users can reference this information to maintain context and track the subjects throughout the analysis workflow.

Analysis console

By organizing and presenting data within these fields, the analysis console provides a centralized location for users to manage and retrieve their genetic analysis data efficiently and without any limitations imposed on data volume.

3.3.1 Analyses

This section provides a comprehensive list of all analyses conducted within the account, offering an overview of analysis projects for easy access and review of specific analysis details. The table is sorted by default in descending order of the most recent modification, ensuring that the latest analyses are displayed at the top. The following fields are available:

• NAME: Displays the name of the analysis along with the designated
  panel or focus. Clicking on the name of the analysis will bring you to
  the 2.5. Analysis Details window.

  
  

• PROBAND SAMPLE: Indicates the name of the affected VCF samples that
  serve as the primary subject of the analysis. Clicking on the
  hyperlink will bring you to the 3.4.1 VCF
  Details detail page.

  
  

• SUBJECT ID: Represents a unique identifier assigned to each VCF
  sample. Typically, this identifier corresponds to the patient’s
  identification in EMR or LIMS systems, enabling seamless integration.
  Clicking on the subject will bring you to the
  3.6. Subjects page.

  
  

• PROTOCOL: Specifies the protocol utilized for the analysis, outlining
  the predefined filters and annotations employed.

  
  

• PHENOTYPES: If applicable, this field captures the phenotypic terms
  associated with the analysis, providing additional context or criteria
  for variant analysis.

  
  

• STATUS: Reflects the current status of the analysis, providing
  information on its progress or completion.MODIFIED: Indicates the date
  of the last modifications made to the analysis, enabling users to
  track the timeline of updates and changes.

  
  

Analyses window

These fields collectively offer a comprehensive overview of the analyses conducted within the account, facilitating efficient analysis management and tracking of key information.

3.3.2 Analysis Details

The Analysis Details page serves as an informative hub for the specific analysis. This page provides an audit trail of the metrics and details associated with the analysis. The following information is available:

• Created By and Creation Time: It displays the user who created the
  analysis and the timestamp indicating when it was created.

  
  

• Description: If provided during import, the description field captures
  any additional information or notes related to the case.

  
  

• Protocol: This field indicates the specific protocol that was selected
  for the analysis. The protocol determines the set of guidelines and
  criteria used for variant analysis.

  
  

• Genetic Models: It lists the genetic models applied to the sample,
  which are integrated at the protocol level. These models define the
  inheritance patterns considered during the analysis.

  
  

• Default Genetic Model: The default genetic model is the primary model
  displayed when opening the variant analysis interface for the
  analysis. It serves as the starting point for variant interpretation.

  
  

• Disease Frequency (%): This field captures the expected frequency of
  the disease in the population. It is used as a filter to prioritize
  rare variants present in the sample.

  
  

• Phenotype: It shows the phenotype information entered during the
  import process, providing context and additional clinical details
  about the case.

  
  

• Version Set: The Version Set reflects the specific version of the
  Geneyx Analysis software used for the analysis.

  
  

• Local Frequency Date: This field indicates the date of the in-house
  allele frequency database used for the analysis, which informs the
  interpretation of variant frequencies.

  
  

• #SNV Samples in Local Frequency: It displays the number of single
  nucleotide variant (SNV) samples present in the in-house allele
  frequency database at the time of analysis.

  
  

• #SV Samples in Local Frequency: This field shows the number of
  structural variant (SV) samples present in the in-house allele
  frequency database at the time of analysis.

  
  

• Case ACMG Date: It represents the date of the ACMG (American College
  of Medical Genetics and Genomics) guidelines annotation that was used
  in the analysis.

  
  

• Last Modified: This section indicates the user who made the last
  modification to the analysis and the timestamp of the modification.

  
  

Analysis Details page

• Associated samples: Associated samples defined in the protocol will be
  present here. There is also the option to include additional
  associated samples at this stage using the ‘+Add button’

  • To add additional samples, cick the ‘Edit Associated Samples’ button

• Then ‘+Add’ to include additional samples. When all samples have been
  added, click ‘Done Adding Additional Samples’ and then ‘Analyse’

• Gene panels: Gene panels defined in the protocol will be present here.
  There is also the option to include gene panels at this stage using
  the ‘+Add button’

The Analysis Details page provides a comprehensive overview of the analysis, including its origin, applied metrics, and important timestamps. This information helps users track the progress and modifications made to the analysis, ensuring transparency and accountability throughout the workflow as well as an ‘Analysis notes’ feature to allow users to leave timestamped, user-tagged comments at the analysis level

3.3.3 Creating a Focused Workflow

In the Analysis Details window, located to the left of the Analyze icon, you will find a target icon that allows you to create a focused analysis specifically for the uploaded VCF sample. By selecting the target icon, you can utilize existing filtering options to create a new analysis based on specific criteria.

One option is to select an existing enrichment kit, which provides predefined target regions for analysis. This allows you to focus your analysis on specific genomic regions of interest that are covered by the chosen enrichment kit.

Creating a Focused workflow

Alternatively, if you prefer to define a new target region, you have the flexibility to do so. This means you can customize the analysis by specifying your own set of genomic regions to be included as the target. By defining a new target region, you can tailor the analysis to your specific research or clinical requirements.

When creating a New Target Region, the user will be prompted to enter the following information:

• Name: Provide a name for the enrichment kit you are creating. This
  helps in identifying and referencing the kit later.

  
  

• Description: Add detailed information about the enrichment kit, such
  as its purpose, target regions, or any other relevant details.

  
  

• Include Ref/Ref: Selecting this option will capture homozygous
  reference calls in the analysis. It ensures that variants with
  homozygous reference genotypes are included in the analysis results.

  
  

• Include all modifiers: Enabling this option will capture all
  regulatory elements outside of coding regions. It ensures that
  regulatory elements, such as enhancers or promoters, are considered
  during the analysis.

  
  

• Genes/Genomic Regions: Enter a list of genes or genomic regions that
  you want to include in the target region. This allows you to focus the
  analysis specifically on these genes or regions of interest.

  
  

• CADD Scores: CADD scores are part of the smart filtering capabilities,
  which can be used to prefilter variants based on a CADD threshold.
  CADD scores are computed for single nucleotide variants and
  insertions/deletions in the human genome. You can set a specific
  PHRED-scaled score threshold to filter variants based on their
  deleteriousness. Variants with scores above the threshold will be
  included in the analysis.

  
  

• HOM Allele Count: This prefiltering option allows you to filter out
  variants that have a homozygous allele count in the gnomAD database
  greater than a defined value. It helps in excluding variants that are
  commonly observed in the population.

  
  

• HET Allele Count: Similar to the HOM Allele Count, this option allows
  you to filter variants based on their heterozygous allele count in the
  gnomAD database. Variants with a count greater than the defined value
  will be excluded from the analysis.

  
  

• HEMI Allele Count: This prefiltering option is specifically for
  variants on the X chromosome. It filters variants based on their
  hemizygous allele count in the gnomAD database. Variants with a count
  greater than the defined value will be filtered out.

  
  

By providing these options during the creation of a New Target Region, users can customize the analysis to focus on specific genomic regions, apply smart filtering based on CADD scores, and set prefiltering criteria to refine the variant selection process.

Creating a New Target Region

The ability to create a focused analysis using existing filtering or defining a new target region provides users with flexibility and control over the analysis process. This feature allows for more precise and customized analyses based on the specific genomic regions or enrichment kits of interest.

3.3.4 Creating a Copy of Analysis

On the Analysis Details page, located to the left of the Analyze icon, you will find a “Create Copy” option. Selecting this option allows you to create a copy of the analysis with the most recent version of the software, including updated annotations and new features, if available.

By choosing to create a copy, you can ensure that your analysis benefits from the latest advancements in the software and incorporates the most up-to-date annotations. This process maintains all the associated data from the original analysis while updating the annotations to reflect the most recent version.

Create Copy of Analysis

Creating a copy of the analysis with updated annotations and features enables you to leverage the improved functionality and information provided by the latest software version. It allows you to stay current with advancements and enhancements in the analysis process, ensuring that you have access to the most accurate and comprehensive results

3.3.5 Analysis History

On the Analysis Details page, you can find an “Analysis History” option located to the left of the Analyze icon. By selecting this option, you gain access to an audit trail that showcases the chronological sequence of steps performed during the analysis.

The Analysis History feature is valuable from a management perspective as it provides insights into the actions taken throughout the analysis process. It allows you to track the progress of the analysis, review the specific steps that were executed, and understand the order in which they occurred.

By examining the Analysis History, you can gain a comprehensive overview of the analysis workflow, ensuring transparency and accountability. This feature facilitates effective project management by allowing you to monitor the analysis’s progression, identify potential issues or bottlenecks, and assess the overall efficiency of the process.

Analysis History

The Analysis History serves as a useful tool for tracking and documenting the analysis’s evolution, enabling effective collaboration, communication, and decision-making among team members involved in the project.

3.3.6 Recalculate Local Frequency

On the Analysis Details page, you will find an option called “Recalculate Local Frequency” located to the left of the Analyze icon. By selecting this option, you can initiate the recalculation of the local allele frequency within the analysis.

The local allele frequency refers to the frequency of specific genetic variants observed within the analyzed samples. By recalculating the local allele frequency, you ensure that the analysis incorporates the most up-to-date information regarding variant frequencies.

When you choose to recalculate the local allele frequency, the analysis will consider the most recent samples that have been applied to the analysis. This ensures that the frequency calculations are based on the latest available data.

It’s important to note that it may take up to 24 hours for the local allele frequencies to be updated. This timeframe allows sufficient time for the system to process and incorporate the new sample data into the frequency calculations.

By utilizing the “Recalculate Local Frequency” option, you can ensure that the analysis reflects the most current and accurate information regarding variant frequencies, providing you with more reliable and relevant results.

3.4 VCF Samples

The VCF Samples field in the account showcases the collection of uploaded Variant Call Format (VCF) samples, which contain valuable genetic variant information for further analysis and interpretation. The field includes the following details for each VCF sample:

• ID: Represents the name of the VCF file, serving as a unique
  identifier. Clicking the hyperlink will navigate to the
  3.4.1 VCF Details window.

  
  

• SUBJECT: Displays the unique identifier assigned to the VCF sample,
  typically corresponding to the patient’s identification or a specific
  reference in the EMR or LIMS system. Clicking the hyperlink will
  navigate to the 3.6.1 Subject Details window.

  
  

• GENOME BUILD: Specifies the reference genome assembly utilized for
  alignment and mapping during the sequencing process.

  
  

• SEQUENCING TARGET: Indicates the scope of the sequencing, such as
  Whole Genome, Exome, Gene Panel, Targeted Region, or Clinical Exome.

  
  

• SAMPLE SOURCE: Describes the source from which the sample was derived,
  encompassing categories such as Germline, Tumor Biopsy, Blood, Buccal,
  Mitochondria, Saliva, Fetal, Other, and Parents.

  
  

• TAKEN DATE: Represents the date when the sample was received or
  obtained for analysis.

  
  

• SEQUENCE DATE: Reflects the date when the sample underwent the
  sequencing process.

  
  

• EXCLUDE SAMPLE FROM LOCAL ALLELE FREQUENCY: Provides an option to
  exclude the sample from local allele frequency calculations.

  
  

• CREATED BY: Specifies the user who imported the VCF file into the
  system.

  
  

• CREATED: Indicates the date when the VCF file was initially uploaded.

  
  

• MODIFIED BY: Displays the user who made modifications or updates to
  the VCF file.

  
  

• MODIFIED: Represents the date of the last modification or update made
  to the VCF file.

  
  

To modify the details of a specific VCF file, an edit option is available on the right side of each VCF file entry. Clicking on this option will open a new interface where the appropriate modifications can be made to the VCF sample details. Please see 3.4.1 VCF Details for available fields.

VCF Samples window

This comprehensive set of fields and the edit functionality provide users with control and flexibility in managing and updating the details of their VCF samples within the system.

3.4.1 VCF Details

The VCF details page can be accessed either in the Dashboard or in the VCF Samples section by clicking on a specific VCF sample ID. This will direct you to a directory containing various information and files related to the sample, including clinical records, associated data files, applied analyses, annotation history, annotation information, and QC data (if available).

VCF Details

Here are the details of each field:

Details: Clicking on the edit option next to the VCF Sample name provides the ability to update VCF level information. The fields include:

• Serial Number: You can specify a unique identifier for this analysis.
  By default, a value is automatically generated, but you have the
  flexibility to replace it with your own identifier, such as the ID of
  the case in your system.

  
  

• RELATION: This field allows you to specify the relation of the sample
  in reference to the proband (e.g., mother, father, sibling, etc.) if
  applicable.

  
  

• Sequencing Machine: You can indicate the machine used to automate the
  DNA sequencing process for this sample.

  
  

• Enrichment Kit & Filtering: This parameter defines the target capture
  regions for the sample. You can also implement your own BED files if
  needed. See 3.10.1 Enrichment Kits & Smart
  Filtering in the Settings dialog.

  
  

• Genome Build: Specify the reference genome assembly build that was
  used for alignment and mapping of the sample.

  
  

• Taken Date: Enter the date when the sample was obtained or collected.

  
  

• Sequence Date: Provide the date when the sample was sequenced.

  
  

• Received Date: Enter the date when the sequencing data was received.

  
  

• Sequencing Target: This field represents the Next-Generation
  Sequencing (NGS) data type associated with the sample.

  
  

• Sample Source: Specify the method by which DNA was collected for this
  sample (e.g., blood, tissue, saliva, etc.).

  
  

• Notes: You can include any additional comments or relevant information
  about the sample in this field.

  
  

• Methylation file URL: If applicable, you can provide the URL of the
  methylation file associated with the sample. URL file must be present
  on http or https and enable public access to the file.

  • files must be bgzipped with corresponding index file (tbi)

    
    

  • xxx.bed.gz and xxx.bed.gz.tbi

    
    

• BAM file URL: If applicable, you can provide the URL of the BAM file
  associated with the sample. URL file must be present on http or https
  and enable public access to the file.

  • files must be bgzipped with corresponding index file (tbi)

    
    

  • eg. xxx.bam.gz and xxx.bam.gz.tbi

    
    

• For further details on configuring access to BAM and methylation files
  please see section: 3.17.2 Providing BAM & methylation tracks for
  samples not processed by GeneyxAWS
  Configuration

  
  

• Exclude sample from local allele frequency: This option allows you to
  exclude the sample from local allele frequency calculations if
  desired.

  
  

File(s): This section displays all the files associated with the sample. Please note that BAM and BAI files are available only for samples that have undergone the secondary pipeline initiated in Geneyx Analysis.

Permissions: This feature enables users with assigning privileges to assign the sample to a specific group or user within the account. It provides control over the access and ownership of the sample within the system.

VCF Sample Details, File(s), and Permissions

Analyses: This section provides a comprehensive list of all the analyses that have been performed on the VCF sample. By clicking on the hyperlink, you can navigate to the Analysis Details window, where you can explore the specific details of each analysis.

Annotation History: This feature presents a detailed history of the annotation steps carried out on the sample using the designated enrichment kit. The information includes:

• Version Set: Indicates the version of the application that was
  utilized during the annotation process. Clicking on the link will
  provide access to all versions of the annotation sources used.

  
  

• Pipeline: Specifies the data file that was employed in the annotation
  pipeline.

  
  

• Status: Displays the current status of the annotation pipeline,
  indicating whether it is in progress or completed.

  
  

• Timestamp: Indicates the date and time when the annotation process was
  initiated.

  
  

• Lines: Represents the number of variant lines in the annotated VCF
  file.

  
  

• Variants: Indicates the total number of variants captured in the
  annotation pipeline.

  
  

• #AA Changes: Reflects the count of amino acid changes detected during
  the annotation process.

  
  

• Median Depth: Provides the median depth value calculated from all the
  captured variants.

  
  

For each entry in the annotation history, there are several actions and downloadable files available, including:

• TSV: This option enables you to download the entire annotated VCF file
  in TSV format.

  
  

• Art: By selecting this, you can download an artifact file associated
  with the annotation.

  
  

• Log: This allows you to download the annotation log file, providing a
  detailed record of the annotation process.

  
  

• Delete: Clicking on this option will delete the annotation history for
  the specific sample, if desired.

  
  

QC Data: This section provides valuable information regarding the quality control (QC) metrics of the sample, specifically related to its coverage. These metrics are derived from the BAM file and are applicable when using the secondary pipeline in Geneyx. The fields in this section include:

• Passed: Indicates the total number of positions that were captured in
  the sample.

  
  

• Fail: Represents the number of positions that did not meet the QC
  metrics of the secondary pipeline and failed the quality check.

  
  

• Mapped: Indicates the number of positions that were successfully
  mapped during the alignment process.

  
  

• Aligned: Reflects the number of positions that were aligned correctly.

  
  

• Paired: Represents the number of positions where the reads were paired
  correctly.

  
  

• BED Pos: This field provides the count of positions captured within
  the defined target region, based on the applied enrichment kit.

  
  

• Mean: Specifies the mean coverage of the sample, which is the average
  depth of coverage across all positions.

  
  

• %5X: Indicates the percentage of positions covered at a depth of 5
  times or more.

  
  

• %20X: Represents the percentage of positions covered at a depth of 20
  times or more.

  
  

• %50X: Indicates the percentage of positions covered at a depth of 50
  times or more.

  
  

Analyses, Annotation History, and QC Data

These metrics provide insights into the coverage and quality of the sequencing data for the sample, giving an indication of its reliability and suitability for further analysis.

3.5 Reports

The Reports section presents a collection of generated reports that encompass clinical summaries, variant annotations, and other pertinent information derived from the analysis process. Users can access these reports to review and share valuable findings. The fields included in this section are as follows:

Created: Represents the date and time when the report was created, providing a timestamp for reference.

Subject: Displays the unique identifier assigned to the samples or subjects associated with the report. Clicking the hyperlink will navigate to the 3.6.1 Subject Details window.

Analysis: Specifies the name or identifier of the analysis from which the report was generated. Clicking this link will navigate to the 3.3.2 Analysis Details window.

Summary: Provides a concise summary of the analysis, encapsulating key findings and relevant information.

Report: Presents the report itself in PDF format, enabling users to view and review the comprehensive analysis report. Provided are the downloadable reports in PDF, word, or JSON.

Data file: Offers an excel or TSV file containing the detailed findings and data derived from the analysis, allowing for further exploration and analysis if required.

Report dialog

The Report window also includes “Reports for Review” and “Reports History” under the Report icon in the navigation window. The comprehensive Report History consolidates all generated reports, while Reports for Review strategically filters reports with a Sub-status assignment excluding “Closed.” This intuitive organization streamlines accessibility and retrieval of relevant reports.

By organizing and presenting these reports in a structured manner, users can conveniently access and retrieve important insights from their analyses. The availability of both PDF reports and Excel data files ensures flexibility and ease of sharing information with colleagues or other stakeholders.

3.6 Subjects

The Subjects field provides comprehensive information about individual subjects involved in the analyses. This data includes subject IDs, names, demographics, and clinical information, offering users the ability to maintain context and track subjects throughout the analysis workflow. The available fields in this section are as follows:

• ID: Represents the unique identification assigned to the subject
  within the system. The hyperlink will navigate to the 3.6. Subjects
  window.

  
  

• NAME: Displays the name of the patient or subject.

  
  

• DATE OF BIRTH: Specifies the date of birth for the subject, providing
  essential demographic information.

  
  

• CONSENT – PERSONAL DATA: Indicates the consent status regarding the
  usage of personal data for the subject.

  
  

• CONSENT – CLINICAL DATA: Represents the consent status regarding the
  usage of clinical data for the subject.

  
  

• CREATED BY: Identifies the user who created the subject entry in the
  system.

  
  

• CREATED: Displays the date and time when the subject was initially
  entered into the system.

  
  

• MODIFIED BY: Indicates the user who made modifications to the subject
  information.

  
  

• MODIFIED: Represents the date and time when the subject information
  was last modified.

  
  

Additionally, each subject entry includes a delete option, allowing users to remove subjects from the internal database if necessary.

Subjects window

By organizing and presenting subject details in this manner, users can easily access and manage subject information throughout the analysis process, ensuring effective tracking and reference.

3.6.1 Subject Details

The Subject details page can be accessed either in the Dashboard or in the Subject window by clicking on a specific Subject ID. This will direct you to a directory containing various information and files related to the Subject, including clinical records, associated data files, applied analyses, and sequencing samples.

Subject details window

In this dialog users can update Subject information, including:

• SUBJECT ID (required): This is a unique identifier for the subject. It
  is commonly the patient identifier used in the Electronic Medical
  Record (EMR) or Laboratory Information Management System (LIMS), but
  it can be any identifier you choose.

  
  

• NAME (optional): You can enter the name of the subject if available.

  
  

• DATE OF BIRTH (optional): Enter the date of birth of the subject if
  known.

  
  

• GENDER (optional): Specify the gender of the subject as Male or
  Female. If the information is unknown or unspecified, you can leave
  this field blank.

  
  

Note: If left blank, gender is assumed as female and variants on chrX will be annotated as homozygous. If ‘Male’ then chrX variants will be annotated as hemizygous.

• CONSENT – PERSONAL DATA (optional): Indicate whether the subject has
  provided consent for the use of their personal data by selecting Yes
  or No. If the consent status is unknown or unspecified, you can leave
  this field blank.

  
  

• CONSENT – CLINICAL DATA (optional): Specify whether the subject has
  provided consent for the use of their clinical data by selecting Yes
  or No. If the consent status is unknown or unspecified, you can leave
  this field blank.

  
  

• CONSANGUINITY (optional): This field is commonly used to indicate
  parental consanguinity in cases of rare disease analysis. You can
  provide relevant information if applicable.

  
  

• ETHNICITY (optional): Enter the ethnicity of the subject if known.

  
  

• PATERNAL ANCESTRY (optional): Specify the ancestry or origin of the
  subject’s father.

  
  

• MATERNAL ANCESTRY (optional): Specify the ancestry or origin of the
  subject’s mother.

  
  

• FAMILY HISTORY (optional): You can add any notes or details regarding
  the family history of the subject in the provided text box.

  
  

Users also have the option to add or create a new clinical record for the VCF sample. Any updates made to this field will be automatically applied to the analysis and will be reflected in the generated report. The fields for the clinical record include:

• Record Date: This is the date when the clinical information was
  provided or documented.

  
  

• Description: Users can provide a clinical summary or description of
  the subject’s condition or relevant information.

  
  

• Phenotype Codes: This field enables users to enter a list of Human
  Phenotype Ontology (HPO) phenotypes associated with the subject. HPO
  phenotypes are standardized terms used to describe observable
  characteristics or symptoms related to genetic disorders or diseases.

  
  

Create a new Clinical Record

By including this information in the clinical record, users can enhance the analysis and reporting process, ensuring that the relevant clinical details and phenotypic information are captured and considered during the interpretation of the genomic data.

Users are also empowered with the capability to access and manage applied analyses for the subject, VCF samples, and sequencing samples. This functionality allows users to make modifications or delete the applied analyses as needed, providing greater control and flexibility in the analysis process. For information related to Seq. Samples, please see 3.7.2 Seq Samples section.

Subject derived Analyses, VCF Samples, and Seq Samples

3.6.2 VUS Monitor

Keeping track of variant classification changes can be a daunting task. To simplify this process, we have implemented an auto-notification feature that alerts you whenever variants undergo classification changes according to ClinVar. This proactive notification provides comprehensive details, including a direct link to the analysis, enabling you to perform retrospective analyses with ease and precision.

If a variant has been assigned as a VUS in the ‘Annotate variant’ pop-up in an analysis, it will automatically be integrated into the VUS monitor. In the next update of the application, these newly added variants will appear and a notification provided in the toolbar.

Notification of variant classification update.

Specifically, there will be an Update Classification section which will show the updated classification according to ClinVar.

A variant can also be easily investigated by clicking the refresh icon on the last column. This will update the analysis with the given variant and include the latest release of the annotation sources.

3.7 Data Management

The Data Management interface serves as a powerful tool for implementing secondary pipelines, specifically for generating VCF files from fastq data. This functionality leverages the efficient Illumina DRAGEN pipeline for accurate variant calling. Users can take advantage of this feature by manually uploading fastq files or utilizing batch upload capabilities through integration with a cloud infrastructure hosting the fastq files. It’s important to note that access to this feature is typically limited to licenses that support fastq upload and processing, ensuring optimal performance and compatibility. Total FASTQ file size per sample should not exceed 120Gb.

3.7.1 Processing Tasks

The Processing Tasks interface provides detailed information on the conversion progress from fastq files to VCF files. To facilitate easier navigation, this dialog offers the option to filter tasks based on date and category. In the upper right corner of the interface, users can select either “Upload Single Sample” or “Batch Upload” to initiate the variant calling pipeline for the desired samples.

Furthermore, the interface also displays information about the data storage created from the fastq files. This allows users to track and manage the storage associated with the processed files.

Within the Processing Tasks interface, you’ll find the following relevant fields:

• DATE: The date when the processing task was initiated.

  
  

• SAMPLE ID: The unique identifier assigned to the sample.

  
  

• SUBJECT: The subject associated with the sample.

  
  

• BATCH: The title of the batch workflow, if applicable.

  
  

• STATUS: The current status of the pipeline, indicating whether the
  task is in progress or completed.

  
  

• DETAILS: Provides additional information and specific details about
  the pipeline and its execution.

  
  

• PIPELINE: Specifies the pipeline that was utilized for variant calling
  and subsequent annotation.

  
  

• PROTOCOLS: Identifies the specific protocol that was employed for
  variant analysis.

  
  

• GENOME BUILD: Indicates the reference genome assembly that was used
  during the variant calling process.

  
  

Processing Tasks

Overall, the Processing Tasks interface offers a user-friendly and comprehensive view of the fastq to VCF conversion process, giving users control over their data and facilitating efficient management of variant calling tasks.

3.7.2 Seq Samples

The Seq. Samples interface presents a comprehensive list of all sequencing samples that have been created, along with their associated clinical information. The table includes the following columns:

• ID: This column displays the unique identifier for each sequencing
  sample, known as the Sequencing Sample ID.

  
  

• Sample: The Sample column provides various details about the sample,
  including:

  • Consent: Indicates whether consent has been provided for the sample.

    
    

  • Target: Specifies the sequencing target of the sample.

    
    

  • Source: Indicates the source of the sample.

    
    

  • Relation: Describes the relationship of the sample, if applicable.

    
    

  • Enrichment Kit: Specifies the enrichment kit that was used for
    sequencing.

    
    

  • Collection ID: This column displays the collection sample ID
    associated with the sequencing sample.

    
    

  • Files: Indicates the number of fastq files associated with the
    sample.

    
    

  • Taken: Specifies the date when the sample was taken.

    
    

  • Sequenced Date: Indicates the date when the sample was sequenced.

    
    

  • Received: Specifies the date when the sample was received.

    
    

  • Created: Indicates the date when the sequencing sample was initially
    created.

    
    

  • Modified: Specifies the date when modifications were last made to
    the sequencing sample.

    
    

• Subject

  • ID: Represents the unique identification assigned to the subject
    within the system. The hyperlink will navigate to the 3.6. Subjects
    window.

    
    

  • NAME: Displays the name of the patient or subject.

    
    

  • DATE OF BIRTH: Specifies the date of birth for the subject,
    providing essential demographic information.

    
    

  • CONSENT – PERSONAL DATA: Indicates the consent status regarding the
    usage of personal data for the subject.

    
    

  • CONSENT – CLINICAL DATA: Represents the consent status regarding the
    usage of clinical data for the subject.

    
    

  • CREATED BY: Identifies the user who created the subject entry in the
    system.

    
    

  • CREATED: Displays the date and time when the subject was initially
    entered into the system.

    
    

  • MODIFIED BY: Indicates the user who made modifications to the
    subject information.

    
    

  • MODIFIED: Represents the date and time when the subject information
    was last modified.

    
    

• Phenotypes: If phenotypic terms and HPO ontologies have been entered
  for the sequencing sample, this column will display the associated
  phenotypes. It provides valuable information about the observed
  characteristics or traits related to the sample, as well as the
  hierarchical structure of the Human Phenotype Ontology (HPO) terms
  used to describe those phenotypes.

  
  

• QC Data

  
  

  • Passed: Indicates the total number of positions that were captured
    in the sample.

    
    
  
  

  • Fail: Represents the number of positions that did not meet the QC
    metrics of the secondary pipeline and failed the quality check.

    
    
  
  

  • Mapped: Indicates the number of positions that were successfully
    mapped during the alignment process.

    
    
  
  

  • Aligned: Reflects the number of positions that were aligned
    correctly.

    
    
  
  

  • Paired: Represents the number of positions where the reads were
    paired correctly.

    
    
  
  

  • BED Pos: This field provides the count of positions captured within
    the defined target region, based on the applied enrichment kit.

    
    
  
  

  • Mean: Specifies the mean coverage of the sample, which is the
    average depth of coverage across all positions.

    
    
  
  

  • %5X: Indicates the percentage of positions covered at a depth of 5
    times or more.

    
    

  • %20X: Represents the percentage of positions covered at a depth of
    20 times or more

    
    

  • %50X: Indicates the percentage of positions covered at a depth of 50
    times or more.

    
    

Sequencing Sample dialog

3.7.3 Upload Single Sample (fastq)

This section provides a step-by-step guide on initiating a secondary pipeline to convert fastq files to VCF format for a single sample. The process is applicable to both local and remote fastq files. If your fastq files are stored on a cloud infrastructure, you will need to configure the Data Source in the Settings dialog before proceeding.

Once the setup is complete, you can proceed with initiating the pipeline. Click on the Upload Single Sample icon in the Seq. Samples or through the Processing Tasks window. Follow the instructions provided in the interface to select and upload the fastq files. Make sure to provide accurate and relevant information, such as consent status, sequencing target, sample source, and any other required fields.

Upload Single Sample icon.

The next sections will cover the steps and information for each section.

3.7.4 Entering Subject Information (fastq)

When you click on the Upload Single Sample icon, you will be prompted to provide the necessary Subject information. This will allow you to create a New Subject or use an Existing Subject. The fields include:

• Subject ID (required): This field is used to assign a unique
  identifier to the subject.

  
  

• Name (optional): You can enter the name of the subject if available.

  
  

• Date of Birth (optional): If known, you can specify the date of birth
  of the subject.

  
  

• Gender (optional): You can select the gender of the subject as Male or
  Female. If the gender is unknown or unspecified, you can leave this
  field blank. It’s important to note that selecting the appropriate
  gender is required if you’re interested in generating a CNV (Copy
  Number Variation) panel of normals to call events in non-autosomal
  chromosomes.

  
  

• Consent – Personal Data (optional): This field allows you to specify
  whether consent has been given for the use of personal data associated
  with the subject.

  
  

• Consent – Clinical Data (optional): You can indicate whether consent
  has been given for the use of clinical data related to the subject.

  
  

• Consanguinity (optional): This field is commonly used to specify
  parental consanguinity in cases of rare disease analysis.

  
  

• Ethnicity (optional): You can enter the ethnicity of the subject if
  known.

  
  

• Paternal Ancestry (optional): If available, you can provide
  information about the paternal ancestry of the subject.

  
  

• Maternal Ancestry (optional): If known, you can provide details about
  the maternal ancestry of the subject.

  
  

• Family History (optional): This field allows you to include any
  additional family history information relevant to the subject.

  
  

By providing the necessary Subject information in these fields, you can ensure proper identification and contextual information for the uploaded single sample.

Subject information

By providing the necessary Subject information in these fields, you can ensure proper identification and contextual information for the uploaded single sample.

3.7.5 Entering Seq Samples Details (fastq)

The Sequencing Sample Details section contains important fields that provide essential information about the sample. Here are the details of each field:

• Serial Number: The Serial Number refers to the prefix of the fastq
  file names for Illumina FASTQ. In this example, the fastq files are
  named MQ21111B_005.R1.fq.gz and MQ21111B_005.R1.fq.gz. In this case,
  the Serial Number would be “MQ21111B.”

For MGI FASTQ, the Serial Number refers to the sample ID: In this example, the FASTQ files are named V450272805_L01_D12345_1.fq.gz. In this case, the serial Number would be “D12345”.

• Use Consent: Indicate whether you give consent for your colleagues to
  use the sample.

  
  

• Expected # fastq files: Specify the number of fastq files available
  for the sample.

  
  

• Sequencing Target: Select the target of the sequencing for the sample.
  This includes options such as Whole Genome, Exome, Gene Panel, Target
  Region, or Clinical Exome.

  
  

• Enrichment Kit and Smart Filtering: The enrichment kit defines the
  target capture regions for the sample. You can choose from pre-defined
  enrichment kits or add custom enrichment kits in the Settings dialog.
  Smart filtering allows for intelligent filtering of variants based on
  specific criteria.

  
  

• Taken Date: Enter the date when the sample was taken or obtained.

  
  

• Sequence Date: Specify the date when the sample was sequenced.

  
  

• Received Date: Enter the date when the sample was received for
  processing.

  
  

Sequencing Machine: Indicate the sequencing machine that was used for the sample. If no Sequencing machine is selected the default of Illumina will be assumed. To specify for MGI, please use the dropdown and select the relevant machine.

• Sample Source: Select the method by which the DNA sample was
  collected. Options include Germline, Tumor Biopsy, Blood, Buccal,
  Mitochondria, Saliva, Fetal, Bone Marrow, Other, and Parents. * If
  you plan to run a somatic pipeline, you will need to select Tumor
  Biopsy.

  
  

• Notes: Provide any additional notes or relevant information about the
  sample.

  
  

• Exclude Sample from local allele frequency: Select this option if you
  want to exclude the sample from being considered in the internal local
  allele frequency calculations.

  
  

Sequencing Sample Details

These fields collectively provide crucial details about the sequencing sample, ensuring accurate processing and analysis.

3.7.6 Selecting File Source (fastq)

In the File Source section, you have the flexibility to upload local files or fetch them remotely. If your fastq files are stored on a cloud infrastructure, you can configure the connection by accessing the Data Sources feature within the Settings Dialog.

When you choose the “Fetch Remote” option, you will be prompted to select the specific Data Source to be utilized. Various Data sources are supported, including FTP, sFTP, Amazon S3, BaseSpace, One Drive, and Google Cloud Storage. This allows you to seamlessly retrieve the fastq files from your preferred remote location.

Selecting File Source

By offering both local file upload and remote file fetching capabilities, the system caters to different scenarios and enables convenient access to your fastq files, regardless of their location.

3.7.7 Selecting Seq Sample Files (fastq)

In the next dialog, you will have the option to browse to the local directory containing the samples of interest, if applicable. It is necessary to upload the samples before proceeding by selecting the appropriate files. It is important to note that a stable and reliable internet connection is crucial for successful data transfer. Insufficient connectivity may lead to data corruption and result in the failure of the secondary pipeline.

To ensure optimal results and minimize potential issues, it is recommended to establish a direct connection between the cloud infrastructure hosting the data files and Geneyx. This integration eliminates the need to upload the files into Geneyx separately. By directly accessing the cloud infrastructure, you can streamline the data transfer process and enhance the efficiency of the secondary pipeline.

Selecting Seq Sample Files

3.7.8 Selecting Seq Sample Pipeline (fastq)

In the final step, you will need to define the sequencing pipeline to be initiated. This selection is based on the reference genome assembly and can be categorized into the following options:

• DRAGEN Exome – hg19: This pipeline is specifically designed for exome
  sequencing data and utilizes the DRAGEN software with the hg19
  reference genome assembly.

  
  

• DRAGEN Exome – hg38: Similar to the previous option, this pipeline is
  optimized for exome sequencing data but utilizes the hg38 reference
  genome assembly.

  
  

• DRAGEN Whole Genome – hg19: This pipeline is tailored for whole genome
  sequencing data and employs the DRAGEN software along with the hg19
  reference genome assembly.

  
  

DRAGEN Whole Genome – hg38: Similar to the previous option, this pipeline is specifically designed for whole genome sequencing data but utilizes the hg38 reference genome assembly.Users also can select what version of DRAGEN is being used for the secondary pipeline. The baseline DRAGEN version will remain v4.0.5 but will be deprecated with newer releases. This option allows users to conform to their existing workflows whilst enabling exploration of updated DRAGEN features. The optional DRAGEN version (4.2.4) enables several additional features, discussed below, including advanced callers and low pass whole genome options. For upgrading to the newest Dragen caller, please reach out to our support (support@geneyx.com).

DRAGEN v4.2.4: The comprehensive updates included in DRAGEN v4.2.4 can be found here, https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200040845_02_DRAGEN-4.2-Customer-Release-Notes.pdf. DRAGEN v4.4.4: Updates available in DRAGEN v4.4.4 can be found here, https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200068065_00_DRAGEN-4_4_4-Customer-Release-Notes.pdf

Advanced DRAGEN Callers: When running whole genome workflows from the secondary pipeline, there are new sequence-graph realignment settings that run on the backend to improve calling for genes that have high identity paralogs. This includes genes such as: GBA, SMN1, HBA, LPA, RH, CYP2D6, CYP21A2, CYP2B6. For analyses that have whole genome workflows with at least 30X coverage, output metrics for these genes will be present in the Advanced Analysis link in the “Info” section of the analysis. All outputs are taken from Illumina and each output will reference the associated hyperlink.

If advanced caller processing is performed outside of Geneyx, the system supports the direct integration of JSON files generated by these external specialty callers, including DRAGEN TruSight Oncology 500 for display for tumour mutation burden (TMB), microsatellite instability (MSI) and genomic instability (GIS) For further details see https://github.com/geneyx/geneyx.analysis.api/tree/main/scripts/DragenTruSightOncology500

DRAGEN Low pass whole genome sequencing: Low-pass whole genome sequencing (low-pass WGS) is a genomic sequencing approach where the entire genome is sequenced at a relatively low depth, typically less than 5x coverage. Low pass whole genome sequencing is now supported in the secondary pipeline of Geneyx. To implement, the sequencing target will need to set to Whole Genome Low Pass.

Selecting primary pipeline to be initiated

By selecting the appropriate pipeline based on your sequencing data and reference genome assembly, you can ensure accurate and efficient variant calling and analysis.

Sentieon is also available as a secondary pipeline option within Geneyx Analysis. Sentieon’s exceptional software has earned widespread recognition in the genomics community for its exceptional accuracy, speed, and scalability. Trusted by researchers in diverse fields such as cancer genomics, rare disease studies, population genetics, and precision medicine, Sentieon is now seamlessly integrated as part of your secondary pipeline options for SNVs. • Sentieon version 202503.01: For further information please see https://support.sentieon.com/docs/appendix/releasenotes/

Once you click Next, you will have completed the secondary pipeline process.

3.7.9 Sequencing Samples Details

The Sequencing Samples details provide a comprehensive overview of the options selected for the secondary pipeline, as well as the current progress and results. The following sections offer valuable information:

• Seq. Sample: Data Manager and Admins can choose to execute secondary
  pipeline(s) using the

icon.

• Processing Tasks: This section displays the current status of the
  secondary pipeline. If any changes have been made to the pipeline or
  if an error was encountered, the user has the option to reset the
  process or delete it using the X icon. Upon successful completion, a
  hyperlink to the VCF Sample will be generated, allowing easy access to
  the VCF Details page.

  
  

• QC Data: This section showcases the quality control metrics generated
  from the secondary pipeline. These metrics provide insights into the
  reliability and accuracy of the sequencing data, ensuring high-quality
  results.

  
  

• Files: In this section, the fastq files that were utilized during the
  secondary pipeline are listed. This information helps track the source
  and origin of the data, facilitating traceability and reproducibility.

  
  

• VCF Samples: Here, the VCF files that were generated from the pipeline
  are displayed. Clicking on the hyperlink associated with each VCF
  Sample will direct you to the VCF Details page. This page offers
  comprehensive information about the variants detected in the sample
  and allows for further analysis and exploration.

  
  

By providing these detailed sections, the Sequencing Samples interface enables users to track the progress of the secondary pipeline, assess the quality of the data, and access the resulting VCF files for further analysis and interpretation.

Sequencing Samples Details

Once the secondary pipeline is completed, clicking on the VCF hyperlink will take you to the VCF Details page, where you can explore the generated output. The output files included in this page typically consist of a BAM file, a BAM.bai index file, SNV VCF file, and CNV/SV VCF file.

*The analysis of repeat sequences is not optimal for PCR-based next-generation sequencing (NGS) due to the high degree of homology between repeat regions. PCR amplification of repeat regions can result in non-specific amplification, which can lead to errors in sequence assembly and analysis. Furthermore, the presence of repeat regions can result in difficulties in mapping reads back to a reference genome, making accurate alignment and variant calling challenging. Therefore, it is recommended to exercise caution when performing the analysis of repeat sequences or to use specialized protocols and tools that can account for the challenges posed by these regions in PCR-based NGS.

The VCF files are automatically associated with the enrichment kit that was specified during the import process. As a result, the metrics obtained from the enrichment kit will be displayed in the Annotation History section, providing valuable information about the variants detected and annotated in the sample.

To further analyze the VCF file, you have two options:

• Dashboard: You can apply the VCF file to an Analysis by either
  clicking on “New Analysis” in the Dashboard. This allows you to
  perform in-depth analysis and interpretation of the variants within
  the VCF file.

  
  

• VCF Details Page: Alternatively, you can directly access the analysis
  options by clicking on the “New Analysis” icon next to the sample name
  within the VCF Details page. This streamlined approach enables you to
  initiate an analysis specific to the selected VCF sample, ensuring
  efficient exploration and processing of the genomic data.

  
  

By providing these options, the platform empowers users to leverage the generated VCF files for downstream analysis, allowing for detailed investigations into the genetic variants present in the sample.

VCF Sample Details

In the QC Data section, there is an option to output Custom Coverage and QC Metrics. QC Metrics will provide a downloadable file. Information for these metrics can be obtained with the following links:

The mapping_metrics information can be found here: https://support.illumina.com/help/DRAGEN_Germline_Pipeline_v337_OLH_1000000083701/content/source/informatics/apps/mapalignmetrics_swbs_appdraggp.htm

The vc_metrics information can be found here: https://support.illumina.com/help/DRAGEN_Germline_Pipeline_v337_OLH_1000000083701/content/source/informatics/dragen/softwarevarcal_appdrag.htm

When a user selects Custom Coverage, it will provide the option for Gene List or Gene Panel coverage. Gene List allows the user to enter a list of genes, whereas the Gene Panel requires a panel to be integrated into the account. The outputs will provide coverage for the genes at 10 and 20X. More information related to this can be found here: https://github.com/geneyx/geneyx.analysis.api/wiki/Get-Coverage-For-Gene-Panel.

3.8 Batches (fastq)

Geneyx Analysis offers support for batch upload of fastq files using a TSV (tab-separated values) file. This convenient feature allows users to describe a set of fastq files available in external storage, such as FTP, sFTP, Basespace, S3, Google Cloud, or OneDrive. To initiate the batch upload process, it is necessary to configure a Data Source in the Settings dialog, enabling seamless integration with external storage systems. Once a Data Source has been configured, you are now ready to initiate a batch workflow.

By clicking on the “Batches” section, users can access a comprehensive overview of all batch workflows that have been initiated in their Geneyx Analysis account. The displayed fields provide essential information about each batch workflow, including:

• Name: The descriptive name assigned to the batch workflow.

  
  

• File Name: The name of the uploaded text file that contains the batch
  information.

  
  

• Status: The current status of the batch workflow, indicating whether
  it is running, completed, or encountered any errors.

  
  

• Created By: The user who initiated the batch workflow.

  
  

• Created: The date and time when the batch workflow was initiated.

  
  

• Modified By: The user who last made modifications to the batch
  workflow.

  
  

• Modified: The date and time of the last modification made to the batch
  workflow.

  
  

Additionally, users are granted the flexibility to manage their batch workflows. They have the option to delete a batch workflow that is no longer needed or edit the details of an existing workflow, ensuring efficient workflow management and customization.

Batch details overview

With the batch upload functionality and comprehensive batch workflow management options, Geneyx Analysis facilitates the streamlined processing of large-scale sequencing data, empowering users to efficiently analyze and interpret genomic information at scale.

3.8.1 Batch Upload (fastq)

Clicking on the Batch Upload icon will navigate to a window where you can download a TSV template. The template needs to be modified with the updated sample information. The fields include:

• # p_sn – Patient serial number / id [Required]. This is the
  prefix of the sample present in the Data Source that hosts the fastq
  file. For example, if you have two fastq files named
  MQ21111B_005.R1.fq.gz and MQ21111B_005.R2.fq.gz, the serial number
  would be “MQ21111B”.

• # p_name – Patient name [Optional]

  
  

• # p_gender – Patient gender (M, F) [Optional]. This is important to
  specify if you are interested in calling events in non-autosomal
  chromosomes.

  
  

• # p_dob – Patient date of birth (yyyy/mm/dd) [Optional]

  
  

• # p_consanguinity – Patient consanguinity (Consang, NonConsang)
  [Optional]

  
  

• # p_populationType – Patient population Type [Optional]

  
  

• # p_paternalAncestry – Patient parental ancestry [Optional]

  
  

• # p_maternalAncestry – Patient maternal ancestry [Optional]

  
  

• # p_familyHistory – Patient family history [Optional]

  
  

• # p_hasBioSample – Patient indication to have a bio sample available
  (true, false, 0, 1) [Optional]

  
  

• # p_paternalGrandfather – Patient paternal grandfather [Optional]

  
  

• # p_paternalGrandmother – Patient paternal grandmother [Optional]

  
  

• # p_maternalGrandfather – Patient maternal grandfather [Optional]

  
  

• # p_maternalGrandmother – Patient maternal grandmother [Optional]

  
  

• # p_consentPersonal – Patient consent for personal data (true, false,
  0, 1) [Optional]

  
  

• # p_consentClinical – Patient Consent for clinical data (true, false,
  0, 1) [Optional]

  
  

• # p_generallyHealthy – Patient indication to be generally healthy
  [Optional]

  
  

• # c_date – Clinical Record date (yyyy/mm/dd) [Optional]

  
  

• # c_description – Clinical Record description [Optional]

  
  

• # c_phenotypes – Clinical Record list of HPO phenotype codes or names
  colon separated [Optional]

  
  

• # ss_sn – Sequenced sample serial number/id [Required]. This should
  be the same as the Patient Sample Serial number.

  
  

# ss_seqMachine – Sequenced sample – sequencing machine name [Optional] If no sequencing machine is specified in the BatchImportTemplate, the default of Illumina will be assumed. To specify for MGI, please specify the relevant machine in the # ss_seqMachine field.

• # ss_kit – Sequenced sample – enrichment kit [Optional]. List of
  enrichment kits are available in the Enrichment Kit and Smart
  filtering dialog in Settings.

  
  

• # ss_takenDate – Sequenced sample – taken date (yyyy/mm/dd)
  [Optional]

  
  

• # ss_seqDate – Sequenced sample – sequencing date (yyyy/mm/dd)
  [Optional]

  
  

• # ss_receiveDate – Sequenced sample – Received date (yyyy/mm/dd)
  [Optional]

  
  

• # ss_type – Sequenced sample – sample type (RnaSeq, DnaSeq)
  [Optional]

  
  

• # ss_target – Sequenced sample – sequencing target (WholeGenome,
  Exome, GenePanel, TargetRegion, ClinicalExome) [Required]

  
  

• # ss_source – Sequenced sample – sample source (GermLine, Blood,
  Tumor, Buccal, Mitochondria, Fetal, BoneMarrow, Other) [Optional].
  *Selecting Tumor will apply the somatic workflow to the sample.

  
  

• # ss_relation – Sequenced sample – relation (Mother, Father, Sibling,
  Twin, MotherRelative, FatherRelative, Other) [Optional]

  
  

• # ss_consent – Sequenced sample – use consent [Optional]

  
  

• # ss_collectionId – Sequenced sample – collection id [Optional]

  
  

• # ss_dataSource – Sequenced sample – data source from account
  [Required]. This is the Name of the Data Source.

  
  

• # ss_fastqCount – Sequenced sample – expected fastq files count
  [Optional]

  
  

• # ss_notes – Sequenced sample – notes [Optional]

  
  

• # ss_excludeLAF – Flags whether this sample should be excluded from
  the in-house allele frequencies [Optional]

  
  

• # pipeline – The pipeline name to run – otherwise use the seq target
  default pipeline defined in account (skip or – to skip pipeline.
  overwrite or o to overwrite existing task). [Optional]

  
  

• # build – the pipeline genome build to run – valid values are (hg19,
  hg38) [Required]

  
  

• # protocols – The protocols to run (defined by the serial-numbers and
  comma separated) [Optional]. This will apply the output files
  directly into analyses and greatly improve sample automation
  workflows.

  
  

• # genePanel – The gene panel by name to add for each created analysis
  [Optional]

  
  

To streamline the batch upload process, each sample in the batch workflow should be displayed on a per-row basis, presenting the updated information for the required fields. This allows users to conveniently review and modify the details of each sample as needed. Once the necessary modifications are made, the next step is to select the “Browse” button, which enables users to navigate to the updated TSV (tab-separated values) file that contains the batch information.

Upon selecting the TSV file, users can proceed by clicking the “Process Batch” button. This action initiates the secondary pipeline for all the samples defined within the batch workflow. The secondary pipeline performs the necessary steps, such as variant calling and VCF file generation, for each sample in the batch. By initiating the process, users can efficiently process a large number of samples simultaneously, saving time and effort.

Batch Upload Dialog

The Batch functionality in Geneyx Analysis empowers users to effectively manage and execute batch workflows, automating the analysis of multiple samples and enabling efficient processing of large-scale sequencing data

3.9 Variant Browser

The Variant Browser in Geneyx Analysis provides a powerful platform for querying and exploring SNVs (Single Nucleotide Variants) and CNV/SVs (Copy Number Variants/Structural Variants) across all samples within an account. This feature allows users to apply unique filtering logic and perform comprehensive analyses on a large-scale dataset.

With the Variant Browser, users can flexibly define filters based on various criteria such as genomic coordinates, variant type, allele frequency, functional impact, and more. This enables targeted investigations and in-depth exploration of specific variants or genomic regions of interest.

The Variant Browser is designed to handle large-scale datasets, allowing users to query and analyze up to 200,000 samples at a time. This capacity ensures that users can efficiently navigate and explore extensive genomic data, gaining valuable insights into the genetic variations present in their samples.

By leveraging the capabilities of the Variant Browser, researchers and clinicians can conduct sophisticated variant analysis, identify potential disease-causing variants, and unravel the genetic basis of complex disorders.

Variant Browser

To run the Variant Browser workflow in Geneyx Analysis, follow these steps:

• Click on the “New” button located in the upper right corner of the
  Variant Browser interface. This will open the Query Builder dialog.

  
  

• In the Query Builder dialog, you can specify the genome build for your
  analysis. Choose the appropriate genome build from the available
  options.

  
  

• Configure the specific filters based on your analysis requirements.
  You can define filters based on various criteria such as genomic
  coordinates, variant type, allele frequency, functional impact, and
  more. This allows you to narrow down the variants of interest.

  
  

• Once you have configured the filter logic, click “OK” to save the
  filter settings.

  
  

• After setting up the filters, click on the “Run” button located in the
  upper right corner of the Variant Browser interface. This will
  initiate the query across all internal samples in your account.

  
  

• The results of the query will be displayed in a table format, showing
  the variants that match your specified filters. You can explore the
  results and perform further analysis.

  
  

• If desired, you can export the query results, edit them, or save them
  for downstream applications. This allows you to integrate the results
  into your research or clinical workflows.

  
  

By following these steps, you can effectively utilize the Variant Browser workflow to query and analyze variants across all internal samples, enabling you to gain valuable insights into the genomic variations within your dataset.

Query Builder Dialog

3.10 Settings

The Settings dialog in Geneyx Analysis provides users with the ability to customize and configure various aspects of their account. It allows for both general user-initiated modifications and certain administrative capabilities.

By accessing the Settings dialog, users can take advantage of these customizable features and tailor their Geneyx Analysis account to suit their specific requirements, allowing for a more personalized and efficient genomic analysis experience.

Settings Dialog

3.10.1 Enrichment Kits & Smart Filtering

Enrichment kits play a crucial role in the pre-sequencing DNA preparation process by selectively amplifying or capturing specific regions of interest from the genome. Instead of sequencing the entire genome, only the targeted regions are enriched and sequenced, resulting in a more cost-effective and focused analysis.

In Geneyx Analysis, default enrichment kits are provided, but users also have the flexibility to edit and customize them according to their specific research or clinical requirements. The edit icon allows users to modify the target capture regions, add or remove specific genomic regions, or create entirely custom enrichment kits. This customization ensures that the sequencing and analysis focus on the specific regions of interest relevant to the study or investigation.

Smart filtering is another powerful feature in Geneyx Analysis that empowers users to define frequency and CADD score thresholds for filtering out irrelevant variants before the VCF annotation step. Frequency thresholds enable users to filter variants based on their frequency in specific populations or databases, helping to prioritize rare or low-frequency variants that might be more relevant to the analysis. CADD score thresholds allow users to filter variants based on their predicted pathogenicity, focusing on variants with higher scores that are more likely to have functional significance. In addition to existing features, you can also apply SMART filtering based on Splice-AI and Read Depth.

By leveraging smart filtering, users can efficiently narrow down the variants of interest, reducing the analysis workload and improving the accuracy of downstream interpretation. This feature helps researchers and clinicians focus on the most relevant variants and prioritize their investigation efforts, saving time and resources in the analysis process.

Enrichment Kits

Geneyx provides a Default-Exons enrichment kit, which will be applied by default unless a protocol is configured with a different enrichment kit. Users can specify which enrichment kit they wish to use as default in this dialog.

* Default – Exons only’ kit – selecting this enrichment kit applies a .bed file curated from UCSC and ClinVar. This bed file is updated with each release of Geneyx Analysis. Please contact support@geneyx.com for the most current versions.

When selecting “New” in the upper right corner of the Enrichment Kit & Smart Filtering interface, users can add new enrichment kits to the list of selectable options. The following details need to be entered for creating a new enrichment kit:

• NAME: Provide a name for the enrichment kit.

  
  

• DESCRIPTION: Enter a description or additional details about the
  enrichment kit.

  
  

• BED FILE PADDING: Specify the number of bases to be added as padding
  to each region in the sequencing BED file. This padding helps ensure
  complete coverage of the target regions during sequencing.

  
  

• DEFAULT: Enrichment kit assigned as ‘Default’ in the settings, will be
  applied when no ‘Default Enrichment kit and Smart filtering’ is
  applied to a protocol. Users can specify which enrichment kit they
  wish to use as default.

  
  

Under the Smart Filtering section, users can configure additional pre-filtering options for efficient variant annotation. These options include:

• Exons +/-50bp BED file: This option captures all exonic regions with
  an additional 50 base pairs of padding on each side of the exon.

  • This uses the ‘Default-Exons only’ .bed

• Filtering :Users can define which variants will be captured when the
  enrichment kit is applied. This includes options like including
  homozygous reference variants (ref/ref) and capturing all modifiers
  according to the specified rules.

  
  

• Merge BED with ClinVAR BED: This option combines the existing BED file
  with the ClinVar BED file, capturing variants that are present in
  both.

  
  

• Always include ClinVar P/LP variants: Include ClinVar
  Pathogenic/Likely Pathogenic variants regardless of CADD, splice-AI,
  gnomAD, and ColorsDB defined thresholds

  
  

• VCF FILTER FIELD VALUES: If the VCF has a FILTER value, such as PASS
  or ., they can be specified to automate the annotation of the VCF
  file.

  
  

• DP (>= OR N/A): Enables prefiltering based on read depth.

  
  

• GQ (>= OR N/A): Enables prefiltering based on genotype quality.

  
  

• CADD SCORE (≥ OR N/A): Pre-filter variants based on the specified CADD
  score threshold. Variants with a CADD score equal to or higher than
  the threshold will be included.

  
  

• SPLICE AI: (>= OR N/A): Enables prefiltering based on SpliceAI.

  
  

• HOM ALLELE COUNT (≤ OR N/A): Pre-filter variants based on the
  homozygous allele count from gnomAD exomes/genomes data. Variants with
  a homozygous allele count less than or equal to the specified
  threshold will be included.

  
  

• HET ALLELE COUNT (≤ OR N/A): Pre-filter variants based on the
  heterozygous allele count from gnomAD exomes/genomes data. Variants
  with a heterozygous allele count less than or equal to the specified
  threshold will be included.

  
  

• HEMI ALLELE COUNT (≤ OR N/A): Pre-filter variants based on the
  hemizygous allele count from gnomAD exomes/genomes data. Variants with
  a hemizygous allele count less than or equal to the specified
  threshold will be included.

  
  

• CoLoRSdb AF (% <= or N/A): Pre-filter variants according to CoLoRSdb
  AF (%)

  
  

Once the user clicks Save, it will navigate to the Enrichment kit details, where you can add sequencing and genotyping BED files. Once you click Add, you will need to input the following:

• TYPE: This includes Sequencing and Genotype. Genotype is used for a
  BED file that contains individual genetic variant locations.
  Sequencing is used for BED files that encompass regions of DNA to
  capture.

• GENOME BUILD: hg19/GRCh37 or hg38

  
  

• BED FILE: This includes ability to Select Existing, Select Shared, and
  Upload a new file. To upload a new file, select the Browse icon to
  locate the BED file. Once selected, click Save, and it will convert
  the BED file into a new enrichment kit that can be used. Note:
  Mitochondrial coordinates should be entered as chrMT 1 16569
  (NC_012920) whether hg19 or GRCh38 – this is because chrM 1 16571
  (NC_001807) is obsolete.

  
  

By customizing these options, users can tailor the enrichment kit and smart filtering settings to their specific analysis requirements, ensuring that only relevant variants are captured and processed during the analysis pipeline.

Enrichment Kit Details

3.10.2 Gene Panels

To incorporate a specific subset of genes into the analysis workflow, the Gene Panels feature can be utilized. To create a new gene list, simply click on the “New” icon located in the top right corner of the interface. If you wish to modify an existing gene list, select the “Edit” icon next to the respective panel. Modification of gene panels is restricted to users with Admin permissions.

When creating or editing a gene list, it is important to enter the gene names in accordance with the standards set by the HUGO Gene Nomenclature Committee (HGNC). This ensures accurate recognition and processing of the gene names. In case a gene is not recognized correctly, a notification will be displayed indicating the incorrect gene entry.The number of genes in each panel is displayed to allow users easier review of content.

Adjacent to the “New” icon, you will find the “PanelApp” icon, which provides access to PanelApp. PanelApp offers recommended gene panels that can be explored and used as references for creating custom gene lists.

By leveraging the Gene Panels feature, users can conveniently integrate specific genes of interest into their analysis, allowing for more targeted and focused investigations within the selected gene subset.

Gene Panels

Gene panels can be incorporated into the analysis workflow either at the protocol level or during the analysis stage. When a gene panel is added at the protocol level, it is automatically applied to capture variants exclusively within the specified genes.

By including a gene panel at the protocol level, the analysis pipeline is tailored to focus solely on the genes defined in the panel. This ensures that the subsequent variant calling and annotation processes exclusively consider variants within the designated gene set.

Adding gene panels at the protocol level offers the advantage of streamlining the analysis workflow by predefining the targeted genes. This approach allows for more efficient and accurate identification of relevant variants, facilitating in-depth investigations within the specific gene subset.

Gene Panel option at the protocol level

Gene panels can be disabled from use (for example if a newer version of a panel is required), by selecting the Disable icon in the edit panel.

Disable gene panel

3.10.3 Variant Maps

Variant maps are utilized to define target capture regions within an analysis, either through a BED file or specific genomic positions. They enable the inclusion of specific variants of interest during the analysis process. To create a new variant map, click on the New icon, which will open a dialog where the following information needs to be entered:

• NAME: Provide a name for the variant map.

  
  

• DESCRIPTION: Add a description that provides details about the variant
  map.

  
  

• LOCATIONS: Enter the locations in a BED file format, or you can
  directly paste the contents from a BED file. This specifies the
  genomic regions to be targeted.

  
  

• VARIANTS: Enter the variants in a tab-delimited format, with each line
  reserved for one variant. This allows for the inclusion of specific
  variant positions of interest.

  
  

• RS IDS: Users can enter a list of rsIDs that they wish to capture in a
  comma separated format.

  
  

Creating a Variant Map

By defining variant maps, you can customize the analysis to focus on specific genomic regions or variants, enabling more precise investigations and interpretations within those designated regions.

3.10.4 Allele Frequency Backlog

The allele frequency backlog feature enables users to incorporate a database of variant- and associated allele frequencies into their internal catalog. By utilizing this feature, users can enhance the representation of variants specific to different populations within their Geneyx account.

The allele frequency backlog feature helps to improve the accuracy and relevance of variant analysis by considering population-specific allele frequencies. By incorporating this information into the internal catalog, users can obtain more reliable and context-specific interpretations of variants within their samples.

This feature contributes to a more comprehensive understanding of genetic variation, particularly with regards to population-specific variations and their impact on variant interpretation and disease association.

Allele Frequency Backlog

When entering data into the Allele Frequency backlog, you can follow a specific format to ensure accurate and structured information. The format for entering data are provided as templates, and as follows:

• Chromosome: Specify the chromosome number or identifier where the
  variant is located.

  
  

• Position: Provide the genomic position of the variant on the specified
  chromosome.

  
  

• Reference Allele: Enter the reference allele for the variant at the
  given position.

  
  

• Alternate Allele: Specify the alternate allele(s) for the variant at
  the given position.

  
  

• Population: Indicate the population or ethnic group associated with
  the allele frequency data.

  
  

• Allele Frequency: Enter the frequency of the alternate allele in the
  specified population. This should be provided as a decimal value
  ranging from 0 to 1.

  
  

Each line represents a single variant with its corresponding allele frequency information. You can add multiple variants by entering them on separate lines, following the same format.

By accurately entering the data in this format, you can effectively incorporate population-specific allele frequencies into your internal catalog, enriching the analysis and interpretation of variants within different populations.

Entering data into the Allele Frequency Backlog

3.10.5 Sequencing Machines

The Sequencing Machine console refers to the instrument used during the primary analysis of the sequencing data. If the sequencing machine used for your samples is not available by default in the system, you have the option to create a new entry.

To create a new sequencing machine entry, click on the New icon next to the Sequencing Machine field. This will open a dialog where you can provide the necessary information for the new sequencing machine:

• Sequencing company: Select sequencing company from the drop down

  • If no option is selected the default of Illumina will be used

    
    

  • To enable batch upload of MGI FASTQ the ‘MGI’ option must be
    selected.

    
    

• Model: Enter the name or identifier of the sequencing machine.

  
  

• Description (optional): Provide a description or additional details
  about the sequencing machine.

  
  

By entering the relevant details, you can add a new sequencing machine entry to accurately reflect the instrument used for your sequencing samples. This ensures that the information is properly recorded and can be referenced in the future for analysis and interpretation purposes.

Sequencing Machines

3.10.6 Data Sources

Data Sources in the context of variant calling refers to the storage locations where the fastq files required for the secondary pipeline can be accessed. These storage devices include:

• Amazon S3: Amazon Simple Storage Service is a web service offered by
  Amazon Web Services (AWS) for storing and retrieving data. It provides
  scalable, reliable, and secure object storage in the cloud.

  
  

• FTP: File Transfer Protocol is a standard network protocol used for
  transferring files between a client and a server on a computer
  network. It allows users to upload and download files from an FTP
  server.

  
  

• Secure File Transfer Protocol (SFTP): SFTP is a secure version of FTP
  that uses encryption to protect the confidentiality and integrity of
  data during file transfer. It provides a more secure alternative to
  FTP.

  
  

• BaseSpace: BaseSpace is a web-based platform provided by Illumina, a
  leading genomics company. It allows biologists and informaticians to
  store, analyze, and share genetic data generated from Illumina
  sequencing systems.
  Please note when using Basespace, the Illumina SampleSheet used to generate FASTQ should adhere to the following: either Sample_ID must be the same as Sample_Name, or only Sample_ID should be used. If Sample_ID does not = Sample_Name, files will not be detected.

  
  

• OneDrive: OneDrive is a file hosting and synchronization service
  provided by Microsoft. It allows users to store files and access them
  from various devices. It also integrates with Microsoft Office
  applications.

  
  

• Google Cloud Storage: Google Cloud Storage is a cloud-based object
  storage service provided by Google Cloud Platform. It offers scalable
  and durable storage for a wide range of data types and is accessible
  via RESTful APIs.

  
  

These data sources provide options for accessing and retrieving the fastq files required for variant calling and subsequent analysis. By configuring the appropriate data source in the Settings dialog, users can seamlessly integrate their data from these storage devices into the secondary pipeline.

To create a new Data Source for seamless communication with Geneyx Analysis, follow these steps:

• Go to the navigation pane on the left side of the Geneyx Analysis
  platform.

  
  

• Select “Settings” from the navigation options. This will open the
  Settings menu.

  
  

• Within the Settings menu, locate and select “Data Sources.” This will
  take you to the Data Sources configuration page.

  
  

• On the Data Sources page, you can view and manage existing Data
  Sources. To create a new Data Source, click on the “New” button or the
  “+ Add Data Source” option.

  
  

• A dialog or form will appear, allowing you to configure the details of
  the new Data Source.

  
  

• Enter the required information for the new Data Source, including the
  cloud storage provider (e.g., Amazon S3, Google Cloud Storage),
  authentication credentials, and any additional settings specific to
  the chosen provider.

  
  

• If you’re setting up a cloud-based Data Source, you may need to
  provide access keys or authentication tokens to establish the
  connection between Geneyx Analysis and the cloud storage service.

  
  

• Once you have entered all the necessary information, click “Save” or
  “Create” to create the new Data Source.

  
  

• The newly created Data Source will now be available for selection when
  configuring file sources or workflows within Geneyx Analysis.

  
  

*Please specify the specific directory in which the fastq files are stored.

Data Source dialog

By setting up a Data Source with the cloud storage provider, you can establish a seamless communication channel between Geneyx Analysis and the designated cloud infrastructure where your fastq files are stored. This is particularly advantageous for handling large fastq files efficiently. Additionally, you also have the option to upload fastq files locally if needed.

3.10.7 Case Sub Statuses

Case Sub Statuses are a way to further categorize and track the progress or status of individual cases within a laboratory or workflow. They allow for customization based on the specific requirements and needs of the lab.

To configure Case Sub Statuses in Geneyx Analysis, follow these steps:

• Access the Geneyx Analysis platform and navigate to the Settings
  section.

  
  

• Within the Settings menu, locate and select “Case Sub Statuses” or a
  similar option related to case statuses or workflows.

  
  

• On the Case Sub Statuses configuration page, you will see a list of
  existing sub statuses, if any.

  
  

• To create a new Case Sub Status, click on the “New” button or an
  equivalent option to add a new sub status.

  
  

• Provide a descriptive name for the new sub status. This should reflect
  the specific stage, progress, or status that you want to track for
  cases.

  
  

• Save or apply the changes to create the new Case Sub Status.

  
  

• Repeat the process if you need to create multiple sub statuses to
  cover different stages or statuses in your workflow.

  
  

Case Sub States can be applied in the Analysis Details window or in an analysis by clicking “Open” and changing to the specific status of the case.

Changing the status of a case

When a case status has been changed to “Closed”, the analysis will convert into a read-only mode.

In this mode, variants can no longer be selected or deselected, ensuring results remain frozen and protected from accidental modification. While reports can no longer be generated, all other analysis functionalities remain accessible for review. Only users with administrator permissions can unlock the analysis for further editing.

By customizing Case Sub Statuses, you can create a more granular and tailored system for tracking and managing cases within your lab. This allows you to categorize cases based on specific milestones, progress, or custom stages that align with your workflow and requirements.

3.10.8 ACMG Settings

The ACMG Settings in Geneyx Analysis allows users to customize specific thresholds that are used in the ACMG (American College of Medical Genetics and Genomics) criteria for variant analysis. These thresholds help determine the pathogenicity or benign status of variants based on various prediction algorithms and scores. Here are the thresholds that can be modified:

• REVEL Pathogenic Cutoff: This threshold determines the cutoff score
  for the REVEL (Rare Exome Variant Ensemble Learner) algorithm.
  Variants with a score greater than the specified cutoff are considered
  evidence of pathogenicity.

  
  

• REVEL Benign Cutoff: This threshold sets the suggested cutoff for the
  REVEL algorithm to identify benign variants. Variants with scores
  below this cutoff are more likely to be classified as benign.

  
  

• SIFT Cutoff: The SIFT algorithm predicts the deleteriousness of amino
  acid substitutions. The SIFT cutoff defines the threshold for
  normalized probabilities. Positions with probabilities below the
  cutoff are predicted to be deleterious, while those equal to or above
  the cutoff are predicted to be tolerated.

  
  

• PhyloP Cutoff: PhyloP scores indicate whether sites are under
  purifying selection. A PhyloP score cutoff of less than 0 is typically
  used to filter out sites under selection, effectively removing them
  from the analysis.

  
  

• GERP Cutoff: GERP scores also indicate the impact of mutations
  affected by purifying selection. A GERP score cutoff of less than 2 is
  generally used to filter out sites under selection.

  
  

• dbNSFP dbscSNV Cutoff: dbNSFP dbscSNV provides a dichotomous effect
  score for variants. The specified threshold value determines the
  cutoff for considering variants as having dichotomous effects.

  
  

• MetaSVM Cutoff: MetaSVM is a variant prediction tool. The MetaSVM
  cutoff defines the threshold value used to classify variants based on
  their predicted pathogenicity.

  
  

ACMG Settings

By modifying these thresholds in the ACMG Settings, users can fine-tune the variant analysis criteria to align with their specific requirements and interpretations of variant pathogenicity.

3.10.9 Protocols

The Protocols dialog in Geneyx Analysis provides an overview of workflows that can be utilized for variant analysis. A protocol defines the filtering logic and genetic models used to identify clinically relevant variants in a streamlined manner. It serves as a standardized template for workflows, incorporating specific criteria such as genetic models, report structure, allele frequency thresholds, and associated phenotypes and diseases. Here are the details included in the Protocols dialog:

• NAME: This field displays the name of the protocol, which helps
  identify and differentiate between different protocols.

  
  

• TYPE: The protocol is categorized into specific types, allowing users
  to group and organize them based on their purpose or application.

  
  

• DESCRIPTION: This section provides additional details and information
  about the protocol, helping users understand its purpose, objectives,
  or any specific considerations.

  
  

• PHENOTYPES: The protocol may be associated with specific phenotypes,
  indicating the clinical characteristics or traits that are relevant
  for the variant analysis performed using this protocol.

  
  

• DISEASE: This field lists the associated diseases or conditions that
  are the focus of the variant analysis conducted using the protocol.

  
  

• TEMPLATE: The protocol may be based on a specific template, which
  serves as the foundation for the analysis workflow. The template
  provides a standardized structure and format for reporting results and
  interpreting variants.

  
  

• CREATED BY: This field identifies the user who initially created the
  protocol, providing information about its origin.

  
  

• CREATED: The date or timestamp when the protocol was originally
  created is displayed, enabling users to track the timeline of protocol
  development.

  
  

• MODIFIED BY: If modifications have been made to the protocol, this
  field indicates the user responsible for those modifications.

  
  

• MODIFIED: The date or timestamp of the most recent modifications made
  to the protocol is listed, allowing users to track any updates or
  changes to the protocol.

  
  

Protocol Dialog

By utilizing protocols, users can streamline their variant analysis workflows, ensure consistency, and apply standardized criteria and interpretations to identify clinically relevant variants.

To create a new protocol in Geneyx Analysis, follow these steps:

1. Click on the New icon in the top right corner of the protocol interface.

2. Fill in the following fields to define the protocol:

• NAME: Provide a name for the protocol.

  
  

• TYPE: Select the focus or category of the protocol from options like
  Misc, Somatic, Germline, Health Screening, Panel Design, etc.

  
  

• ORDINAL: Specify the position of the protocol in a series.

  
  

• SERIAL NUMBER: Enter a unique identifier for the protocol.

  
  

• Analysis Mode: Available options include Individual, Parental, and
  Familial. Individual: Used when the index is a singleton sample.
  Parental: Includes both mother and father, typically used for carrier
  screening. Familial: Does not designate an index and encompasses all
  variants across all samples within the family.

  
  

• Required TAT: The turnaround time (TAT) for analysis, specified in
  days. Once the analysis is initiated, a timer will start and continue
  running until the case is closed.

  
  

• AUTOMATIC ANALYSIS: Enable this option to apply pre-defined filter
  logic to the analysis and See appendix A for filters available.AUTO
  ANNOTATE: If a variant is reported on in one case and is seen in
  another, this feature ensures consistent relevance, classification,
  and comments.

  
  

• DESCRIPTION: Provide user-applied details or additional information
  about the protocol.

  
  

• PHENOTYPES: Enter associated phenotypic terms, if applicable.

  
  

• ALWAYS SHOW PAT/LP: Enable this option to filter logic and always
  display pathogenic and likely pathogenic variants according to
  ClinVar.

  
  

• ALWAYS SHOW PAT/LP IN-HOUSE: This allows a user to always display
  Likely Pathogenic (LP) and Pathogenic (P) variants based on In-house
  classifications. Enabling this option ensures that these variants are
  captured and presented to you, even if they fail to meet specific
  filtering criteria. This feature helps uncover potentially clinically
  significant variants that might have been missed otherwise.

  
  

• DEFAULT GENETIC MODEL: Set the default genetic model that is displayed
  first when opening the variant analysis interface.

  
  

• DEFAULT ENRICHMENT KIT & SMART FILTERING: Optionally select a custom
  enrichment kit for pre-sequencing DNA preparation.

  
  

Note: The option to hide columns has been moved from Protocol settings to Genetic model settings ( 3.12.4 Genetic Models) from version 6.2.

Fill in additional fields as needed, such as:

• DISEASE: Specify the disease related to the protocol.

  
  

• DISEASE FREQUENCY (%): Enter the expected percentage of individuals
  presenting the symptoms in the general population. This represents the
  prevalence of the disorder and is not related to allele frequency. The
  default value is 0.1%, indicating that the disorder is found in less
  than 1 out of 1000 individuals.

  
  

• CALCULATE LOCAL FREQUENCY: Enable this option to calculate the local
  frequency of the variants across internal samples within the protocol.

  
  

• REPORT CONFIGURATIONS: Choose between default or custom
  configurations. Report Configurations can be accessed in the Settings
  dialog.

  
  

• REPORT TEMPLATE: Select a report template from the available default
  or custom choices. This is available in the Settings window.

  
  

• DEFAULT SORT BY COLUMN: Define the column by which the data will be
  displayed first when opening the variant analysis interface.

  
  

• SORT DIRECTION: Specify the default data presentation order, either in
  descending or ascending manner.

  
  

• PROBAND SAMPLE SOURCE: Define the location from which the DNA was
  collected for the proband. Options include Germline, Tumor Biopsy,
  Blood, Buccal, Mitochondria, Saliva, Fetal, Bone Marrow, Other..

  
  

• VARIANT MAP: Select a target capture region defined by a BED file.

  
  

• HIDDEN COLUMNS: Specify which annotation sources should be hidden in
  the variant analysis by default. This includes:

  • REF; ALT; AA; HGVS; Zyg; Heteroplasy; RefSeq; DOM; REC; Q&R; DP4;
    DP2; Alt(%); ClinVar indications; Review statuses; Pheno; Matched
    phenotypes; ClinVar; OMIM; OMIM Inheritance; COSMIC; Mitomap;
    LitVar2; HGMD; V; Tags; G; AF(%); #CNVs/SVs; #D-CNV/SNVs; Effect;
    Sev; CADD(PHRED); Max AF(%), Splice-AI, GNEv4 AF(%), GNGv4 AF(%),
    CoLoRSdb AF (%)

• LICENSE: Provide license details for paid protocols.

  
  

• DEFAULT SORT BY COLUMN: Enable the capability to sort variants in the
  variant analysis interface by an annotation field.

  
  

• SORT DIRECTION: Choose whether the default column sorting should be in
  ascending or descending order.

  
  

• SINGLE-GENETIC MODEL VARIANT SELECTION: Setting this option to ‘Yes’
  will alert the user that a variant has already had a user annotation
  made in another genetic model.

  
  

SINGLE-GENETIC MODEL VARIANT SELECTION: The user is alertred to the prior user annotation of the vatiant.

Once all the desired fields have been assigned for the new protocol, click the Save icon. This will store the protocol with your Geneyx Analysis account, allowing you to use it for all downstream variant analysis and repeated workflows.

Protocol Configuration

In addition to the fields mentioned earlier, you can further customize your protocol by incorporating Filters, Gene Panels, and Associated samples. These options provide more flexibility and specificity to your variant analysis. Here’s an overview of these customization features:

• Filters: Filters allow you to define specific criteria to narrow down
  and focus your variant analysis within the protocol. By configuring
  filters, you can apply specific rules and conditions to select
  variants that meet your criteria. You can refer to the Filter section
  for detailed instructions on setting up filters and their application
  within the protocol.

  
  

• Gene Panels: Gene Panels provide a way to target and analyze a select
  subset of genes within your protocol. You can integrate a gene list
  into your workflow using the Gene Panels option. To create a new gene
  list, click on the New icon in the top right corner. If you need to
  edit an existing gene list, select the edit icon next to each panel.
  Ensure that all genes are entered according to the HUGO Gene
  Nomenclature Committee (HGNC) guidelines. If any gene is not
  recognized correctly, you will receive a notification.

  
  

• Associated samples: This feature allows you to associate specific
  samples with your protocol. By linking samples to the protocol, you
  can easily access and analyze relevant data within the context of the
  protocol. Associated samples can provide valuable insights and
  streamline the analysis process.

  
  

Remember that configuring filters within a protocol applies those specific filters only within that particular protocol. It allows you to tailor the analysis criteria and conditions according to the requirements of the protocol.

Additional protocol customizations

By creating and utilizing protocols, you can streamline your variant analysis processes, maintain consistency, and apply standardized criteria and reporting templates across different analyses and cases.

3.10.10 Filters

The Filters dialog provides the capability to customize the logic used within each protocol. Please note that modifying filter logic requires Administrative Privileges. Users can modify existing filters or create new ones by selecting the New icon in the upper right corner. The following dialog will be displayed, with the fields explained below:

• Name: Enter a name for the filter.

  
  

• Description: Provide a description that explains the purpose or
  function of the filter.

  
  

• Category: Choose the category under which the filter should be
  organized when accessing it in the analysis.

  
  

• Query: Within each section, you can apply custom filter logic by
  defining a Query. The Query field allows you to input a Boolean
  JavaScript expression that will be evaluated for each row of VCF
  (Variant Call Format) data. You can refer to the available filters and
  examples in the following link:
  https://github.com/geneyx/geneyx.analysis.api/wiki/FilterQuery.

  • The Query field supports all JavaScript functions, and you can find
    basic examples of JavaScript usage here:
    https://mathjs.org/examples/basic_usage.js.html.

    
    

  • Commonly used functions include “||” for OR logic, “&&” for AND
    logic, “!==”” for NOT EQUALS, “==” for EQUALS, and “>” or “<” for
    LESS THAN or GREATER THAN comparisons. If you have more complex
    filters that you would like to implement, you can reach out to
    support@geneyx.com for assistance.

    
    
• Query Data: The Query Data field can be used to capture multiple
  values. For example, you can capture relevant genes by entering the
  appropriate query.

Creating a New Filter

By utilizing the flexibility of the Query and Data fields, you can create custom filter logic that precisely matches your variant analysis requirements.

3.10.11 Genetic Model Management

Geneyx Analysis now provides advanced customization capabilities for genetic models at the account level, offering enhanced flexibility across analysis and reporting workflows. This update streamlines model governance, improves cross-analysis consistency, and significantly enhances customization for laboratories with specialized workflows.

Genetic Model Management and Column Settings

Key updates and features include:

• Renaming Genetic Models: Users can directly rename existing
  genetic models from a dedicated settings panel.

  
  

• Custom Model Creation: The system supports the creation of new
  custom genetic models for both Single Nucleotide Variants (SNVs) and
  Copy Number Variations/Structural Variants (CNVs/SVs), with the
  capacity to create up to four custom models for each type.

  
  

• Model Reordering: Genetic models can be reordered to control their
  display sequence and execution priority within each analysis.

  
  

• Column settings: Columns can be hidden or removed per genetic
  model. (Note: The feature to hide columns was previously in the
  Protocol settings)

  
  

• Reporting Section Assignment: Genetic models can now be directly
  assigned to specific sections within patient reports. This enables
  tailored clinical reporting for various categories, such as Primary
  Findings, Carrier Screening, and Pharmacogenomics (PGx).

  
  

All existing relationships between genetic models and other system components, including protocols, filters, gene panels, variant maps, and auto-execution filters, remain fully intact and functional.

Please note that genetic model settings will apply to all protocols to which the genetic model is applied.

3.10.12 Secondary Pipelines

The Secondary Pipelines provide a list of available options for variant calling in Geneyx Analysis. These options include:

• DRAGEN Exome: This pipeline enables ultra-rapid analysis of NGS
  (Next-Generation Sequencing) data for whole exomes. It is designed to
  efficiently analyze genetic variants in the exome regions of the
  genome.

  
  

• DRAGEN Exome (Clinical Exome): This pipeline enables ultra-rapid
  analysis of NGS data for a limited number of genes, specifically
  focused on genes associated with clinical exome sequencing. It allows
  for targeted analysis of clinically relevant genes.

  
  

• DRAGEN Exome (Panel): This pipeline enables ultra-rapid analysis of
  NGS data for a targeted gene panel. It is designed for analyzing
  specific gene panels, allowing for focused variant calling in the
  selected genes of interest.

  
  

• DRAGEN Exome (Target Region): This pipeline enables ultra-rapid
  analysis of NGS data for targeted regions of interest. It allows for
  efficient analysis of specific genomic regions, providing a more
  focused variant calling approach.

  
  

• DRAGEN Whole Genome: This pipeline enables ultra-rapid analysis of NGS
  data for whole genomes. It is designed to analyze genetic variants
  across the entire genome, providing comprehensive variant calling
  results.

  
  

• Sentieon Exome: This pipeline enables ultra-rapid analysis of NGS
  (Next-Generation Sequencing) data for whole exomes. It is designed to
  efficiently analyze genetic variants in the exome regions of the
  genome. Note: It is currently restricted to SNV only.

  
  

• Sentieon Genome: This pipeline enables ultra-rapid analysis of NGS
  (Next-Generation Sequencing) data for whole genomes. It is designed to
  efficiently analyze genetic variants in the exome regions of the
  genome. Note: It is currently restricted to SNV only.

  
  

It is important to note that for calling of Copy Number Variations (CNVs) and Structural Variants (SVs), a panel of normal (PON) is required for exome and panels (PON not required for WGS). The PON creates a baseline coverage profile against which your sample of interest can be compared. The PON should ideally consist of 50 samples that have undergone the same library preparation methods, although they do not necessarily have to come from the same sequencing run. To incorporate a PON to your account please contact support@geneyx.com.

Geneyx offer the option to bring your own DRAGEN license. License details can be entered under Settings-Secondary Pipelines. Please contact support@geneyx.com to utilize this option.

VCF files containing pre-annotated or marked variants can be applied to the secondary pipeline to create a unique field in the output VCF file. These marked variants will enable the user to have a customized annotation in the variant interface. This is available on request.

Secondary Pipelines

By utilizing the appropriate Secondary Pipeline based on your specific analysis needs, you can achieve efficient and accurate variant calling results in Geneyx Analysis.

3.10.13 Report Configurations

The report modification feature in Geneyx Analysis allows users to customize the appearance and content of their reports according to their specific needs. To create a new report, simply click on the New icon located in the upper right corner of the interface. This will open a pop-up window where you can configure the report settings. Here’s an overview of the options available:

• Report Name: Enter a name for the report in the provided text box.

  
  

• Company/Lab Logo: Upload the logo image of your company or laboratory
  to be included in the report. This helps in branding and
  customization.

  
  

• Color Scheme: Choose the desired color scheme for the report to match
  your branding or personal preference.

  
  

• Header: Write the header of the report, which typically includes
  information such as the name of the company or lab, address, contact
  details, and any other relevant information.

  
  

• Introduction Text: Provide an introduction text that explains the type
  of test being conducted or the intended audience of the report. This
  section can help set the context and provide necessary background
  information.

  
  

• Methods: Describe the methods applied during the analysis. This
  section can include details about the sequencing techniques, variant
  calling methods, and any other relevant information about the analysis
  workflow.

  
  

• Limitations and References, in the Report Configuration. These fields
  come pre-populated with default information, providing a structured
  foundation for report content. This ensures a more detailed and
  standardized approach to report generation, enhancing the overall
  quality and accuracy of the information presented.

  
  

• Disclaimer: Include a disclaimer section where you can add any
  necessary disclaimers, legal statements, or other relevant information
  regarding the report and its limitations.

  
  

• Signature: Decide whether you want to include a signature section in
  the report, where authorized individuals can sign off on the findings
  or conclusions.

  
  

• CIViC Evidence: Choose whether you want to include CIViC evidence in
  the report. CIViC is a database of clinically relevant genetic
  variants and their associated evidence. Adding CIViC evidence can
  provide additional information and context for the reported variants.

  
  

Report Configurations

To apply the report template configuration to a protocol, you can follow these instructions:

1. Start by navigating to the Settings menu and open the Protocol
   dialog.

   
   

2. Select the specific protocol you want to modify and click on the
   edit icon.

   
   

3. Within the protocol settings, you’ll have the option to update
   various parameters, including the selection of a pre-configured
   report.

   
   

4. Choose the report template that you have previously configured and
   saved.

   
   

5. Once you have selected the desired report, save the changes to the
   protocol settings.

   
   

From now on, every time you generate a new report using the updated protocol, it will automatically incorporate the configurations from the selected report template. This ensures consistent and efficient report generation.

Please note that for older analyses that were performed before the protocol update, you will need to manually apply the changes. To do this, simply access the navigation window and click on the Reset option. This will ensure that the updated protocol configurations are applied to the older analyses, enabling the generation of reports with the new settings.

By utilizing this process, you can easily apply and generate reports using specific configurations tailored to your requirements.

Report Configuration

Together, customizing these elements, users can create reports that align with their branding, contain all the necessary information, and meet their specific requirements. Once the report settings are configured, they can be saved and applied to generate customized reports for their analyses in Geneyx Analysis.

3.10.14 Genome Browser

Geneyx supports the ability to customize the default tracks in IGV through the settings dialog. Once configured, the selected tracks will display automatically when IGV is opened. In IGV, SNV and SV/CNV variants from the proband and associated samples derived from the VCF are displayed as a new track. This will allow the exact variant to be investigated and determine if it is shared among samples in the analysis.

Genome Browser in the Settings interface enables user to customize the tracks in IGV.

3.10.15 Variant Tags

Variant tags are integrated when annotating variants and the values can be configured in the Settings dialog. These will appear in the variant and gene interpretations in the in-house allele frequency database.

Variant Tags can be configured in the Settings interface and used when annotating variants.

3.10.16 Gene Descriptions

Within the Setting dialog, a powerful Gene Descriptions feature has been integrated, allowing the meticulous curation of individual or bulk gene interpretations. This curated information reflects in the analysis when clicking on the Gene information column, and can be transferred to the final report, enhancing the depth and specificity of genetic insights.

Gene Descriptions can be curated and automatically incorporated into analyses and reports.

3.10.17 Variant Knowledgebase

Geneyx’s classified variants are organized within a knowledgebase, uniquely tied to each account. This feature provides the capability to export and import this valuable data. An exemplary use case involves lifting over annotated variants to an alternate genome assembly, leveraging the transformed data as an allele frequency backlog for augmented evidence across different assemblies.

This interface provides five different tabs: Batch Actions, hg19 SNV, hg19 CNV/SVs, hg38 SNV, hg38 CNV/SVs.

Variant Knowledgebase Interface

Batch Actions: The Batch Actions interface enables users to import and export variant knowledgebases. In the upper right corner, there is an Import and Export Option. The import option will enable users to import external data into Geneyx, which will then be displayed in the In-House Data column for all analyses.

Clicking on Import, will provide the following options:

• Select Variant Knowledgebase Import File: This allows users to browse
  to a local file that will be imported into Geneyx. The tool tip of
  this also provides users with a template to use as a reference.
  Additional templates can be found here:
  https://github.com/geneyx/geneyx.analysis.api/tree/main/templates/Variant%20KB

  
  

• Import Name: Name of the imported file.

  
  

• Genome Build: The reference genome assembly of the input file.

  
  

• Variant Type: Is this a SNV or CNV/SVs file.

  
  

• Allow New Tags: If a tag is associated with a variant that is not
  present in the Variant Tag option, this will apply the new tags to the
  database.

  
  

• Overwrite Variants if exists: If a variant already exists in the
  knowledgebase, selecting this option will overwrite the information
  with what is being imported.

  
  

Import Variants option of Batch Actions

In the Batch Actions interface, if Export is selected, it will export all variants that have been annotated in the users account. This is configured in a format for which values are available here: https://github.com/geneyx/geneyx.analysis.api/blob/main/Variant-Kb-Templates.zip.

Hg19 SNV: If variants have been imported in the Batch Action function with hg19 reference genome and SNV as the variant type, they will be displayed here. This dialog gives the user the ability to modify the existing information or import new variants by selecting New in the upper right corner.

Hg19 SNV interface

Hg19 CNV/SVs: If variants have been imported in the Batch Action function with hg19 reference genome and CNV/SVs as the variant type, they will be displayed here. This dialog gives the user the ability to modify the existing information or import new variants by selecting New in the upper right corner.

Hg19 CNV/SVs Interface

Hg38 SNV: If variants have been imported in the Batch Action function with hg38 reference genome and SNV as the variant type, they will be displayed here. This dialog gives the user the ability to modify the existing information or import new variants by selecting New in the upper right corner.

Hg38 SNV interface

Hg38 CNV/SVs: If variants have been imported in the Batch Action function with hg38 reference genome and CNV/SVs as the variant type, they will be displayed here. This dialog gives the user the ability to modify the existing information or import new variants by selecting New in the upper right corner.

Hg38 CNV/SVs Interface

When variants have been imported into the Variant Knowledgebase, the information will display in the IN HOUSE column of analyses.

IN HOUSE column colored green, will display information from variant knowledgebase.

If you click on the interpretation on the Variant or Gene level, the associated information will be displayed, including the Source column. Information imported using the Variant Knowledgebase are represented with a notebook icon, and have a tool tip indicting the knowledgebase it was derived from.

In House data showing information pulled from Variant Knowledgebase.

3.11 Accounts

If you require the capability to grant permission to other accounts or would like to request access to someone else’s data, Geneyx Analysis supports multiple account configurations to facilitate such collaborations. For more detailed information and assistance with setting up this feature, please reach out to support@geneyx.com. The Geneyx support team will be able to provide you with further guidance and address any specific requirements or questions you may have regarding account permissions and data access.

3.12 Variant Analysis

Once an analysis has been created and the annotation process completed, users can perform variant analysis by navigating to the Dashboard window and clicking on Analyze next to the subject of interest.

The analysis screen will display all variant information provided by the VCF file as well as the annotations and genetics models that are populated based on the specified protocol.

3.12.1 Summary Information

Summary information regarding the sample is present in the upper right corner of the analysis page. The information that can be accessed includes:

1. Subject: Provides general details about the subject. Please note
   that if the calculation of variant ratios on chromosome X that are
   not in pseudo autosomal regions differs from the assigned gender,
   the user will receive a notification regarding the discrepancy.

   
   

2. Clinical: User can review the clinical details of the analysis and
   keywords selected for scoring and ranking genes.

   
   

3. VCF Samples: Here the user can see which samples were used in the
   analysis, the total number of variants for those samples, and
   Identity by Descent.

   
   

4. Identity by Descent: Underlies genetically mediated similarities
   among individuals.

   • HET in both parents: reflects the total number (Count) of
     heterozygous variants in both parents and the number of homozygous
     variants in the proband (should be around 25% for a real trio).

     
     

   • HOM-ALT in Mother, HOM-Ref in Father reflects the alternate
     alleles that are potentially transmitted from the mother to the
     proband, missing in the father and called as heterozygous in the
     proband (should be around 100% for a real trio).

     
     

   • HOM-ALT in Father, HOM-Ref in Mother reflects the alternate
     alleles that are potentially transmitted from the father to the
     proband, missing in the mother and called as heterozygous in the
     proband (should be around 100% for a real trio).

     
     

5. Uniparental Disomy (UPD) Analysis: The UPD analysis section provides
   valuable information about uniparental disomy, including the
   chromosome number, significance (p-value), and category of
   inheritance. Here’s how you can interpret and explore this analysis:

   • Chromosome: This indicates the specific chromosome number under
     investigation.

     
     

   • Significance (p-value): The p-value represents the statistical
     significance of the observed UPD. As a general guideline, a
     p-value less than 1e-40 is considered significant.

     
     

   • Category of Inheritance: This categorizes the type of inheritance
     associated with the UPD, such as maternal hetero- or isodisomy,
     paternal hetero- or isodisomy, or biparental inheritance.

     
     

   • It’s important to note that in some cases, there may be two values
     of significance associated with the UPD analysis. In such
     situations, it is recommended to select the greater value and
     perform a manual inspection for further investigation.

     
     

   • To explore additional insights and details regarding the UPD
     analysis, you can click on the “Details” option. This will provide
     more in-depth information and help you understand the specific
     characteristics and implications of the identified UPD.

     
     

   • Understanding the results of the UPD analysis is crucial for
     identifying potential genomic aberrations and gaining insights
     into the inheritance patterns of certain chromosomal regions. It
     can assist in the evaluation of genetic disorders and guide
     further investigations or medical decision-making.

     
     

   • By utilizing the UPD analysis feature, Geneyx Analysis empowers
     users to explore and interpret the significance of uniparental
     disomy, facilitating comprehensive variant analysis and aiding in
     the understanding of genetic conditions.

     
     

VCF Summary Information

3.12.2 Variant Analysis

The table interface in Geneyx Analysis offers a user-friendly and interactive experience for variant analysis. Here are some key features and customization options available within the table interface:

• Tabs for Genetic Models: The table interface is organized into tabs,
  with each tab dedicated to a specific genetic model used for the
  analysis. This allows users to focus on the variants relevant to a
  particular model and mode of inheritance.

  
  

• Interactive Table: Within each tab, the table presents the variants in
  rows, and each column represents a specific attribute of the variant.
  This tabular representation enables users to efficiently navigate and
  analyze the data.

  
  

• Collapse/Expand Categories: The attributes within the table are
  grouped into five categories. Users have the option to collapse or
  expand these categories using the ‘>’ icon located to the right of
  each category title. This feature helps to declutter the view and
  focus on the desired information.

  
  

• Customizable Column Width: Users can customize the width of individual
  columns within the table. This flexibility allows for better
  visibility and arrangement of the data. If needed, the settings icon
  provides an option to restore the default column width.

  
  

Variant Analysis Interface

By offering a well-organized and customizable table interface, Geneyx Analysis empowers users to efficiently explore and analyze variants across different genetic models. The interactive nature of the table interface enhances usability and allows for a more tailored and comprehensive variant analysis experience.

3.12.3 Filters and Tools

The Filters dialog, located on the left side of the interface, provides a summary of the applied filters for the current tab. This pane gives users an overview of the filters that have been applied to refine the variant analysis.

The variant dashboard in Geneyx Analysis provides the flexibility to apply filters to every column in the table. To apply a filter, simply click on the filter icon located next to the column header. This action will open a dialog where you can define the desired threshold or criteria for filtering.

By setting a threshold, the variant table will dynamically update to display only the variants that meet the specified criteria. This interactive filtering capability allows users to focus on specific subsets of variants based on their desired thresholds or criteria.

Furthermore, each applied column filter will be reflected in the filter dashboard on the left side of the interface. This dashboard provides a summary of all the applied filters, allowing users to easily track and manage their filtering logic.

In addition to the filters derived from the columns in the table, users can apply further filtering using predefined Gene Panels, Gene Lists, and Variant Maps. To access these additional filters, click on the Settings icon located on the left side of the window. Once the Settings icon is selected, a new window will appear, providing options to create and manage Gene Panels and Variant Maps. These custom filters can be created based on specific criteria or genetic regions of interest.

Once created, these Gene Panels and Variant Maps are stored internally within the Geneyx Analysis system and can be applied to all analysis cases. This allows users to consistently apply the same predefined filters across different analyses, saving time and ensuring consistency in the filtering logic.

Filter logic pane

In addition to the basic column filters, Geneyx Analysis offers the flexibility to create customizable filters using complex logic. These advanced filters can be created and configured in the Filters section within the Settings menu.

With the advanced filters, users have the ability to define intricate criteria and conditions based on multiple attributes and parameters. This allows for more refined and specific filtering of variants based on research or clinical requirements. Filter presets can be created in the Settings dialog and easily applied by searching for the name in the Filter window. Furthermore, if the filters need to be reset to the original filters, the user can select the gear icon on the right side. This will revert the filters to the original presets.

Resetting the filter to original presets.

However, if you encounter any difficulties or challenges in creating the desired criteria using the advanced filters, the Geneyx support team is readily available to assist you. You can reach out to support@geneyx.com for guidance and support in creating the custom filters according to your specific needs.

The support team will be able to provide you with expert assistance and help you configure the filters to effectively capture the variants of interest, ensuring a seamless and efficient analysis experience.

Together, the ability to apply column filters empowers users to explore and analyze the variants based on specific attributes or criteria of interest. It facilitates the customization of the analysis to suit individual research or clinical requirements, providing a comprehensive and efficient variant filtering experience.

Another useful tool of the Geneyx application is the magnification icon in the upper right corner. This feature enables the user to enter a gene symbol and see if any SNV or CNV/SV events are present and is independent of any filters that are applied. It serves as a holistic approach to view all variants of a given gene and associated coverage.

Magnification icon enables user to search for all variants (SNV/CNV) and coverage using gene input.

3.12.4 Genetic Models

Genetic models in Geneyx Analysis play a crucial role in identifying relevant variants based on the mode of inheritance. To navigate between different genetic models, simply click on the tab corresponding to the model of interest. Each genetic model focuses on specific inheritance patterns and provides a targeted analysis approach. Genetic models can also be applied or removed from protocol, to do this you will need to go to Protocols in the Settings dialog.

Genetic models can also incorporate specific filter logic tailored to each model. These filters can be configured in the Filter dialog accessible through the Settings menu. By customizing the filters within each genetic model, you can refine the analysis results to meet your specific requirements and focus on variants that are most relevant to the selected mode of inheritance.

Here are some of the genetic models available in Geneyx Analysis:

1. Fast Track: This model applies filters designed to identify the most
   relevant variant(s) based on the clinical phenotype, if available,
   or the pathogenicity of the variants.

   
   

2. Recessive HOM: This model focuses on displaying clinically relevant
   variants associated with recessive inheritance, specifically those
   where the proband is homozygous.

   
   

3. Recessive Compound HET: This model highlights clinically relevant
   variants associated with recessive inheritance, specifically those
   where the proband is heterozygous.

   
   

4. Dominant HET: This model showcases clinically relevant variants
   associated with dominant inheritance, particularly those where the
   proband is heterozygous.

   
   

5. Mitochondrial: This model specifically displays clinically relevant
   variants associated with mitochondrial DNA.

   
   

6. CNV: This model is designed to identify clinically relevant copy
   number variants.

   
   

7. Incidental findings: This model focuses on displaying variants
   associated with ACMG incidental findings genes.

   
   

Genetic Models based on mode of inheritance

By selecting the appropriate genetic model and applying the associated filters, you can effectively narrow down and analyze the variants that are most relevant to the mode of inheritance or specific clinical considerations. This enhances the efficiency and accuracy of variant interpretation and aids in making informed decisions during the analysis process.

3.12.5 Annotations

Within each genetic inheritance model, Geneyx Analysis provides annotations and algorithms that aid in identifying candidate variants highly associated with the patient’s phenotype. These annotations can be applied to the filter logic using the filter icon next to each column. By clicking on any of the ‘Relevance’, ‘Pathogenic’, ‘Notes’ or ‘Tags’ columns, the following annotations are available for completion:

Once a variant in annotated, the annotation information will be stored for the variant, which will be available for all downstream analyses and can be included in the final report. These columns are:

• Relevance: This annotation allows you to set the relevance of a
  variant, determining whether it should be included in the final
  report.

  
  

• Pathogenic: With this annotation, you can indicate the pathogenicity
  of a variant, determining its inclusion in the report.

  
  

• Validation: Does the annotation require validation

  
  

• Notes: You can add insights and notes about a variant, including its
  potential relevance to the diagnosis or any additional information
  deemed important.

  
  

• Tags: Variant tags can be included in the annotation and are created
  in the Settings dialog.

  
  

• Variant Interpretation: This can be used to provide context regarding
  the variant and will be stored for all downstream encounters of the
  variant.

  
  

• Variant Level Recommendations: This can be used for recommendations in
  the annotation process.

  
  

• References: Users can add PMID (up to 15) in the format PMID:XXXXXX or
  XXXXXX

  
  

• Gene Description: Populated from the Gene Description section within
  the Settings interface.

  
  

• Phenotype & Evidence: This enables users to select information from
  public annotation sources.

  
  

• ACMG criteria applied to the variant will also be visible in this
  window.

  
  

• Note: once pathogenicity or relevance are assigned to a variant the
  variant will be blocked for further editing. To allow continued
  editing return to the genetic model where pathogenicity/relevance were
  assigned and remove criteria.

  
  

The variant dashboard in Geneyx Analysis also consists of several public annotations, each providing valuable information. Here are the column details:

• LOCATION: This column displays the genetic location of the variant
  based on the selected genetic assembly (GRCh37/38). When a variant is
  selected for visualization by clicking on the location, IGV will
  display in a new window. This will allow for visualization to occur on
  a different monitor and can be easily referenced using the URL.
  Clicking on the location will direct you to the adjusted view in the
  IGV genome browser. Alternatively, you can select the UCSC icon in the
  top right to view the variant in the UCSC genome browser.

  
  

• GENE: This column displays the gene associated with the specific
  variant. The model of inheritance (AR = Autosomal Recessive, AD =
  Autosomal Dominant, XL = X linked, DG = Digenic) is indicated. Icons
  displayed next to genes indicate if a pseudogene (P), ACMG secondary
  finding gene (SF) or addition information (I), where tooltips will
  display the added information.

  
  

Gene information tips

– Clicking on the gene name will provide access to clinical information, pathways, and drugs associated with the gene. This includes OMIM/ClinVar, Expression, Pathways, and Coverage. Within this section, you will find the following details:

• GDI (Human Gene Damage Index): GDI measures the accumulated mutational
  damage of each human gene in the healthy human population. It helps
  identify genes with a higher likelihood of being disease-causing based
  on the mutational data from the 1000 Genomes Project database.

  
  

• GnomAD Missense Z-score: This score measures the constraint or
  intolerance of a gene or transcript to missense variations based on
  the gnomAD dataset. Positive Z-scores indicate more constraint, while
  negative scores indicate less constraint.

  
  

• The following metrics, derived from gnomAD (Genome Aggregation
  Database), are utilized within Geneyx Analysis to assess gene
  constraint and tolerance to variation. These metrics are now available
  for both gnomAD v2.1.1 and gnomAD v4.0.1 datasets:

  • pLI (Probability of Loss-of-function Intolerance): pLI scores
    measure how constrained or intolerant a gene or transcript is to
    loss-of-function variations. Higher pLI scores indicate intolerance
    to loss-of-function variants.

    
    

  • Loss-of-function: This section provides information on the gene’s
    tolerance to protein truncating variations, such as nonsense,
    frameshift, splicing, and deletion variations.

    
    

  • O/E (Observed/Expected): The O/E constraint score represents the
    ratio of observed to expected variants in a gene. It provides a
    measurement of a gene’s tolerance to a specific class of variation.

    
    

  • #HOM LOF: gnomAD homozygous LoF count

    
    

  • # HET LOF: gnomAD heterozygous LoF count

    
    

• OMIM/ClinVar: This section displays gene-phenotype associations
  according to OMIM (Online Mendelian Inheritance in Man) and summarizes
  pathogenic/likely pathogenic single nucleotide variants (SNVs) and
  copy number variants (CNVs) in the gene based on ClinVar. A newly
  added Edit History column in the OMIM gene table displays the most
  recent update date for each entry (e.g., “05/03/2022”), providing
  users with context when reviewing genes.

  • Clicking on the “+” icon for a given phenotype will display the
    clinical synopsis according to OMIM. The Clinical Synopsis section
    in the Online Mendelian Inheritance in Man (OMIM) database offers
    concise gene-specific summaries of clinical features and symptoms
    associated with genetic disorder/syndrome, along with information
    about the molecular basis of the associated disease.

• Phenotypes Associated with Gene: This section presents the known
  conditions associated with the gene based on OMIM phenotypes, along
  with the associated inheritance model and relevant documentation.

  
  

• Diseases Associated with in ClinGen: ClinGen (Clinical Genome
  Resource) defines the clinical relevance of genes and variants, and
  the ClinGen Variant Curation Expert Panels (VCEPs) play a crucial role
  in this process by providing expert evaluation of genetic variants to
  determine their clinical significance. This information is now
  available in the Gene information for the disease if available.

  
  

• Digenic Inheritance: Digenic inheritance refers to a mode of genetic
  inheritance in which two genes contribute jointly to the phenotype,
  meaning that variations in both genes are necessary to produce a
  specific trait or disease. Unlike classical Mendelian inheritance,
  where a single gene mutation is typically responsible for a disorder,
  digenic inheritance involves the interaction between mutations in two
  different genes. If a gene is associated with a phenotype with digenic
  inheritance, the second gene will be displayed in the Genes tab of the
  application with associated phenotype.

  
  

• DECIPHER: Disease associated with the gene from the DECIHER resource

  
  

• Orphanet: Disease associated with the gene from the Orphanet resource

  
  

• SFARI: Phenotypes and disorders associated with the gene from SFARI
  resource

  
  

• Variants: Provides a structured view of ClinVar variant
  classifications for the selected gene, including detailed tables on
  pathogenic SNVs, CNVs by condition/effect, and an overall
  pathogenicity summary.

  
  

• Coverage: If applicable, this column displays the coverage of the
  gene, providing information on the sequencing depth for that specific
  gene.

  
  

• Gene Description: This will pull in interpretations made on the gene
  level, which is available in the Settings dialog in gene descriptions.

  
  

xGene level evidence

Other useful hyperlinks inlcude:

GeneCards: https://www.genecards.org/

OMIM: https://omim.org/

GnomAD: https://gnomad.broadinstitute.org/

ClinVar: https://www.ncbi.nlm.nih.gov/

GTEX: https://www.gtexportal.org/home/

Orphanet: https://www.orpha.net/en

Medline Plus: https://medlineplus.gov/

Clinical Trials: https://clinicaltrials.gov/

Genomic and Genetic Data:

This category provides information regarding variant nomenclature. The fields include:

• REF – The allele present in the reference genome.

  
  

• ALT – The alternative call for the sample.

  
  

• AA – The amino acid change, if applicable.

  
  

• HGVS – The representation of the variant in HGVS (Human Genome
  Variation Society) nomenclature including full HGVS-compliant
  annotations for frameshift variants (e.g., p.Leu1204ValfsTer153) for
  clarity.

  
  

• ZYG – The zygosity of the variant, indicating whether it is
  heterozygous, homozygous, or hemizygous.

  
  

• A1, A2: Allele 1/Allele 2 (in case phasing blocks are present in the
  VCF file) – These provide insight into variant phasing by resolving
  haplotypes without maternal and paternal sequencing information.

  
  

• Phasing block: The phasing block in which this variant is found.
  Phasing information will be available if present in the VCF file.

  
  

• REFSEQ – The relevant transcript ID(s) that pertain to the effect on
  the protein. This track contains RefSeq Gene transcripts annotated by
  the NCBI Homo sapiens Annotation Release. The canonical transcript is
  now selected according to Matched Annotation from the NCBI and
  EMBL-EBI (MANE). If a MANE transcript is not available, the canonical
  transcript will be selected based on shortest transcript ID.

  
  

• Exon #: The impacted exon of the gene.

  
  

• Splice Distal: Distance of the splice site to the canonical splice
  site location.

  
  

• CODON – The codon change caused by the variant.

  
  

• DBSNP – Known identifier (dbSNP RSID). This track displays common
  single nucleotide polymorphisms (SNPs) from dbSNP build 151. dbSNP is
  a database that catalogs short variations in nucleotide sequences from
  various organisms. It includes single nucleotide variations, short
  nucleotide insertions and deletions, short tandem repeats, and
  microsatellites. Common SNPs are those with a minor allele frequency
  of at least 1% in at least one 1000Genomes population, contributed by
  two or more founders. Some entries in dbSNP may have additional
  information associated with them, such as disease associations,
  genotype information, and allele origin.

  
  

• DBSNP version – The earliest dbSNP version that included the variant.

  
  

ACMG:

The American College of Medical Genetics and Genomics (ACMG) has developed guidelines for interpreting sequencing variants. These guidelines are used to classify variants based on various types of evidence, including population data, computational data, functional data, and segregation data.

• Dom – This option aggregates data from public databases using the ACMG
  Guidelines for variants associated with dominant inheritance.

  
  

• Rec – This option aggregates data from public databases using the ACMG
  Guidelines for variants associated with recessive inheritance.

  
  

By selecting one of these options, you will be directed to the full ACMG classification panel. On the left side, you will find different categories of the ACMG guidelines, and the middle dialog displays the specific ACMG criteria. Applicable criteria will be solid, while empty criteria indicate insufficient evidence for application. Crossed-out criteria indicate that they are not applicable or that the variant evidence contradicts the criteria.

Population frequency information is from gnomAD v2 for GNE and from gnomAD v4.1 for GNG.

Hovering over an individual criterion will provide additional details, and clicking on a cell allows for manual override of the autoclassification. The supporting evidence for each criterion is available on the right-hand side of the panel.

Associated Samples:

• The “Associated samples” section provides information about the
  zygosity (heterozygous or homozygous) of the variant in parents, if
  parental samples were submitted for analysis. This allows for the
  assessment of variant inheritance patterns and can be useful in
  determining the potential relevance of the variant in relation to the
  patient’s phenotype.

Variant Calling Q&R (Quality and Read Information):

This category provides information extracted from the VCF (Variant Call Format) file, which includes details about the quality and characteristics of the variant calls.

• Q&R: This field classifies the variant calls based on their quality.
  Variants are categorized as Low quality if the coverage is less than
  10x and the genotype quality (GQ) is less than 15. Variants are
  categorized as Medium quality if the coverage is greater than 10x or
  the GQ is greater than 15. Variants are categorized as High quality if
  the coverage is greater than 20x and the GQ is greater than 50.

  
  

• Depth: This indicates the total read depth at the variant position,
  which represents the number of reads covering that particular genomic
  region.

  
  

• DP2: This field provides the read counts for the reference allele
  (Ref) and the alternative allele (Alt), respectively. It gives
  insights into the distribution of reads supporting each allele.

  
  

• %ALT: The percentage of reads that display the alternative allele
  among all the reads at the variant position. This provides an
  estimation of the allelic frequency based on the sequencing data.

  
  

• GQ: The variant calling quality score (GQ) represents the confidence
  in the genotype call for that specific variant. A higher GQ value
  indicates a higher confidence in the called genotype.

  
  

• FILTER: This field indicates the quality filter status assigned by the
  variant caller algorithm. It helps determine if the variant passes
  specific quality control criteria or if it should be flagged as
  potentially unreliable.

  
  

• PL: Phred-scaled genotype likelihoods (HOM REF, HET, HOM ALT)
  represent the log-scaled likelihoods of each possible genotype. These
  values provide additional information about the likelihood of each
  genotype given the observed sequencing data.

  
  

• AMP SCORE: The amplification score represents the coverage at the
  variant position relative to the median coverage across the genome. It
  provides insights into the level of amplification or depletion of
  reads at the variant location compared to the average coverage.

  
  

These variant calling and read information metrics help assess the quality, depth, genotype likelihoods, and other relevant characteristics of the variant calls in the analysis.

Clinical Evidence:

This category provides information about the clinical evidence associated with the variants.

• PHENO: The PHENO score represents the association between the selected
  phenotypes and the gene. It is calculated using the Geneyx
  Knowledgebase and Geneyx Phenotyper (Pheneyx), which integrates major
  clinical data sources (OMIM, ClinVar, OrphaNet, and HPO) and utilizes
  advanced matching capabilities to establish direct and indirect
  associations between genes and biomedical information.

  • This feature includes: matched and unmatched phenotypes to the gene,
    diseases and variants related and the publication year of displayed
    publications. Now, users can easily discern the chronological
    context of phenotypic data, adding a valuable layer of context to
    the prioritization process.

• MATCHED PHENOTYPES: This field indicates the number of matching terms
  between the selected phenotypes and the gene. It helps identify the
  degree of phenotypic overlap between the gene and the observed patient
  characteristics.

  
  

• CLINVAR: The CLINVAR database, maintained by NCBI, provides
  information about the phenotypes and supporting evidence for variants
  listed in the dbSNP database. It offers valuable insights into the
  clinical significance and implications of specific variants. For more
  detailed information about the ClinVar database, please refer to the
  “About ClinVar” section.

  
  

• REVIEW STATUS: One of the key features of ClinVar is its review status
  for each variant, which indicates the level of evidence and consensus
  supporting the clinical significance of the variant. The review status
  of ClinVar submissions is a separate column that can also be filtered
  on.

  
  

• CLN-AA: The CLN-AA field represents the clinical significance of
  matching ClinVar entries based on amino acid changes. Clicking on the
  hyperlink provides access to clinical relevance and summaries for
  pathogenic entries, offering valuable insights into the clinical
  interpretation of the variant.

  
  

• OMIM: OMIM (Online Mendelian Inheritance in Man) is a comprehensive
  and authoritative compendium of human genes and genetic phenotypes. It
  is freely available and regularly updated. The OMIM track displays the
  names of associated HPO (Human Phenotype Ontology) phenotypes,
  providing a valuable resource for understanding the genetic basis of
  various phenotypes.

  
  

• OMIM Inheritance: The OMIM Inheritance track displays information
  about the inheritance patterns associated with the gene and its
  corresponding phenotypes. The inheritance patterns include Autosomal
  Dominant (AD), Autosomal Recessive (AR), Multifactorial (Mu), Somatic
  Mutation (SMu), Digenic Dominant (DD), Digenic Recessive (DR),
  Isolated Cases (IC), Mitochondrial (Mi), Pseudoautosomal Dominant
  (PADom), Pseudoautosomal Recessive (PARec), Somatic Mosaicism
  (SomMos), X-Linked (XL), X-Linked Dominant (XLD), X-Linked Recessive
  (XLR), and Y-Linked (YL).

  
  

• CLINGEN HAPLO: ClinGen haplosensitivity and hyperlink (in CNV/SV
  genetic model only)

  
  

• CLINGEN TRILPO: ClinGen triplosensitivity and hyperlink (in CNV/SV
  genetic model only)

  
  

• CIVIC: CIViC is an open-access, community-driven web resource for the
  clinical interpretation of variants in cancer. It aims to facilitate
  precision medicine by providing an educational platform for
  disseminating knowledge and fostering active discussions on the
  clinical significance of cancer genome alterations. The information
  includes the CIViC Evidence ID (EID), Disease, and Drugs associated
  with the variant.

  
  

• EL: The Evidence Level field denotes the experimental method from
  which the evidence statement is derived. It ranges from inferential
  associations from a single experiment to trusted associations that
  routinely inform clinical action.

  
  

• ET: The Evidence Type field describes the predictive, prognostic, or
  diagnostic association between an evidence statement and a variant,
  providing insights into the nature of the evidence.

  
  

• TR: The Trust Rating field provides a rating out of 5 stars,
  indicating the level of trustworthiness or reliability assigned to the
  evidence.

  
  

• PUBS: The PUBS field represents the number of relevant publications
  available in the MasterMind database, offering additional scientific
  literature supporting the variant.

  
  

• LitVar is an integrated annotation source that will enhance phenotypic
  prioritization through improved literature mining and can be used as
  an independent annotation source. LitVar allows the search and
  retrieval of variant specific information from relevant studies in the
  literature, with related concept (e.g., diseases) annotations. By
  normalizing variant names, LitVar returns the same results regardless
  of which name of a variant (e.g. BRCA1 p.P871L or c.2612C>T) is used
  in the query.

  
  

• GoogleSearch, Geneyx supports the ability to easily search for the
  variant in google with a useful hyperlink output. This can then be
  used for other downstream purposes, and aims to enhance searching
  functionalities.

  
  

This clinical evidence section offers valuable insights into the phenotypic associations, clinical significance, inheritance patterns, and supporting literature associated with the variants under consideration.

IN HOUSE:

This section provides information about the frequency of the variant observed internally across samples.

• V: The V field contains in-house annotations for this variant. It
  includes associated Date, Relevance, Pathogenicity, Notes, and
  Analyses, providing additional context and details about the variant.
  This will display the most recent classification (by pathogenicity) in
  the local allele frequency database. This classification can be
  applied as a filter and will enhance the understanding of variants and
  genes previously evaluated.

  
  

• G: The G field includes the Date and Time of observed variants, amino
  acid (AA) changes, Zygosity Effect, Relevance, Pathogenicity, Notes,
  and Analyses. This information helps in understanding the specific
  characteristics and implications of the variant.

  
  

• AF (%): The AF (%), or Allele Frequency, represents the frequency of
  the variant allele in all the samples of the current account. It is
  expressed as a percentage, providing an indication of how common the
  variant is within the sample population.

  
  

• #Local Het: The #Local Het field indicates the count of samples
  within this account that have this heterozygous variant. It provides
  insights into the number of individuals carrying one copy of the
  variant allele in the local sample population.

  
  

• #Local Hom: The #Local Hom field represents the count of samples
  within this account that have this homozygous variant. It helps
  identify the number of individuals with two copies of the variant
  allele in the local sample population.

  
  

• #Local Hem: The #Local Hem field displays the count of samples
  within this account that have this hemizygous variant. It indicates
  the number of individuals with a single copy of the variant allele in
  the local sample population, typically applicable to variants on sex
  chromosomes.

  
  

The IN HOUSE section provides information about the variant’s frequency and characteristics within the internal sample population, aiding in the assessment and interpretation of the variant’s significance.

Matched CNV/SVs:

Enhancing the SNV genetic models, Geneyx incorporates CNV/SV visibility within the analysis. This column displays the total number of CNVs overlapping the given SNV position. Additionally, a dedicated column highlights CNV events predicted to be damaging, encompassing events that overlap exons and exhibit an allele frequency below 5%. To further simplify your analysis, you can now search for CNVs using the magnifying glass tool, providing efficient access to the desired information.

Effect & Prediction:

This section provides relevant information about the effect of the variant, including functional and conservation annotations.

• EFFECT: The EFFECT field describes the impact of the variant on the
  protein, including splicing effects. It provides various options, such
  as downstream, exon, frameshift, missense, synonymous, start gained,
  stop change, and more. This information helps understand the specific
  alteration caused by the variant.

  
  

• SEV: The SEV field indicates the severity of genetic variants based on
  their coding or splicing effects. Variants are categorized into high,
  medium, or low severity. High severity variants include loss of
  function (LOF), splicing, and coding variants with specific in-silico
  prediction scores. Medium severity variants have less stringent
  criteria than high severity variants. Low severity variants meet
  specific criteria, such as being in the splice site region or a LOF
  variant with low scores for CADD or Splice-AI.

  
  

• CADD (PHRED): The CADD (PHRED) field provides the PHRED-scaled scores
  (ranging from 1 to 99) for Combined Annotation Dependent Depletion
  (CADD) scores. These scores indicate the deleteriousness of single
  nucleotide variants, indels, and insertion/deletions in the human
  genome. Higher scores suggest a higher likelihood of deleterious
  effects.

  
  

• CADD (RAW): The CADD (RAW) field provides the raw CADD scores, also
  known as “C-scores,” which come directly from the Support Vector
  Machine model. These scores have relative meaning, with higher values
  indicating a higher likelihood of deleterious effects.

  
  

• PLI: The PLI field represents the gnomAD probability of being a LoF
  (Loss of Function) intolerant variant. The pLI score measures how
  constrained or intolerant a gene or transcript is to
  protein-truncating variation. Higher pLI scores indicate greater
  intolerance to truncating variants.

  
  

• REVEL: The REVEL field displays the Rare Exome Variant Ensemble
  Learner (REVEL) score, which predicts the pathogenicity of missense
  variants. It combines scores from multiple individual tools to assess
  the likelihood of a variant being disease-causing. Scores range from 0
  to 1, with higher scores indicating a higher likelihood of
  pathogenicity.

  
  

• SPLICE-AI: The SPLICE-AI field provides information about the SpliceAI
  score, which predicts splice junctions and identifies noncoding
  genetic variants that cause cryptic splicing. The score has different
  cutoffs indicating high recall, recommended, and high precision.
  Precomputed scores for whole genomes are available to improve
  annotations for non-coding variants in addition to hyperlink to
  SpliceAI look-up (https://spliceailookup.broadinstitute.org/) for
  masked scores and to change max distance

  
  

• AlphaMissense: AlphaMissense is a predictive model that generates
  pathogenicity scores for single nucleotide missense variants in human
  protein-coding genes. The predictions cover a vast number of possible
  variants (71 million) across approximately 19,000 genes. The
  pathogenicity scores provided by AlphaMissense range from 0 to 1 and
  can be interpreted as the predicted probability of a variant being
  clinically pathogenic. Higher scores suggest a higher likelihood of
  the variant contributing to a clinical condition.

  
  

• PHYLOP: The PHYLOP field represents the PhyloP conservation score,
  which measures the evolutionary conservation at individual alignment
  sites based on multiple alignments of mammalian genomes. Higher scores
  indicate greater conservation.

  
  

• GERP_RS: The GERP_RS field displays the Genomic Evolutionary Rate
  Profiling Rejected Substitutions (GERP NR) scores. These scores
  estimate the evolutionary constraint of specific positions, with
  higher scores indicating greater conservation.

  
  

• LRT PRED, MUTTASTER, POLYPHEN2 HDIV, POLYPHEN2 HVAR, SIFT: These
  fields provide predictions from various tools, such as Likelihood
  Ratio Test (LRT), MutationTaster, PolyPhen2 (HDIV and HVAR), and SIFT.
  These tools assess the deleteriousness or tolerability of variants
  based on specific criteria.

  
  

• UNIPROT: The UNIPROT field shows the accession of the protein in the
  Universal Protein Resource (UniProt), which is a comprehensive
  database of protein sequence and annotation data.

  
  

• ADA SCORE, RF SCORE: The ADA SCORE and RF SCORE fields present
  prediction scores based on ada-boost and random forests, respectively.
  These scores estimate the probability of a splicing variant affecting
  splicing, with suggested cutoffs for binary predictions.

  
  

Frequency:

This section provides information about the frequency of the variant across different population databases.

• MAX AF (%): The MAX AF field displays the maximum observed allele
  frequency in population databases such as 1000 Genomes, , and gnomAD.
  It represents the highest frequency at which the variant has been
  observed in these databases.

  
  

• GNEV4 AF (%): gnomAD v4 exomes allele frequency (for GRCh38 samples
  only)

  
  

• GNGV4 AF (%) gnomAD v4 genomes allele frequency (for GRCh38 samples
  only)

  
  

• COLORSDB AF (%): CoLoRSdb allele frequency

  
  

• #HOM: The #HOM field represents the count of samples in gnomAD where
  the variant is homozygous. It indicates the number of individuals in
  the gnomAD database who have two copies of the allele.

  
  

• #HET: The #HET field represents the count of samples in gnomAD where
  the variant is heterozygous. It indicates the number of individuals in
  the gnomAD database who have one copy of the allele.

  
  

• #HEMI: The #HEMI field represents the count of samples in gnomAD
  where the variant is hemizygous. It indicates the number of
  individuals in the gnomAD database who have a single copy of the
  allele on the X chromosome.

  
  

• GnomAD Flag: The GnomAD Flag field provides information about quality
  control filters applied to the variant in GnomAD exomes and genomes.
  These filters ensure the reliability and accuracy of the variant data.

  
  

• GNOMAD-v4 Hom: The count of samples with this homozygous variant in
  gnomAD v4 (for GRCh38 samples only)

  
  

• GNOMAD-v4 Het: The count of samples with this heterozygous variant in
  gnomAD v4 (for GRCh38 samples only)

  
  

• COLORSDB HOM: The count of samples with this homozygous variant in
  CoLoRSdb

  
  

• COLORSDB HET: The count of samples with this homozygous variant in
  CoLoRSdb

  
  

• 1K GENOME AF %: The 1K GENOME AF field displays the allele frequency
  of the variant in the 1000 Genomes Project database. It represents the
  frequency at which the variant has been observed in the 1000 Genomes
  population.

  
  

• ESP African AF %: The ESP African AF field represents the allele
  frequency of the variant in the Exome Sequencing Project (ESP) African
  American population. It indicates the frequency at which the variant
  has been observed in this specific population.

  
  

• ESP European AF %: The ESP European AF field represents the allele
  frequency of the variant in the ESP European population. It indicates
  the frequency at which the variant has been observed in this specific
  population.

  
  

• DBSNP MAF %: The DBSNP MAF field displays the minor allele frequency
  of the variant in the dbSNP database. It represents the frequency at
  which the variant has been observed in the general population
  according to dbSNP.

  
  

• HAN AF %: The HAN AF field represents the allele frequency of the
  variant in the CONVERGE project for the Han Chinese population. It
  indicates the frequency at which the variant has been observed in this
  specific population.

  
  

• GNE AF: The GNE AF field displays the gnomAD Exomes allele frequency.
  It represents the frequency at which the variant has been observed in
  the gnomAD Exomes population.

  
  

• GNG AF: The GNG AF field displays the gnomAD Genomes allele frequency.
  It represents the frequency at which the variant has been observed in
  the gnomAD Genomes population.

  
  

• GNE controls AF (%): The GNE controls AF field represents the allele
  frequency of the variant in gnomAD exomes controls. It indicates the
  frequency at which the variant has been observed in the gnomAD exomes
  control group.

  
  

• GNG controls AF (%): The GNG controls AF field represents the allele
  frequency of the variant in gnomAD genomes controls. It indicates the
  frequency at which the variant has been observed in the gnomAD genomes
  control group.

  
  

• GNE AMR: gnomAD exomes AMR allele frequency

  
  

• GNE AFR: gnomAD exomes AFR allele frequency

  
  

• GNG AFR: gnomAD genomes AFR allele frequency

  
  

• GNE ASJ: gnomAD exomes ASJ allele frequency

  
  

• GNG ASJ: gnomAD genomes ASJ allele frequency

  
  

• GNE EAS: gnomAD exomes EAS allele frequency

  
  

• GNG EAS: gnomAD genomes EAS allele frequency

  
  

• GNE FIN: gnomAD exomes FIN allele frequency

  
  

• GNG FIN: gnomAD genomes FIN allele frequency

  
  

• GNE NFE: gnomAD exomes NFE allele frequency

  
  

• GNG NFE: gnomAD genomes NFE allele frequency

  
  

• GNE OTH: gnomAD exomes OTH allele frequency

  
  

• GNE SAS: gnomAD exomes SAS allele frequency

  
  

Mitochondrial:

This section provides information specific to mitochondrial variants.

• Heteroplasmy: Heteroplasmy is a measure of the presence of different
  mitochondrial DNA variants within an individual. It is calculated by
  dividing the depth of informative reads supporting the reference
  allele by the total depth of informative reads at that particular
  location. Heteroplasmy indicates the degree of variability or mixture
  of mitochondrial DNA variants within an individual.

  
  

• MitoMap: MitoMap is a valuable resource that contains published data
  on human mitochondrial DNA variation. The variant tables in MitoMap
  provide information such as frequencies of mitochondrial DNA variants,
  which are derived from 48,882 full-length human mitochondrial DNA
  sequences. MitoMap is a reliable source for exploring and
  understanding mitochondrial DNA variation. For more detailed
  information about MitoMap, please refer to the “About MITOMAP”
  section.

  
  

CNV/SV Annotations:

The CNV/SV (Copy Number Variant/Structural Variant) annotations provide detailed information about structural variations and copy number variants. These annotations offer insights into the analysis of CNVs/SVs, including various annotation sources and visualization capabilities. Additionally, they allow for a closer examination of individual genes affected by the event.

• Location: This indicates the genetic location of the structural
  variation. Selecting the location provides further details in the IGV
  genome browser.

Genomic and Genetic Data:

This category provides information about the size and characteristics of the structural variation.

• REF: Reference bases if available

  
  

• Length (LEN): The length of the CNV/SV in base pairs.

  
  

• %RMSK: The percentage of DNA sequences spanning interspersed repeats
  and low complexity DNA sequences.

  
  

• Cytogenetic Region: The cytogenetic region associated with the
  variant. CNVs and SVs are annotated with frequency databases according
  to the matching effect. CNV/SV frequency databases include DGV, DGV
  Gold, gnomAD SV, and genomes.

  
  

• Start: The starting position of the structural variation.

  
  

• End: The ending position of the structural variation.

  
  

• Zygosity: Zygosity of CNV/SVs

  
  

• Junction reads: Number of reads spanning the event junction

  
  

• Spanning reads: Number of reads aligning near the event junction

  
  

• Associated samples: The number of overlapping CNV/SVs in associated
  samples (if applicable).

  
  

Variant Calling Q&R:

• Score: Quality score of the event.

  
  

• ROH Score: Score for runs of homozygosity events. The score increases
  with every additional homozygous variant (0.025) and decreases with a
  large penalty (1-0.025) for every heterozygous SNV.

  
  

• CAMO(%): Dark and camouflage genes refer to those that are difficult
  to pinpoint due to various reasons, such as their location in genomic
  regions with complex structural variations, repetitive sequences, or
  areas prone to technical challenges in sequencing. Annotating Dark and
  Camouflage genes helps to improve the accuracy and comprehensiveness
  of CNV analysis. As such Dark (low sequencing depth) and Camouflage
  (ambiguous alignment) are now new columns integrated into the CNV/SV
  tab and displayed in IGV.

  
  

• DARK(%):Dark and camouflage genes refer to those that are difficult to
  pinpoint due to various reasons, such as their location in genomic
  regions with complex structural variations, repetitive sequences, or
  areas prone to technical challenges in sequencing. Annotating Dark and
  Camouflage genes helps to improve the accuracy and comprehensiveness
  of CNV analysis. As such Dark (low sequencing depth) and Camouflage
  (ambiguous alignment) are now new columns integrated into the CNV/SV
  tab and displayed in IGV.

  
  

• FFPM: Fusion fragments per million total reads

  
  

Effect & Prediction:

This section presents information related to the effect and predicted consequences of the variant.

• Effect: Describes the effect of the variant, including Breakend (BND),
  Deletion (DEL), Duplication (DUP), Insertion (INS), Runs of
  Homozygosity(ROH), and Inversion (INV).

  • ROH refers to a stretch of consecutive homozygous genetic markers on
    both chromosomes, which can be indicative of offspring from
    consanguineous marriages and increase the risk of autosomal
    recessive disorders.

• Copy Number: The number of copies of the CNV/SV event.

  
  

• Zygosity (ZYG): The genotype call in the proband sample, indicating if
  it is heterozygous (HET), homozygous (HOM), or hemizygous (HEMI).

  
  

• A1/A2: This reflects the allele phasing if available in the VCF file.

  
  

• Genes: The number of genes affected by the event.

  
  

• Max Exons (M.EXONS): The maximum number of exons affected within
  genes.

  
  

• Enhancers (ENHS): The number of enhancers impacted by the event.

  
  

• Repeat: If the variant is a repeat variant, information about the
  repeat will be presented, including the number in the wild-type
  sequence, the repeat sequence itself, and the number in each allele.

  
  

• Gene fusion: Pair of genes involved in fusion event

  
  

If associated samples are applied to the analysis, such as Trio workflows, the interface will now display events (CNV/SV) that directly match the proband. This will enable detection of de novo or transmitted events. Geneyx also displays the Reference and Alternate alleles, if present, as well as read depth and variant allele frequency.

Clinical Evidence:

This category displays clinical evidence associated with the variant based on annotation sources.

• Pheno (Max): The score of the association between selected phenotypes
  and the gene. This score utilizes the Geneyx Knowledgebase and Geneyx
  Phenotyper (Pheneyx) to consolidate clinical data sources such as
  OMIM, ClinVar, OrphaNet, and HPO.ClinVar: Clinical significance of
  matching ClinVar entries.

  
  

• OMIM Inheritance Statistics: A summarized inheritance pattern provided
  by OMIM for impacted genes.

  
  

In House:

• #LV: The total number of local variants out of all samples present in
  the account.

  
  

• #Local Het: The count of samples with this heterozygous variant in
  the account.

  
  

• #Local Hom: The count of samples with this homozygous variant in the
  account.

  
  

• #Local Hemi: The count of samples with this hemizygous variant in the
  account.

  
  

Matched SNVs:

• #SNV: The total number of SNVs found within the genomic location of
  the CNV/SV within the sample.

  
  

• #D-SNVs: The total number of potentially deleterious SNVs (VUS, LP,
  and Pathogenic) found within the genomic location of the event within
  the sample.

  
  

Frequency:

Frequency information regarding the structural variation in population databases.

• Max AF (%): The maximum allele frequency for this event (>80%
  overlap) based on DGV, DGV-Gold, gnomAD-SV, and gnomAD genomes.

  
  

• DGV AF (%): The highest overlapping variant allele frequency for this
  event in DGV.

  
  

• DGV-Gold AF (%): The highest overlapping variant allele frequency for
  this event in DGV Gold.

  
  

• GDS AF (%): The highest overlapping variant allele frequency for this
  event in gnomAD.

  
  

Genes Affected by CNV:

Once a CNV (Copy Number Variant) of interest has been identified, it is possible to investigate the individual gene level to gain more insights. When a CNV is selected, the bottom window will update to display the genes affected by the CNV.

The information at the gene level includes:

• GENE: Displays the impacted gene. Clicking on the gene will provide
  access to clinical information, pathways, and drugs associated with
  this gene.

  
  

• OVERLAP: Indicates if the event is a full or partial overlap of the
  gene.

  
  

• REFSEQ – Transcript according to which gene is affected

  
  

• PHENO: The score of the association between the selected phenotypes
  and the gene. This scoring algorithm utilizes the Geneyx Knowledgebase
  and Geneyx Phenotyper (Pheneyx), which consolidates major clinical
  data sources such as OMIM, ClinVar, OrphaNet, and HPO. It employs
  advanced matching capabilities with direct and indirect associations
  between genes and biomedical information.

  
  

• MATCHED PHENOTYPES: The number of matching terms and the relevant list
  of phenotype terms associated with the gene.

  
  

• IMPACTED EXON(S): Exon(s) affected by the event according to MANE
  transcript

  
  

• #CODING EXONS: number of coding exons affected within this gene by
  the event.

  
  

• #NON_CODING EXONS: number of non-coding exons affected within this
  gene by the event.

  
  

• ENH SCORE: Enhancer confidence score.

  
  

• ENH-GENE SCORE: Gene-Enhancer associated score.CLINVAR: Clinical
  significance of matching ClinVar entries. Clicking on it provides
  relevance and summary for pathogenic entries.

  
  

• OMIM (GENE): The gene phenotype as defined by OMIM.

  
  

• OMIM INHERITANCE: The disease inheritance pattern defined by OMIM.

  
  

• #GNF: Number of features in gnomAD SV that fully overlap this
  gene/enhancer.

  
  

• #GNP: The number of features in gnomAD SV that partially overlap the
  gene/enhancer.

  
  

• #D-HOM SNVs: Total number of homozygous deleterious SNVs (Single
  Nucleotide Variants) found within the genomic location of the CNV and
  the gene.

  
  

• #D-HET SNVs: Total number of heterozygous deleterious SNVs found
  within the genomic location of the CNV and the gene.

  
  

The CNV event can also be visualized using the IGV genome browser, which can be expanded using the icon in the upper right corner. Alternatively, if preferred, this event can be plotted in the UCSC browser using the hyperlink in the upper right corner.

a CNV/SV ACMG Guidelines

The CNV/SV tab supports the technical standards for reporting of constitutional copy number variants according to the joint consensus recommendations of ACMG and ClinGen, reference article here https://pubmed.ncbi.nlm.nih.gov/31690835/ . ACMG guidelines are calculated automatically for all deletion and duplication events and there is an interface to modify criteria using internal evidence. The interface reflects a similar approach as the ClinGen CNV calculator with full transparency of activated or inactivated criteria, as well as audit trails for all actions implemented.

ACMG Guidelines for CNV/SV

When you click on the ACMG criteria, a detailed view will appear, showcasing the logic used to score the event. This view will present the five different categories defined by the ACMG guidelines, each automatically populated with the relevant criteria. Users will have the ability to manually modify each criterion to tailor the assessment as needed.

Legend for ACMG automated criteria

The options include:

• Solid with border- Scored Criteria

  
  

• Solid without border- Criteria with evidence

  
  

• Border Only – Not an automated criterion

  
  

• Slash Box- Unmet criteria (inactivated automatically based on
  evidence)

  
  

When you hover over a specific criterion, a tooltip will appear, providing detailed information about the evidence used for scoring. This tooltip will display a concise summary of the supporting data, such as relevant studies, experimental results, computational predictions, or clinical observations that contributed to the assessment of the criterion. This feature ensures that users have immediate access to the underlying evidence, allowing for a better understanding of how each criterion was evaluated and scored.

Each criterion will have a tool tip feature that shows what evidence was used to score it.

When you click on a specific criterion, a detailed information panel will open, providing comprehensive insights into the focus and relevance of that criterion. This panel will include a summary of the criterion’s purpose and its role in the overall scoring process. Additionally, users will have interactive options within this panel:

• Activate or Inactivate: Users can toggle the criterion on or off,
  allowing them to include or exclude it from the final scoring. This
  flexibility is crucial for adapting the evaluation to specific
  contexts or new information.

  
  

• Adjust the Score: Users will have the ability to manually adjust
  the score associated with the criterion. This feature allows for
  fine-tuning the assessment based on expert judgment or additional
  evidence that may not have been captured by the automated scoring
  system.

  
  

These functionalities provide users with a robust and flexible tool to customize the evaluation process, ensuring that the scoring reflects the most accurate and relevant information available.

ACMG criterion options

Once the criteria have been saved, the interpretation will be securely stored for all future encounters of that specific variant. This feature ensures that the detailed assessment, including any manual adjustments and contextual notes, is readily available for reference in subsequent analyses. By storing this information, users can maintain consistency and accuracy in variant interpretation over time.

This capability is particularly valuable for the interpretation of Copy Number Variants (CNVs) and Structural Variants (SVs), such as deletions and duplications. By having a refined and reusable interpretation framework, users can apply a more precise and efficient approach to evaluating these variants. The stored interpretations will align with the best practice workflows established by the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen).

Through this feature, the system supports a streamlined and standardized process, allowing users to leverage past evaluations and ensure that each variant is interpreted according to the highest standards of clinical genetics. This not only enhances the quality and reliability of variant interpretation but also saves time and resources by reducing redundant efforts in re-evaluating previously encountered variants.

3.12.5b Repeat Expansion Analysis

MRepeat expansion refers to a specific type of genetic mutation where a segment of DNA, typically consisting of a short sequence of nucleotides, is repeated multiple times within a gene. These repeated sequences are also known as “tandem repeats” or “microsatellites.” When these repeat sequences expand beyond a certain threshold, it can lead to various genetic disorders and diseases. Geneyx supports the ability to import repeats from different file formats, including DRAGEN, ONT, and PacBio.

Some well-known genetic disorders associated with repeat expansions include:

• Huntington’s Disease: This is a neurodegenerative disorder caused by
  an expanded CAG repeat in the HTT gene. The expanded repeat leads to
  the production of a mutant protein that damages nerve cells in the
  brain.

  
  

• Fragile X Syndrome: Fragile X syndrome is caused by an expansion of
  CGG repeats in the FMR1 gene. It is the most common inherited cause of
  intellectual disability and autism.

  
  

• Myotonic Dystrophy: Myotonic dystrophy is characterized by an
  expansion of CTG repeats in the DMPK gene. This expansion results in
  muscle wasting, myotonia, and a range of other symptoms.

  
  

• Spinocerebellar Ataxias (SCAs): Several SCAs are caused by repeat
  expansions in different genes. These disorders lead to problems with
  coordination and balance due to damage in the cerebellum.

  
  

• Frontotemporal Dementia (FTD): Some forms of FTD are associated with
  repeat expansions in specific genes, such as the C9orf72 gene.

  
  

The severity of these genetic diseases often depends on the length of the repeat expansion. Longer expansions tend to be associated with more severe and earlier onset of symptoms. The underlying molecular mechanisms by which these repeat expansions cause disease are complex and can vary from one disorder to another.

Repeat expansion analysis is integrated into the CNV/SV genetic model and we have introduced a color-coding system, which adds a layer of visual information to the analysis results. Repeats are now displayed in one of four distinct colors, each of which represents a specific clinical implication:

• Red: A red color signifies that at least one of the repeat alleles
  found in the individual is within the pathogenic range. This color
  indicates a significant deviation from the wildtype, potentially
  warranting further medical attention.

  
  

• Orange: Repeats are colored orange when at least one repeat is within
  the premutation range, but no repeat falls within the pathogenic
  range. This color serves as a warning sign that a repeat is in a range
  of clinical interest.

  
  

• Green: A green color indicates that both repeats are outside of the
  mutation or premutation range. This is generally considered a normal
  result with no immediate clinical concern.

  
  

• No Color: In cases where there isn’t enough clinical data available to
  establish a clear range threshold, the repeat is left uncolored. This
  signals that further research may be needed to determine the clinical
  implications.

  
  

Repeat Expansion analysis showing full mutation in TCF4 gene, associated with Fuchs endothelial corneal dystrophy.

Repeat expansions are also split into two columns. The first will show the observed copies on allele one and allele two, and the second column will show the repeat unit. Hovering over the repeat will show the wild type allele copy.

Enhanced Filtering Options

In addition to the color-coding system, Geneyx implements filtering options to empower users in customizing their analysis. Users can filter repeats based on their color or potential clinical impact. This feature allows for more focused and efficient analysis, making it easier to identify relevant mutations.

Repeats filtering options.

The ranges and clinical implications attributed to each color have been curated and are derived from various authoritative sources. These sources include:

• GeneReviews: A comprehensive resource for genetic disorders, providing
  detailed information on the clinical characteristics and management of
  various conditions.

  
  

• MedlinePlus: A trusted source of health information from the National
  Library of Medicine, offering in-depth information on diseases,
  conditions, and wellness.

  
  

• OMIM (Online Mendelian Inheritance in Man): A catalog of human genes
  and genetic disorders, offering detailed information about genetic
  conditions.

  
  

• Medical Literature: Our data curation process also involves reviewing
  medical literature and research studies to ensure that valuable
  information is incorporated.

  
  

Additional In-House Data Presentation

We’ve also improved the presentation of in-house data within the analysis dashboard. Users will now find the total number of samples, as well as the count of homozygotes, heterozygotes, and hemizygotes for the repeat with lower frequency within their in-house database. Hovering over the total number of samples value reveals the distribution of both repeat alleles within user’s in house repository.

In-house distribution of the repeats

Clicking on the total sample count opens a pop-up window displaying a table and histogram:

• Table: Offers additional information for the less frequent
  allele reported in the in-house data, such as samples where it has
  been identified, associated analyses, phenotypes, and more.

  
  

• Histogram: Visualizes the frequency of all repeats found in the
  reported gene.

  
  

Repeat counts distribution in TCF4 gene. Sample repeat alleles indicated in orange.

3.12.6 Annotating Variants

The variant table provides three annotation fields to the left of each variant row: Relevance, Pathogenic, and Notes. These fields can be defined for each candidate variant and are available for reporting purposes. Here is an overview of how these annotation fields work:

Clicking on Relevance opens a window where the user can categorize the relevance of the variant and include additional information to be rendered in the report. The window includes the following options:

• Inheritance Model: Select between recessive or dominant inheritance.

  
  

• Relevance: Choose from options like High (1), Medium (2), Low (3), or
  Not Relevant to indicate the relevance of the variant.

  
  

• Pathogenicity: Select the appropriate pathogenicity category such as
  Pathogenic, Likely Pathogenic, Uncertain Significance, Likely Benign,
  Benign, or Risk Factor.

  
  

• Validation: Specify whether the variant requires validation, has
  positive validation, or has negative validation.

  
  

• Notes: Add free-text notes to provide additional details or comments.

  
  

Phenotype and Evidence: This allows the user to select relevant disorders from OMIM (Online Mendelian Inheritance in Man) and associated evidence to be included in the report. By selecting the checkbox next to each entry, the information will be incorporated into the report.

• ClinVar: This section presents information from ClinVar about the
  variant, including a hyperlinked accession number, the type of the
  variant, the associated conditions, and the clinical significance of
  the variant.

  
  

• OMIM: This displays known conditions associated with the disease
  according to OMIM phenotypes. It provides the associated inheritance
  model and a hyperlink to relevant documentation.

  
  

• Evidence: This section shows diseases, gene summaries, publications,
  and related variant and analysis terms for the given gene.

  
  

• Diseases related to gene: An advanced search that identifies all
  publications present in databases such as OrphaNet, OMIM, UniProt,
  Decipher, and PubMed, containing conditions matching the entered
  phenotype and the given gene. Gene summaries are provided, with
  highlighted phenotypic terms if matched.

  
  

• Publications related to gene: This displays publications in ClinVar
  and Entrez that are related to the given gene.

  
  

• Variants related to gene: Shows all variants in ClinVar and OMIM that
  are present in the gene.

  
  

• Analysis terms related to gene: Displays terms that are frequently,
  moderately, or rarely related to the entered phenotype.

  
  

Once information has been entered in the Relevance interface, the fields will be populated in the variant table, providing relevant information for each variant.

3.12.7 Reporting Variants

Once the relevance and associated information has been set, user can review the selections across all of the genetic models using the ‘Selected Variants’ feature.

Geneyx Analysis includes a convenient Selected Variant view that consolidates all the selected variants from different genetic models in one place.

To access this feature, simply click on the open book icon located in the upper right corner. It will open the Selected Variant view, which pulls information from the Annotate Variant window.

Select Variant View

The Selected Variant view provides a comprehensive overview of all the variants that have been selected, allowing for easy navigation and analysis of the chosen variants across different genetic models. It streamlines the process of reviewing and studying selected variants, enhancing the efficiency of variant analysis and interpretation.

After selecting the clinically relevant variants and applying the relevance scores and related notes, the next step is to review the findings using the ‘Report Preview’ button located in the top right corner. There is also a Report Configuration option, which empowers users to tailor the report layout with case-specific modifications, ensuring a dynamic and personalized presentation. Importantly, alterations made within this feature are exclusive to the specific report, avoiding unintended changes in other reports implementing the template configuration.

Report icon displayed in upper right corner

By clicking the green Report Preview icon, a visual summary of all the findings, along with the general information, will be generated. In this view, users have the flexibility to choose which evidence sections should be included in the final report.

The Report Preview provides a mock report that gives an overview of the findings and allows for a comprehensive review before generating the final report. To generate the report, simply click the Save button. This will produce a complete report in PDF format, containing all the relevant information, supporting evidence, including publications, and more. Additionally, an Excel file will be generated, displaying the full variant tables that were analyzed.

The generated report in PDF format provides a comprehensive and organized presentation of the findings, making it easier to communicate and share the results with colleagues, healthcare professionals, or other stakeholders involved in the analysis process. If a customized report is required, please contact support@geneyx.com.

3.13 Batch VCF Upload (& Joint VCF Files)

In addition to the option of uploading individual samples, Geneyx Analysis provides users with the convenience of uploading multiple samples in batch using the VCF Uploader. The VCF Uploader utilizes an Excel spreadsheet with a list of samples and key fields, along with an executable file that is configured to the user’s account. To utilize this feature, users need their API ID and API Key, which can be obtained from your user profile or by contacting support@geneyx.com. Additionally, this feature is only supported for Windows OS, if you are using a MAC please contact support. Here are the steps to use the VCF Uploader:

1. Visit the following link to access the VCF Uploader:]
   https://github.com/geneyx/geneyx.analysis.api/tree/main/apps .
   Download both files provided at the link and unzip the downloaded
   file.

   
   

2. Open the “Tgex.VCFUploader.exe.config” file and modify it by adding
   your account credentials (API ID and API Key). If you don’t have
   this information, please reach out to support@geneyx.com for
   assistance.

   
   

3. Navigate to the “Resources” folder within the unzipped files. This
   folder contains an Excel document that needs to be updated with the
   files you want to import. Place this updated Excel file in the same
   directory where your VCF files are located.

   
   

4. Open the “Tgex.VCFUploader.exe” file.

   
   

5. In the bottom left corner of the application, click on “Select File”
   and choose the updated Excel file that contains the sample
   information.

   
   

6. Optionally, you can define a protocol that will be applied to all
   the samples in the batch. If you don’t need a protocol, you can
   leave this field blank.

   
   

7. Click on the “Import” button. This will initiate the upload process
   for all the samples listed in the Excel file to your Geneyx Analysis
   account.

   
   

VCF Uploader

Additionally, there is a video recording available, which can be downloaded here, https://github.com/geneyx/geneyx.analysis.api/tree/main/apps/VCF%20Uploader that demonstrates how to use this. By utilizing the VCF Uploader, users can streamline the process of uploading multiple samples in batch, saving time and effort.

3.14 APIs

The API feature in Geneyx Analysis provides the ability to automate sample workflows by integrating with Laboratory Information Management Systems (LIMS) and Electronic Health Record (EHR) systems. It allows for seamless data exchange between Geneyx and these systems, facilitating the transfer of patient metadata and relevant information. Users with API-enabled accounts can access their API credentials through the User Profile menu, displaying API ID, API Key and Copy and Regenerate options.

Swagger documentation can be found here https://analysis.geneyx.com/swagger/ui/index To enable API on your account please contact support@geneyx.com. By leveraging the Geneyx Analysis API feature and using the provided scripts and tools, you can streamline your workflows and automate the exchange of information between Geneyx and your LIMS or EHR systems.

3.15 Python Scripts/Command Line

Geneyx Analysis offers the capability to utilize Python scripts to perform various functions within the application. These scripts can be accessed from the Geneyx Analysis API repository on GitHub: https://github.com/geneyx/geneyx.analysis.api/tree/main/scripts. Let’s explore the available scripts:

**ga.config.yml**:

• This configuration file should be placed in the same directory as the
  other Python scripts. It serves as a reference for the account
  configuration and specifies the server URL, `apiUserId`, and
  `apiUserKey`. You can obtain the `apiUserId` and `apiUserKey` by
  contacting support@geneyx.com.

**ga_CreateCase.py**:

• This script allows users to create an analysis by utilizing existing
  VCF files in their account. The `CreateCase_Data.json` file, located
  in the templates directory, contains the data fields that can be used
  with this Python script. Descriptions of the available fields can be
  found in the following link:
  https://github.com/geneyx/geneyx.analysis.api/wiki/Create-Case.

  
  

• This command creates a case in the account using the information
  provided in the JSON file.

  
  

**ga_addClinicalRecord.py**:

• This Python script enables users to add a clinical record to an
  existing subject. The `clinicalRecord.json` file can be modified
  with the specific information and executed with the script. For the
  phenotypic codes, only “HP:” terms are accepted. Detailed field
  descriptions can be found here:
  https://github.com/geneyx/geneyx.analysis.api/wiki/AddClinicalRecord.

**ga_createPatient.py**:

• This Python script is used to create a new subject. The
  `patient.json` file contains the available fields, and you can find
  descriptions for these fields in the following link:
  https://github.com/geneyx/geneyx.analysis.api/wiki/CreatePatient.

**ga_uploadSample_json.py**:

• This Python script allows users to upload a VCF sample into a new or
  existing subject along with associated patient information. The
  `sample.json` file in the template directory should be modified with
  the relevant data. Field descriptions can be found here:
  https://github.com/geneyx/geneyx.analysis.api/wiki/Create-Sample.

  
  

• To run this script, the following fields need to be specified:
  `–snvVCF`, `–svVCF`, and `–cnvVCF`.

  
  

By leveraging these Python scripts, users can perform specific actions within Geneyx Analysis, such as creating cases, adding clinical records, creating patients, and uploading VCF samples. The provided links offer additional details on the required fields and their descriptions, enabling users to customize and execute the scripts effectively.

3.15.1 CNV/SV/Repeat Unification Script

Streamlining the consolidation of multiple VCFs for Copy Number Variations (CNVs), Structural Variations (SV), and repeats, a comprehensive video tutorial has been created. This tutorial guides users on efficiently merging these diverse files, enhancing the understanding of script usage for proper sample uploads. The scripts can be found here: https://github.com/geneyx/geneyx.analysis.api/tree/main/scripts/UnifyVcf. Tutorials can be found here: https://geneyxuk.com/?s=unification.

This script not only unifies the files but also modifies them when necessary, since each provider provides slightly different vcf files and the final file has to include specific fields for Geneyx application to be able to read it properly. Hence, it’s important to run the script that matches the pipeline with which the vcf files were created.

The options are:

DragenUnifyVcf.py – for DRAGEN pipeline ONTUnifyVcf.py – for Oxford Nanopore sequences and pipeline PacBioUnifyVcf.py – for PacBio sequences and pipeline

Running the Unifying scripts: usage:

DragenUnifyVcf.py [-h] -o OUTPUTPATH [-s SVPATH] [-c CNVPATH] [-r REPEATPATH]

ONTUnifyVcf.py [-h] -o OUTPUTPATH [-s SVPATH] [-c CNVPATH] [-r REPEATPATH]

PacBioUnifyVcf.py [-h] -o OUTPUTPATH [-s SVPATH] [-c CNVPATH] [-r FULLREPEATPATH] [-b REPEATLOCATIONSBEDFILEPATH]

-o The path to the unified vcf file, including its name. The script compresses the output file, so its name should end with “.vcf” and not “.gz” -s The path to the structural variants (sv) vcf. This file can be either gzipped or unzipped, but it must be a vcf file. -c The path to the Copy Number Variants (CNV) vcf. This file can be either gzipped or unzipped, but it must be a vcf file. -r The path to the tandem repeats variants vcf. This file can be either gzipped or unzipped, but it must be a vcf file. -b

Relevant only for PacBio. A bed file used to filter the repeats vcf file. If the PacBioUnifyVcf.py script is called without this parameter, the repeats vcf file (if given) will not be unified. When called with this parameter, the PacBioUnifyVcf.py script creates a filtered repeats vcf file and unifies it (rather than the full repeats file) with the other vcf files. This bed file can be downloaded from PacBio’s github: https://github.com/PacificBiosciences/trgt/tree/main/repeats (named pathogenic_repeats.hg38.bed or the like) Please notice that when running with this parameter you have to run on linux and have bedtools installed

3.15.2 Microarray to VCF Converter

For customers that have microarray data, such as those from Affymetrix, Geneyx provides a script to convert the file into a compatible format with Geneyx. The script is available here, https://github.com/geneyx/geneyx.analysis.api/tree/main/apps/microarray. The converted files can then be loaded into the CNV/SV genetic model. Users can also select this as a sequencing target during data upload.

3.15.3 DRAGEN TruSight Oncology Parser

For customers using DRAGEN TSO500 and running DRAGEN secondary analysis, this parser converts the CombinedVariantOutput.tsv to supported json format for incorporation of MSI, TMB and GSI valused https://github.com/geneyx/geneyx.analysis.api/tree/main/scripts/DragenTruSightOncology500

3.16 Pharmacogenomics (PGx)

Pharmacogenomics (PGx) is a rapidly advancing field within precision medicine, focusing on the relationship between genetic variations and drug metabolism. The adoption of PGx in clinical and diagnostic settings is gaining momentum as knowledgebases and genetic insights continue to improve. At Geneyx, we recognize the significance of this field and are committed to offering a comprehensive PGx workflow to our users.

The Geneyx PGx workflow provides thorough interpretations for 13 CPIC level A/B genes that influence the metabolism of 68 commonly prescribed drugs. This information is curated from multiple reliable annotation sources, including the Clinical Pharmacogenomic Implementation Consortium (CPIC), the Food and Drug Administration (FDA), and the Pharmacogenomics Knowledge Base (PharmGKB). Each reported gene is accompanied by useful hyperlinks to these databases, allowing easy access to additional information, all of which is automatically integrated into the report.

The Geneyx PGx report delivers detailed patient and drug information in a user-friendly and customizable format. Genes with low quality or missing SNPs can be conveniently excluded from the report. Moreover, final modifications to the report can be made using the report editor, and electronic signature sign-off ensures compliance and accountability. The report presents the gene name, genotype, and the impact of genetic variations on drug metabolism. Additionally, gene descriptions elucidate the gene’s function, its association with drug metabolism, and the effect of genetic changes on therapeutic efficacy.

By leveraging key databases and unique genetic profiles, the Geneyx Pharmacogenomics workflow equips users with powerful insights to mitigate potential adverse drug reactions and metabolic responses. If you are affiliated with a clinical or diagnostic company and are interested in providing PGx reports to your patients, we invite you to schedule a meeting with us to experience the Geneyx PGx workflow in real time.

The PGx protocol requires have a PGx license to use. To obtain a license, please contact support@geneyx.com.

3.16.1 Instruction for using PGx

The PGx protocol in Geneyx Analysis requires capturing SNVs that are typically located outside of coding regions and are homozygous reference alleles. To ensure accurate results, the protocol necessitates genotyping enrichment.

The PGx protocol is associated with an enrichment kit that contains PGx BED files for both the hg19 and hg38 reference genome builds. When starting from fastq files, please select the PGx enrichment during the fastq upload process. This enrichment kit captures all exonic regions, along with 50 base pairs into intronic regions, and merges them with the ClinVar bed file. Additionally, a PGx BED file is used for genotyping, enabling the capture of variants even if they are homozygous reference at a specific position. This is particularly important for capturing star alleles and facilitating subsequent reporting.

If you are starting from VCF files, please consider the following factors. Ensure that the secondary pipeline used to call the VCF files includes the regions defined in the PGx BED files. If these files are not available, please contact support@geneyx.com for assistance. Your development or IT team will need to implement the PGx BED files accordingly. Alternatively, you can consider calling a gvcf output. For guidance on using gvcf, please reach out to support@geneyx.com.

Once you have the enriched VCF ready, you can import it into the PGx protocol. Please note that this protocol is specific to PGx analysis and does not include other genetic models. The default filtering is already applied, eliminating the need for modifying the displayed variants. To view the star alleles, simply click on the star icon located in the upper right corner. This option will display all the genes that will be included in the report. If necessary, you can exclude genes that lack coverage, exhibit low quality, or have missing SNPs.

PGx Star Allele icon

After making the required modifications, you can generate the report. The report template has already been created but can be further customized to match your lab’s design and specific requirements. We encourage you to test this feature and reach out to us if you have any questions or concerns.

PGx Result Preview

We are committed to providing a comprehensive and user-friendly PGx workflow, and we appreciate your interest in our system. Should you require further assistance or have additional inquiries, please do not hesitate to contact us. We are here to support you.

3.17 Long-Read Sequencing

Long read sequencing, a powerful technology in genomics, offers several advantages over traditional short read sequencing by generating longer contiguous sequences of DNA. This ability to produce long reads, often spanning thousands to millions of base pairs, provides a more comprehensive view of complex regions of the genome. It enhances the detection of structural variants, repetitive sequences, and phased haplotypes, which are crucial for understanding genetic diversity and disease mechanisms. Long read sequencing technologies, such as those developed by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies, have become invaluable tools for researchers aiming to achieve a more complete and accurate assembly of genomes.

Geneyx, a leading provider of genomic data analysis solutions, offers robust support for the analysis of long read sequencing data. The platform is equipped with advanced algorithms and tools specifically designed to handle the unique characteristics of long read data. Geneyx facilitates the accurate detection and annotation of variants, including single nucleotide polymorphisms (SNPs), insertions, deletions, and complex structural variants. The platform’s intuitive interface and comprehensive analysis capabilities enable researchers to interpret long read sequencing data efficiently, providing insights into genetic disorders, personalized medicine, and evolutionary studies. By leveraging Geneyx’s powerful analysis tools, researchers can maximize the potential of long read sequencing to uncover novel genetic information and advance genomic research.

3.17.1 Import

Geneyx supports the analysis of long-read sequencing data starting from VCF files. The platform accommodates various file formats, including SNV, CNV, SV, and repeat files. To ensure proper merging and annotation, SV, CNV, and repeat files must be processed through a unification script, available [here](https://github.com/geneyx/geneyx.analysis.api/tree/main/scripts/UnifyVcf).

Once the data is in the correct format, it can be uploaded into the dedicated “Long-Read” protocols. To activate these protocols, please contact support@geneyx.com. The upload process allows for the import of SNV and associated files. If the unification script was used, the resulting file can be imported into either the CNV or SV model, ensuring comprehensive analysis and accurate annotation.

Long Read Protocols

After the files finish uploading, a new dialog will appear, allowing for the entry of key fields associated with the files. By default, this protocol utilizes a “Long Read Sequencing” enrichment kit. This kit defines the target capture regions based on all transcripts in the human genome, covering end-to-end, including all intronic regions and upstream and downstream regions. Integrated SMART filtering helps remove background noise specific to long-read samples. Detailed information on the SMART filtering process can be viewed here.

Import Dialog

The other key fields for import are BAM FILE URL and METHYLATION FILE URL. If these files are hosted on a cloud infrastructure, the links for these files can be placed in these fields. In some cases, the configuration of the cloud infrastructure is needed. Please see section 3.17.2, AWS Configuration, for an example. Alternatively, local IGV can be used, further details on configuration can be found in the section Desktop IGV Integration.

Once the information is entered, click next and enter in any associated phenotypes for the patient. This will help identify candidate variants through a prioritization tool.

3.17.2 Analyzing Long-Read Data

There are unique features available for analyzing long-read data in Geneyx Analysis. If the uploaded VCF files contain allele phasing, this information will be pulled into the Genetic and Genomic categories. Clicking the expansion icon of this column will display this information. Allele phasing is a process in genetics that involves determining which alleles at different loci belong to the same chromosome. This information helps to understand whether certain genetic variants are inherited together or independently. It is crucial for studying genetic linkage, haplotype analysis, and understanding the inheritance patterns of alleles within populations. Advances in sequencing technologies and computational methods have significantly improved our ability to accurately phase alleles, thereby enhancing our understanding of genetic variation and its implications in health and disease.

Allele Phasing in the Genomic and Genetic Data category

Another feature that enhances the analysis of long-read data is the capability to visualize overlapping SNV and CNV events. When CNV/SV files are imported alongside SNV files, Geneyx includes a “MATCHED CNV/SVs” column. This feature is particularly useful as overlapping events may suggest the presence of potential recessive compound heterozygous variants involving both CNVs and SNVs. Understanding these overlaps provides valuable insights into complex genetic interactions and their potential impact on phenotypic expression.

Matched CNV/SVs column highlighted in yellow

Haplotype visualization in the BAM files involves accessing a genetic variant’s specific location. By clicking on the variant, users can open Integrative Genomics Viewer (IGV), which displays detailed coverage and pileup information from the BAM file. Additionally, IGV allows users to examine the associated haplotypes, providing a comprehensive view of how genetic variations are distributed and potentially linked across the genome. This capability supports detailed genomic analysis, aiding in the identification and interpretation of haplotype structures and their implications in genetic research and diagnostics.

Haplotypes present in BAM files

Methylation visualization within Geneyx is facilitated by linking a specific URL during data import. This feature is particularly beneficial for researchers and clinicians alike. By associating samples with a case, users can compare regions that exhibit differential methylation patterns. This comparative analysis provides insights into epigenetic modifications across the genome, aiding in the exploration of potential biomarkers or regulatory mechanisms underlying various diseases or biological processes. The ability to visualize and analyze methylation data enhances the depth and scope of genomic investigations, offering valuable insights into gene expression regulation and its impact on health and disease.

Methylation Plots in IGV

Repeat expansions, analyzed from long-read data, provide crucial insights into genomic instability and disease mechanisms. This capability is detailed in section 3.12.4a of the Geneyx platform, focusing on Repeat Expansion Analysis. By leveraging long-read sequencing technologies, researchers can accurately characterize and quantify repetitive DNA sequences that are challenging to resolve with traditional short-read methods. This analysis is particularly relevant in studying disorders associated with unstable repeat expansions, such as Huntington’s disease and various types of ataxias. Understanding the dynamics of repeat expansions at a molecular level enhances our comprehension of genetic diseases, paving the way for improved diagnostic accuracy and potential therapeutic interventions.

3.19 Providing BAM & methylation tracks for samples not processed by Geneyx

There are cases that the customer executed the secondary pipeline locally and started working with it from VCF. In those cases, if the customer wants to visualize the BAM/Methylation tracks, then what needs to be done

3.19.1 General

First of all, all BAM/Methylation files must be bgzipped with the corresponding index file (tbi) in the following naming conventions:

• Methylation: xxx.bed.gz and xxx.bed.gz.tbi

  
  

• BAM: xxx.bam and xxx.bam.bai

  
  

3.19.2 File accessibility

Geneyx is using the JavaScript version of IGV; hence, the files must be accessible via the user’s web browser. If all users are connected to the same LAN, then having local LAN access to files is enough.

Where to store the files?

There are several options for where and how to provide access to the files

3.19.2.1 Option 1: Local Server

When files are stored locally and need to be accessed only locally (from the same LAN), this approach can be used:

1. Files are installed locally on the file system of the local LAN

   
   

2. Create a web server that serves those files over HTTP or HTTPS –
   that web server can be a local server with a domain name or just an
   IP address

   1. The server CORS settings will allow the Geneyx domain to access
      those files

3. The URLs set to geneyx are the local server’s file URLs

   1. In case the server is over HTTP only, it requires allowing
      untrusted content at each client browser accessing those files

3.19.2.2 Option2: Cloud

The files could be stored at AWS S3 or Azure blob storage.

Those files could be directly or indirectly served from storage, depending on the security and IT setup of the current organization

Containers and Buckets are public

This is the easiest solution, in this case

1. Set CORS permission from the Geneyx domain

   
   

2. Set direct URLs (over https)

   
   

Buckets are public, but are allowed access from a specific IP address or IP range

In this case:

1. Set bucket policy

{

“Version”: “2012-10-17”,

“Statement”: [

{

“Sid”: “AllowAccessFromSpecificIP”,

“Effect”: “Allow”,

“Principal”: “*”,

“Action”: “s3:GetObject”,

“Resource”: “arn:aws:s3:::YOUR_BUCKET_NAME/*”,

“Condition”: {

“IpAddress”: {

“aws:SourceIp”: “YOUR_IP_ADDRESS”

}

}

}

]

}

 

2. Set CORS permission from the Geneyx domain

   
   

3. Set direct URLs (over https)

   
   

CORS definition at AWS S3 bucket

[

{

“AllowedHeaders”: [

“*”

],

“AllowedMethods”: [

“GET”,

“POST”

],

“AllowedOrigins”: [

“https://analysis.geneyx.com”,

“*”

],

“ExposeHeaders”: []

}

]

Containers and Buckets are Private – Use Access Tokens

This scenario requires a web server that acts as a proxy. It gets the object request and redirects to the target storage URL by adding a time-limited access token (service of AWS or AZURE) for a specific IAM user.

1. Create a web server that serves those files over HTTPS – that web
   server can be a local server with a domain name or just an IP
   address

   1. The server CORS settings may allow the Geneyx domain to access
      those files

2. The URLs set to geneyx are the local server’s file URLs

   
   

 

Buckets are private: CDN (CloudFront with Secure Headers Policy) + AWS WAF

This setup ensures that your S3 contents remain private, are accessed securely via CloudFront, and only allow access from specified IPs with AWS WAF protection.

Step 1: Ensure AWS S3 Buckets Are Private

1. Navigate to the AWS S3 Console.

   
   

2. Select the S3 bucket you want to secure.

   
   

3. Under the Permissions tab, confirm the following:

   • Block Public Access settings should be enabled.

     
     

   • No Bucket Policy allows public access.

     
     

   • No Access Control List (ACLs) grants public read access.

     
     

4. Save the settings to enforce privacy.

   
   

Step 2: Configure CloudFront Distribution with Secure Headers

1. Open the AWS CloudFront Console.

   
   

2. Goto Policies → Response headers → Create response headers policy

   
   

3. Create the custom policy as show in images

   
   

4. Click on Create to create custom security headers policy.

   
   

5. Click Create Distribution.

   
   

6. In the Origin section:

   • Set Origin Domain Name to the S3 bucket.

7. Set Viewer Protocol Policy to HTTPS.

 

8. Under Behaviours:

   • Add Custom Headers in Origin Request Policy to send secure
     headers from the application.

     
     

   • Configure CORS in the response headers to allow your
     application’s domain.

     
     

9. Deploy the distribution.

Step 3: Create an AWS WAF IP Set Rule

1. Navigate to AWS WAF & Shield Console.

   
   

2. Click IP Sets and select Create IP Set.

   
   

3. Provide:

   • A descriptive name.

     
     

   • The allowed IP addresses.

     
     

   • Choose the region where CloudFront operates.

     
     

4. Click Create.

   
   

Step 4: Create AWS WAF Web ACL and Attach to CloudFront

1. In the AWS WAF Console, select the CloudFront to as region and
   create a new Web ACL.

   
   

2. Select CloudFront as the resource.

   
   

3. Click on Next and click on Create web acl.

   
   

4. Select the newly created ACL and go to the Rules tab. Click on
   Add rules and select the Add my own rules option with
   the following:

   
   

The rule will block any requests that do not match the IP addresses in the IP set.

 

APPENDICES

Appendix A: Auto Execute Filters

SNV

Field	Type	Notes
Chromosome	String
PositionStart	int
GT	string	HOM, HET, HEM
Allele	byte?	0,1
PhaseBlock	int?
REF	string
ALT	string
Depth	int?
FILTER	string	PASS
DP4_REF_P	int?
DP4_REF_M	int?
DP4_ALT_P	int?
DP4_ALT_M	int?
PL_HOM_REF	int?
PL_HET	int?
PL_HOM_ALT	int?
GQ	int?
AltPercent	double?
RelScore	int?
C1000GP1_AF	string
ESP6500_AA_AF	string
ESP6500_EA_AF	string
GERP_NR	string
GERP_RS	string
PHYLOP	string
LRT_PRED	string
LRT_SCORE	float?
MUTATIONTASTER_PRED	string
MUTATIONTASTER_SCORE	double?
SIFT_SCORE	string
UNIPROT_ACC	string
RsNumber	string
DBVER	string
DBGMAF	string
AN_Adj	int?
AC_Het	int?
AC_Hom	int?
AC_AF	double?
Hemi	int?
GNE_AN_AMR	int?
GNE_HET_AMR	int?
GNE_HOM_AMR	int?
GNE_AF_AMR	double?
GNE_HEMI_AMR	int?
GNE_AN_AFR	int?
GNE_HET_AFR	int?
GNE_HOM_AFR	int?
GNE_AF_AFR	double?
GNE_HEMI_AFR	int?
GNE_AN_ASJ	int?
GNE_HET_ASJ	int?
GNE_HOM_ASJ	int?
GNE_AF_ASJ	double?
GNE_HEMI_ASJ	int?
AN_EAS	int?
Het_EAS	int?
Hom_EAS	int?
EAS_AF	double?
GNE_HEMI_EAS	int?
GNE_AN_FIN	int?
GNE_HET_FIN	int?
GNE_HOM_FIN	int?
GNE_AF_FIN	double?
GNE_HEMI_FIN	int?
GNE_AN_NFE	int?
GNE_HET_NFE	int?
GNE_HOM_NFE	int?
GNE_AF_NFE	double?
GNE_HEMI_NFE	int?
GNE_AN_OTH	int?
GNE_HET_OTH	int?
GNE_HOM_OTH	int?
GNE_AF_OTH	double?
GNE_HEMI_OTH	int?
GNE_AN_SAS	int?
GNE_HET_SAS	int?
GNE_HOM_SAS	int?
GNE_AF_SAS	double?
GNE_HEMI_SAS	int?
GNE_AN_Ctrl	int?
GNE_HET_Ctrl	int?
GNE_HOM_Ctrl	int?
GNE_AF_Ctrl	double?
GNE_HEMI_Ctrl	int?
GNE_FLT	string
GNG_AN_Adj	int?
GNG_AC_Het	int?
GNG_AC_Hom	int?
GNG_AF	double?
GNG_HEMI	int?
GNG_AN_AMR	int?
GNG_HET_AMR	int?
GNG_HOM_AMR	int?
GNG_AF_AMR	double?
GNG_HEMI_AMR	int?
GNG_AN_AFR	int?
GNG_HET_AFR	int?
GNG_HOM_AFR	int?
GNG_AF_AFR	double?
GNG_HEMI_AFR	int?
GNG_AN_ASJ	int?
GNG_HET_ASJ	int?
GNG_HOM_ASJ	int?
GNG_AF_ASJ	double?
GNG_HEMI_ASJ	int?
GNG_AN_EAS	int?
GNG_HET_EAS	int?
GNG_HOM_EAS	int?
GNG_AF_EAS	double?
GNG_HEMI_EAS	int?
GNG_AN_FIN	int?
GNG_HET_FIN	int?
GNG_HOM_FIN	int?
GNG_AF_FIN	double?
GNG_HEMI_FIN	int?
GNG_AN_NFE	int?
GNG_HET_NFE	int?
GNG_HOM_NFE	int?
GNG_AF_NFE	double?
GNG_HEMI_NFE	int?
GNG_AN_OTH	int?
GNG_HET_OTH	int?
GNG_HOM_OTH	int?
GNG_AF_OTH	double?
GNG_HEMI_OTH	int?
GNG_AN_SAS	int?
GNG_HET_SAS	int?
GNG_HOM_SAS	int?
GNG_AF_SAS	double?
GNG_HEMI_SAS	int?
GNG_AN_Ctrl	int?
GNG_HET_Ctrl	int?
GNG_HOM_Ctrl	int?
GNG_AF_Ctrl	double?
GNG_HEMI_Ctrl	int?
GNG_FLT	string
Gene	string
AA	string
HGVSP	string
HGVSC	string
EFFECT	string
IMPACT	string
CODON	string
TRID	string
EXID	int?
NumExons	int?
MM_AF	double?
MM_Disease	string
MM_Locus	string
CV_ID	string
CV_Data	string
CVR_AF	double?
CS_ACC	string
ClinVar	string
ClinVarAcc	string
ClinVarInd	string
ClinVarAccInd	string
AdaScore	double?
RfScore	double?
AcmgCS	string
Rmsk	string
HGMD	string
HGMDPubID	string
INT_WES_AF	double?
INT_WGS_AF	double?
MMC	int?
MMID	string
VersionSetNumber	int
INT_DOMAIN	string
MetaSVM	double?
RevelScore	double?
TRIDEXT	string
GeneData	string
CADDR	double?
CADDP	double?
SAIS	double?
SAIF	string
ManeStatus	byte?
LV2_CNT	int?
LV2_ID	string
AM_Score	double?
AM_Class	string
GN4E_AN	int?
GN4E_Het	int?
GN4E_Hom	int?
GN4E_AF	double?
GN4E_Hemi	int?
GN4E_FLT	string
GN4G_AN	int?
GN4G_Het	int?
GN4G_Hom	int?
GN4G_AF	double?
GN4G_Hemi	int?
GN4G_FLT	string
CLINVAR_Rs	int?
COLOR_AF	double?
COLOR_AC	int?
COLOR_AN	int?
COLOR_Hom	int?
COLOR_Het	int?
COLOR_Hemi	int?
Max_AF	Double

SV

Field	Type	Notes
Chr	string
PosStart	int
PosEnd	int
Len	int?
CopyNumber	double?
Effect	string
PL	double?
HaploInsfIdxCount	int?
CnvScore	double?
GT	string
ChrEnd	string
REF	string
ALT	string
DGV_ID	string
DGV_AF	double?
DGV_AC	int?
DGV_AN	int?
DgvG_ID	string
DgvG_AF	double?
DgvG_AC	int?
DgvG_AN	int?
GD_AN	int?
GD_AF	double?
GD_N_HET	int?
GD_N_HOM	int?
GD_ID	string[]
GD_AC	int?
GD_N_HEM	int?
GD_FILTER	string
GD_CNV_AC	string
Repeat	string
RmskPercent	double?
RangeStart	int?
RangeEnd	int?
LookupKey	string
ClinVarAcc	string
ClinVar	string
Cyt_Regions	string[]
GNG_AF	double?
GNG_AC	int?
GNG_AN_Adj	int?
GNG_AC_Het	int?
GNG_AC_Hom	int?
GNG_HEMI	int?
GNG_AF_Ctrl	double?
GNG_AC_Ctrl	int?
GNG_AN_Ctrl	int?
GNG_HET_Ctrl	int?
GNG_HOM_Ctrl	int?
GNG_HEMI_Ctrl	int?
OMSV_Evidence	string
CG_HaploScore	int?
CG_HaploId	string
CG_TriploScore	int?
CG_TriploId	string
Allele	byte?
PhaseBlock	int?
DEPTH	int?
DP2_REF	int?
DP2_ALT	int?
CAMO	double?
DARK	double?
ROH_SCORE	double?
AltPercent	double?
AcmgCS	string
COLOR_AF	double?
COLOR_AC	int?
COLOR_AN	int?
COLOR_Het	int?
COLOR_Hom	int?
COLOR_Hemi	int?
Max_AF	double

Query format

the query is a Boolean expression where:

• each field need a “it.” prefix e.g. it.AltPercent

  
  

• equality is ==, not equal !=

  
  

• all other operators are >, <, >=, <=