The AI-powered Emedgene platform utilizes machine learning throughout the analysis and interpretation workflow to deliver the fastest time from genomic data to decisions. We apply machine learning models that retrieve evidence-backed answers and provide exceptional decision support.
Using automated interpretation algorithms, Emedgene generates an accurate shortlist of up to 10 potential causative variants. In a joint study of 180 solved cases with Baylor Genetics, 96% of cases were successfully solved by the algorithm. See Meng et al, Genetics in Medicine, 2023 publication for more details.
The platform is not a black box, and overlays a layer of explainable AI (XAI), presenting supporting evidence from the literature and databases which significantly reduces the time to interpret a case.
The algorithms use a proprietary Emedgene knowledge graph which incorporates information extracted from literature with Natural Language Processing, as well as from public databases and is updated on a monthly basis.
Dozens of additional algorithms are incorporated throughout the workflow.
Overall, the system combines AI in a highly optimized and customizable workbench, in order to automate the most time-intensive aspects of genomic analysis and research.
Getting around the platform
Emedgene applications menu
The Emedgene platform is divided into two applications:
Analyze—genomic analysis workbench
Curate—the knowledge management system
To switch from Analyze to Curate:
Go to the nine-dot app launcher icon located on the top navigation panel and select Curate from the dropdown menu.
To switch from Curate to Analyze:
Go to the nine-dot app launcher icon located on the Curate navigation panel and select Analyze from the dropdown menu.
Dashboard tab
The Dashboard tab depicts an overview of the user activity on the Emedgene platform and provides a glance at key performance indicators for an organization.
Lefthand panel
The Diagnostic Yield card shows the percentage of cases classified as Resolved out of the total number of cases of the same type.
The Status Diagram card shows the total number of cases submitted by the organization and the count of cases for each status.
The Stale Cases card highlights cases stalled at intermediate stages of analysis that haven't been finalized.
Righthand panel
The Network Activities panel displays a timeline of user activities within the organization. This log includes activity like creating a case, verifying a filter preset, changing a Case status, generating a report, and more.
Cases tab
The Cases tab provides an overview of genomic sequencing cases submitted by the organization, as well as individual case details.
The Cases tab includes:
Cases table—displays a list of cases along with key details
Permanently delete cases currently in the trash. Use with caution, as this action cannot be undone
Family tree
The Family tree tab includes the following information:
Pedigree diagram. Pedigree legend can be found here.
Sample details for each family member:
Phenotypes. For family members other than the test subject, phenotypes are categorized as:
Related—directly match one of the proband’s phenotypes
Unrelated—do not match any of the proband’s phenotypes
Medical Condition – Indicates whether the individual is considered Healthy or Affected in the case
Sex. Specified by the user
Age. Automatically calculated in years based on the provided date of birth
Maternal and Paternal ethnicity—ethnic background of the proband’s parents
BAM file location. Shown where relevant
How to open a case
To open a case:
A. Hover over the corresponding row in the Cases table and click on the Open case link next to the Case ID in the first column
B. Alternatively, double-click the row
How to label a case
You have the flexibility to manage case labels at any time: create, add, or remove them directly in the Cases table.
Adding labels to a case provides the ability to quickly mark cases for specific use cases and an easy filtering of cases subset in the cases page.
How to search for cases
You can use the Case search tab in the top bar to search for cases by the Case ID or Proband ID.
How to sort cases
You can sort cases by Creation date, Due date, or Quality.
To sort cases:
A. Hover over the column header and click the up or down arrow to sort in ascending or descending order
B. Alternatively, click the column name and select Sort ascending or Sort descending from the dropdown menu
The current sort direction is indicated by a single arrow icon next to the column name.
Only one column can be used for sorting at a time.
Okta identity management
The Emedgene platform utilizes the Okta Identity Management solution to control user access. This improves user management, enhances access and authentication security, and allows organizations to implement single sign-on for their users.
Managing data storage
Storage providers
Launching analysis
2. Family tree
Creating a family tree
Build a pedigree via the visual tool.
It is ideal that a proband selected for case analysis is affected and has disease phenotype(s).
You can add a Father, a Mother, a Sibling, or a Child to any family member, starting with the Proband. To do this, choose their icon, then click on the Add family member button in the bottom right corner of the pedigree builder to select a family member.
More information about the pedigree symbols can be found here.
To delete a family member, choose their icon, then click on the Delete Subject button in the top right corner of the Add patient information panel.
Note: There is no technical limit on the size or number of generations for a family tree.
3. Case info
Creating multiple cases
Tertiary analysis pipeline
Reviewing a case
Get started with Emedgene
Welcome to Emedgene, where we unlock genomic insights for hereditary disease and streamline your tertiary analysis workflows.
So you've signed in and can't wait to get started? Here we will guide you through the platform architecture, case creation, and results review. You can dive a bit deeper by following the links and exploring manuals for the platform's applications:
Analyze—Genomic analysis workbench, where you can accession, interpret, curate and report on your cases, while also efficiently managing the lab workflow
Curate—A repository for all of your organizational curated knowledge
Look around
The platform is operated from the .
By clicking on the corresponding buttons, you can enter:
tab
page
menu
dropdown menu
To enter the flow, click on the namesake button on the . Here:
1
Select file type
2
Upload files
3
Create a family tree
4
1
Select a case to review on the tab. You'll be directed to the that:
Showcases an AI-curated shortlist of variants suggested to be checked first, namely and
Provides numerous customizable to help you by yourself
Top navigation panel
The top navigation panel serves as a guide to the platform. It includes:
dropdown menu activated by clicking the username or profile picture
Case details
The Case details panel provides comprehensive information about a particular case.
The Case details panel is organized into three tabs:
Case info—displays technical, operational, and clinical information about the case
Family tree—shows a graphical pedigree and sample details for each family member
Activity—provides a timeline of all actions taken within the case for audit and collaboration
How to access the Case details panel
From the
Click on the row of the case you want to view. A pop-up side Case details panel will appear on the right. To close the panel, click the X icon in the top right corner.
From an
To expand the Case details panel, click the left-pointing arrow icon on the right edge of the screen. To collapse it, click the right-pointing arrow icon at the top left of the panel.
Case info
The Case info tab includes the following information:
Case ID—a unique identifier assigned to each case by Emedgene, formatted as EMGXXXXXXXXX
Case type—the type of analysis performed:
Activity
The Activity tab offers a timeline of case actions and enables users to leave comments. It supports key functions that enhance case management and review:
Traceability—Maintains a complete, time-stamped history of case actions
Error recovery—Allows users to identify and trace changes, such as variant edits or disease associations, made in error
How to filter cases
You can filter cases using most of the fields, as well as by the case outcome category (Resolved / Not resolved), which is the only filter not displayed as a column.
Category
Case filters
How to group cases
How to group cases
1
In the Cases table navigation panel, select Group by.
2
From the menu, select Status.
How to remove grouping
1
In the Cases table navigation panel, select Group by.
2
From the menu, select None.
How to delete cases
In order to prevent accidental data loss, deleting cases in Emedgene includes a staging step before permanent case deletion.
Once moved to trash, the case becomes inaccessible. This can be reversed by replacing Move to trash or Trash bin with a different status.
2
Empty trash folder (v37.0+)
Authorized users can permanently delete all items in the trash. To do this:
Click Empty trash on the .
Review the warning message showing the number of cases pending deletion.
Confirm to permanently delete all cases in the trash.
After deletion is confirmed:
All cases marked Move to trash are permanently removed
An activity entry is recorded
Email notifications are sent to users who have opted in
Help
Click on the icon in the top navigation panel to open the Help dropdown menu.
From there, you can access:
Help Center: Find feature guides, step-by-step instructions, and tips to help you get the most out of the platform.
Walkthroughs: View short interactive demos of workflows (in development).
Feature requests: Share your ideas and feedback.
What's new: Stay updated with the latest release notes.
About: View general information such as your organization name and platform version.
Manage data storages
To directly import files from your own storage, link it to an organization's storage in Emedgene.
Note: to manage data storage, you must have Manager and Multiple Storage .
How to link your storage to Emedgene:
1
Click on the user initials or profile picture at the rightmost corner of the top navigation panel and select Settings
2
Select the Management tab and proceed to Storage card that lists currently linked storages.
3
To add a new storage:
Click Add Storage
Choose a storage type from:
Azure Data Lake
4
Check the connection to confirm that the storage is successfully linked.
To do this, find the storage in the list and check the cloud icon status:
If it's green, the connection is set correctly
If it's red and strikethrough, something went wrong. Hover over the icon to see details
Click Manage on the right to the storage details.
Click Delete on the right to the storage details.
Manage S3 credentials
Whenever an organization is created, we automatically allocate bucket folders in AWS S3 cloud storage to it:
Path for upload
Folder intended to store input case files.
Authorized user has view and upload privileges.
Path for download
Manage ICA storage
1
Log in to your Illumina private domain via URL in the following format: . This opens the Connected Platform Home
2
In the left navigation panel: User > API keys
3
Family tree legend
While adding a new case, you will build a pedigree and annotate each of the samples with data required for analysis.
After the case has been created, the family tree is available in the Case details panel (righthand panel of the Cases page).
Family tree legend:
Icon fill color in other pedigree members indicates the presence or absence of the proband's phenotypes in a present sample (regardless of the potential presence of additional unrelated phenotypes).
Filled: The individual is affected by all of the proband's phenotypes.
Half-filled: The individual is affected by some of the proband's phenotypes.
Empty: The individual is not affected by any of the proband's phenotypes.
2. Icon color intensity denotes whether sample files have been uploaded for the particular individual.
Full color: The sample has files loaded in the case.
Faded color: No sample files are available.
3. Icon line type indicates whether the sample is considered or excluded during analysis (relevant to samples with uploaded files only)
Solid: The sample is included in the analysis.
Dashed: The sample is ignored by Inheritance filters and the AI Shortlist algorithm, but you still can explore its genotypes.
Adding a sample
You can choose one of the following options:
Existing sample: Pick one of the samples already loaded on the platform
Upload new sample: Upload files from your PC and enter sample name
Choose from storage: Choose files from your cloud storage and enter sample name
No sample: Postpone uploading files but proceed with case creation or skip uploading files for family members other than Proband
Adding patient info for the non-proband samples
Options: Male, Female, Unknown.
Indicates the family relationship of a subject to the Proband automatically inferred from the pedigree. Options: Father, Mother, Sibling, Child, Other.
Expected format: mm/dd/yyyy.
Mark the checkbox if you want to exclude the sample from the AI Shortlist analysis and Inheritance filters while preserving genotype data.
If a sample shares some phenotypes with the Proband, you can copy them by checking this box. Proband's phenotypes will appear in a newly created Related Phenotypes section. To remove any of the proband's phenotypes not observed in a current individual, click the ☒ button next to the HPO term in the Related Phenotypes section.
Secondary findings
While creating a new case, you can choose whether to include secondary findings for the proband. This option is available on the Family Tree screen → Create family tree panel → Show Secondary Findings.
Secondary findings are genetic variants that are not related to the primary indication for testing but may have important medical implications. These variants are automatically assigned the Incidental tag when they meet American College of Medical Genetics and Genomics (ACMG)-defined criteria for reportable secondary findings.
A variant is automatically tagged as a secondary finding if it meets all of the following criteria:
Classification:
Supported parental ethnicities
The ethnicities of the proband's mother and father can be specified during the process of UI or API case creation. Please refer to the following list of supported ethnicities.
Case type and region of interest
Select case type
Select the case type in order to define the proper analysis of your case.
Select region of interest
Users can utilize a custom region of interest (ROI) BED file to limit analysis results to variants within the designated regions. A ROI BED determines which genomic regions will be included in the variant analysis.
BED files that define custom kits can be added in the Organization settings under .
You can select any region of interest, regardless of the case type.
When selecting a Custom BED as you region of interest, you must select a specific BED file that is already configured in your organization.
Sequencing information
Select a coverage BED
A coverage BED file is used to calculate and determine quality control (QC) metrics for your case. This file defines the genomic regions that should meet coverage requirements during sequencing.
BED files defining custom kits can be added in Organization settings > .
Furthermore, the BED file chosen here is linked to a PON (Panel of Normals) file when starting from FASTQs and conducting CNV calling.
After selecting a coverage BED file, the available reference sequences for this kit will be displayed.
Specify sample preparation details
Specify details such as laboratory name, sequencing machine used, sequencing reagent kit, and expected coverage.
Select a gene list
Select a gene list
You can limit analysis to a gene list in the platform while creating a case. Choose between:
1. All genes
No limitation of the analysis.
2. Existing gene list
Select one of the previously added gene lists from a dropdown list.
3. Create a new gene list
Generate a new virtual panel: add a List title and then add all the gene symbols one by one (Selection mode) or in a batch (Batch mode).
A new gene list can be comprised from a combination of configured gene lists and/or individual genes.
A gene list can by configured to hold up to 10,000 genes.
A new gene list can be created by combining configured gene lists and/or individual genes. Each gene list can be configured to contain up to 10,000 genes.
Note: Please use the up-to-date gene symbols approved by the Hugo Gene Nomenclature Committee. When adding gene symbols in a Batch mode, those genes that do not comply with HGNC standards will be automatically excluded from the gene list. These genes will appear for 3 seconds in a black error box at the bottom of the screen.
Selection mode
For each gene please follow the steps described below: Enter a gene symbol in the search box in the right panel (Candidate Genes) and select a matching symbol from a dropdown menu.
After selecting batch mode, paste a list of comma-separated gene symbols in the search box in the right panel (Candidate Genes).
You can choose between two different modes of a gene list feature:
Selected by default.
AI Shortlist is limited to the selected gene panel, no variants in other genes are considered in the results. If this in silico panel is used for analysis of exome or genome data, the gene restriction may be lifted during manual analysis to "open-up" the entire exome or genome for analysis.
Analysis is performed for variants in all the genes. Variants in the targeted genes get upgraded scores during prioritization by the AI Shortlist algorithm.
Formatting DRAGEN MANTA VCFs for Emedgene
For DRAGEN versions earlier than 4.2, when ingesting a DRAGEN Manta VCF containing SVs of type INS, replace the following line in the VCF header:
with
Example:
Replace
with
Emedgene annotations and update frequency
Every case is annotated with the attached table of resources, including proprietary Illumina prediction scores PrimateAI-3D and SpliceAI. All annotations are versioned, and versions recorded in a Versions tab, and saved per case. Key variant significance and knowledge graph databases are updated monthly, so that the most up-to-date information is available during analysis.
Integrating variant annotations from multiple sources
The Emedgene pipeline prioritizes variant annotations based on the calling methodology rank order. The first appearance of a variant is annotated according to the following hierarchy:
The Add New Case flow does not validate that sample IDs are unique or that input files are uncorrupted. Please ensure sample IDs are unique and that input files are valid before creating the case.
A case won't run if Proband sample files are missing. However, sample files are not mandatory for the rest of the family members (although highly recommended).
When choosing an existing file path, the samples used may be cached from the original run. For a top-up flow please use a new file path.
When you are loading sample files from your PC or choosing them from the storage, and there is more than one file per sample, please ensure that all the necessary files are simultaneously selected in the upload pop-up. You may only select one file type per case (i.e. you may not select both a .vcf and a .bam at the same time).
Percentage of reads mapped to the reference sequence.
Blue bars represent each of these parameters per sample, while a vertical line represents a general metric across all the samples of the same case type in the account.
Autosomal call rate
The Autosomal call rate field displays percentage of loci on the array for which a genotype call was successfully made, that only includes autosomes.
A high call rate indicates a high-quality sample and successful genotyping. Low call rates can signify problems with the DNA sample (poor quality or quantity) or issues during the array processing.
Displayed to three decimal places.
Most Likely Candidates and Candidates
To streamline case review, the AI Shortlist pre-selects the list of variants likely to be causative for each case: Most Likely Candidates and Candidates.
Most Likely Candidates
Variants that are most promising for solving the case. This list is limited to 10 top-scored variants but may include more if more than one variant is tagged per gene (suggesting compound heterozygosity). We can change the Most Likely Candidates number limit upon request.
Candidates
Several dozen highly scored variants worth considering.
The ranking of variants by AI Shortlist considers:
SNVs
CNVs
SNV + CNV compound heterozygotes
SVs
mtDNA variants
STRs
The AI Shortlist rates variants based on predicted variant effects, alternative allele frequency, familial segregation pattern, phenotypic match, in silico predictions, and other relevant information from scientific papers and databases.
During the case review, you can untag variants selected by the AI Shortlist or manually tag ones not selected by the AI Shortlist.
Sequencing lab information section
Sequencing lab information section reports sequencing run technicalities as indicated during case creation:
Lab
Instrument
Reagents
Kit type
Expected coverage
Protocol
Sequencing error rate
Sequencing error rate refers to the frequency at which incorrect base calls are made during sequencing process.
Error rate is calculated as number of low quality variants / total variants.
Blue bars represent each of these parameters per sample, while a vertical line represents a general metric across all the samples of the same case type in the account.
Case quality section
The Case quality section summarizes the data quality of the case and highlights the results of validation checks:
Chromosome validation
Confirms that each chromosome with at least 100 SNVs in defined enrichment kit or coding regions includes at least one high-quality variant
gnomAD validation
Verifies that each chromosome with at least 100 SNVs in defined enrichment kit or coding regions includes at least one variant annotated with gnomAD
ClinVar validation
Ensures that each chromosome with at least 100 SNV variants in defined enrichment kit or coding regions includes at least one variant annotated with ClinVar
AI Shortlist validation
Checks that at least one variant is tagged by the AI Shortlist.
This validation is not applicable if the gene list contains fewer than 50 genes
If your workgroup uses a higher threshold, it is reflected in the Gene list threshold field
mtDNA reference validation
Confirms that the rCRS reference is used for mitochondrial DNA
H
"Hawaiian"
I
"Iberian"
"India - Bene Israel Jews"
"India - Cochin Jews"
"Indian"
"Indigenous Amazonian"
"Indigenous peoples in Canada"
"Indonesian"
"Inuit"
"Iranian"
"Iranian Persian Jews"
"Iraq"
"Iraqi Jews"
"Irish"
"Italian"
"Italian Americans"
"Italian Jews"
J
"Japanese"
"Japanese Brazilian"
"Jordan"
K
"Kenyan"
"Korean"
"Kurdish"
"Kurdish Jews"
L
"Latino/Hispanic Americans"
"Lebanese Jews"
"Levantine"
"Libyan"
"Libyan Jews"
Note: Variant types currently annotated and displayed in Emedgene are DEL, DUP and INS.
##source=DRAGEN <version>
##source=MANTA-DRAGEN <version>
##source=DRAGEN 05.121.645.4.0.3
##source=MANTA-DRAGEN 05.121.645.4.0.3
Azure Blob
AWS S3
File Transport Protocol (FTP)
Google Cloud
Secure File Transport Protocol (SFTP)
Illumina Basespace (BSSH)
Illumina Connected Analytics (ICA)
Fill in the required credentials
Click Add storage
How to edit storage information:
How to remove a link to storage:
If data is deleted or moved from the customer's storage, it might adversely affect the case. To learn more about possible consequences, check out this table:
A. Grant access to all workgroups across the domain
If your domain includes multiple workgroups and you want the API key to apply universally, select "All current and future Workgroups and roles (Global API Key)"
B. Grant access to specific workgroups
Select one or more workgroups from the list. For each selected workgroup, assign the following application roles:
Emedgene Has Access
Illumina Connected Analytics - Has Access
Platform-home Workgroup Admin
5
Click Generate.
Once the API key is generated, copy it to your clipboard or download it as a file.
⚠️ Important:
The API key is only accessible while the API Key Generated popup window is open.
After closing the window, the key cannot be retrieved. If you didn’t copy or download it, you’ll need to generate a new key.
1
Log into your Emedgene domain and go to the workgroup where you want to link ICA storage
2
Click on the user avatar and select Settings from the dropdown
3
Select the Management tab
4
In the Storage card, click Add Storage
5
Select Illumina Connected Analytics (not Illumina Connected Analytics V1!) from the Storage type dropdown
6
Fill the storage credentials:
"Api_key"—the API key before
"Project"—the name of the Project in ICA that contains and will contain the data you want to connect
Previously classified as pathogenic or likely pathogenic in ClinVar or Curate variant databases
Zygosity: Heterozygous or homozygous (only homozygous for the HFE gene)
Allele frequency: Less than 5%
Read depth: 10× or higher
Variant quality: Any value except LOW
Affected gene: Listed in the ACMG SF v3.2 or 3.3 medically actionable gene list for reporting secondary findings in clinical exome and genome sequencing (PMID: 37347242, 40568962)
In Emedgene, the terms incidental findings and secondary findings both refer to ACMG-defined secondary findings. The platform continues to use the “incidental” label in certain places for technical consistency, though the modern clinical standard is “secondary findings.”
Tagging criteria
ACMG SF v3.2 gene list
ACMG SF v3.3 (2025 release; requires pipeline v100.39.0+)
When Emedgene was first released, the term “incidental findings” was adopted in alignment with the clinical genomics standard at the time. The 2013 ACMG recommendations defined incidental findings as “the results of a deliberate search for pathogenic or likely pathogenic alterations in genes that are not apparently relevant to a diagnostic indication for which the sequencing test was ordered” (PMID: 23788249).
As the field evolved, the ACMG and broader clinical community began to distinguish between “incidental findings” (unexpected, not actively sought) and “secondary findings” (intentionally analyzed and reportable). This shift was reflected in the updated 2016 ACMG guidance (PMID: 27854360).
To reflect this change, Emedgene introduced the term “secondary findings” into the platform. However, “incidental findings” remains in use throughout the platform for technical consistency.
Tips:
Enable secondary findings when clinically relevant — this ensures variants in actionable genes are surfaced automatically.
Always review findings in the context of
Warnings:
Secondary findings are limited to the ACMG-defined gene lists. Variants outside these lists will not be tagged automatically.
Only variants with adequate sequencing depth and quality are tagged. Low-quality calls may require manual review.
This folder contains a partially annotated (excluding results of proprietary algorithms) VCF file per case.
Authorized user has view and download privileges.
Path for DRAGEN output
This folder contains DRAGEN output files.
Authorized user has view and download privileges.
To get access to your upload, download and DRAGEN output folders, you need to get a key pair consisting of an access key ID and a secret access key. Creating, deactivating, activating and deleting credentials is available for users with Manager and Manage S3 Credentials roles.
You can create and use up to two dynamic access keys at the same time.
When you require technical support, you have the option to generate a new key pair specifically for the troubleshooting process. After the issue has been resolved, you can delete the credentials to ensure security of your system.
The newly generated credentials will only be saved in AWS Identity and Access Management (IAM) and not in our database.
In Settings > Management > S3 Credentials, click on Create Access Key.
You can retrieve the secret access key only when you initially create the key pair. If you lose it, you have to create a new key pair. To immediately copy the secret access key to a secure location, use the Copy to clipboard button.
In Settings > Management > S3 Credentials, click on Deactivate in the corresponding key pair card.
In Settings > Management > S3 Credentials, click on Activate in the corresponding key pair card.
In Settings > Management > S3 Credentials, click on Delete in the corresponding key pair card. Only inactive key pairs can be deleted.
Real-time collaboration – Enables teams to monitor each other’s updates as they happen, ensuring transparency
Training & quality control – Helps identify patterns in variant interpretation and supports consistent application of evidence criteria
Audit compliance – Supports clinical and laboratory documentation standards (e.g., CAP/CLIA) by providing a verifiable action history
Each activity entry includes:
Timestamp (date + time)
User name of the person who performed the action
Action description
Activity logs are kept for at least six years for full traceability.
Category
Activities
Case-related
Case created
Case status changed
Case participants updated
Case labels modified
Report created
Case moved to trash
Case data edited, no reanalysis initiated
Case data edited and reanalysis launched
Comments
Comments left in the Activity tab
Variant tagging
Variant tag updated — this log entry includes a link to the relevant variant page for immediate review
Viewing activity logs
In the Cases table, the Activity tab within the Case details panel displays only comments and case-related activities. To view the full list of all activities, open the Case details panel directly from the individual case page.
The Activity tab logs the following actions:
Edits are permanent. Even if a change is undone, the original action remains recorded for traceability
Logs are case-specific. Activity entries do not reflect changes made in other cases or in the Curate database
Phenotypes not shared with a Proband. They can be added one by one (Selection mode) or in batch (Batch mode).
Please follow the steps described below for each phenotype:
Enter an HPO term (e.g., Hypoplasia of the ulna), an HPO ID (e.g., HP:0003022), or a descriptive phenotype name (e.g., Underdeveloped ulna) in the search box;
Select a matching term from a dropdown menu and press Complete after you've added all the terms.
Paste a list of comma-separated HPO terms or HPO IDs in the search box and press Complete.
1. Fill in the boxes:
Note: The fields marked with (*) are mandatory.
Note: Please omit the Patient ethnicities field for non-proband samples.
1. Sex (*)
2. Relationship
3. Date of Birth
4. Ignore Sample
5. Add Proband's phenotypes
Note: A popup notification will appear at the bottom of the page if any input HPO term or HPO ID is unknown.
6. Unrelated Phenotypes
Selection mode
Batch mode
2. Select Complete.
Whole Genome
Exome
Custom Panel
Array
Sample type—the format of the sample files used in the case:
Gene list—defines whether gene list was used during analysis and how it was applied:
All genes—AI Shortlist was neither confined to nor prioritized a specific gene list
Virtual panel (In silico panel)—AI Shortlist was limited to only the genes in the gene list
Boosted gene list—AI Shortlist analyzed variants in all genes, but variants in the gene list were given higher priority
Analysis type:
If field is not present—carrier analysis was not performed
Carrier—carrier analysis was performed for the selected gene list
Human reference—the genome reference used during case analysis
Ordered by—the user who created the case and the case creation date
Signed by—the user who finalized the case
Related cases—the Case IDs of other cases that share one or more samples with the selected case
Due Date—the user-defined deadline for finalizing the case. To enter or edit the Due Date, click the calendar icon in the Due Date section
Participants—Users involved in the case, whether in submission, analysis, finalization, or those subscribed to updates. To receive email notifications, click the Subscribe icon. To unsubscribe, hover over your avatar and click the X icon
Patient Information—basic demographic details:
Sex. Specified by the user
Age. Automatically calculated in years based on the provided date of birth
Clinical Information:
Proband phenotypes—HPO terms used to describe clinical findings in the proband
Suspected disease—if provided, includes the suspected condition, penetrance (%), and severity (mild, moderate, severe, or profound)
Maternal and Paternal —ethnic background of the proband’s parents
Clinical note—free-text notes provided at the time of launching the analysis
Additional case information can be added using custom fields, either via the API or by including extra columns in your CSV during batch case creation.
This allows you to extend the case details panel with project-specific data.
To enable this feature or learn more, please contact techsupport@illumina.com.
In the Fields menu, use the toggle switch next to each field name to show or hide columns based on your preferred view.
1
In the Cases table, click the column title you want to hide.
2
From the dropdown menu, select Hide column.
You can reorder columns in three ways: drag and drop the column, reorder columns via the Fields menu, or move a column using a dropdown menu.
1
Hover over the column title.
2
Click the six-dot icon () that appears to the left of the title.
3
Drag and drop the column.
1
Click Fields in the Cases table navigation panel.
2
In the Fields menu, hover over the field name.
3
Click the six-dot icon () that appears to the left of the title.
1
Click the column header.
2
From the dropdown menu, select Move left or Move right.
1
Hover over the left or right border of the column header cell.
2
When the resize cursor () appears, click and drag the border to your desired width.
Select a preset group
A preset group is a reusable set of filter presets applied for specific case types, as defined by your laboratory SOPs.
Select a preset group in the Case info screen during case creation or editing.
The group selected for the case determines which presets appear in the Presets tab of the Filtering panel.
If no group is selected, the system automatically applies the default preset group defined in Lab workflow settings.
To manage preset groups, go to Settings → Organization settings → . There, you can create and edit preset groups, hide or unhide them, download them, and set a default preset group for new cases.
Batch case upload via CLI
Prerequisites
Download and install node js platform via https://nodejs.org/en/download
Minimum version required: 16
Upgrade existing installation: nvm install --lts
Batch upload via CLI (Command Line Interface)
Download the batch case create script.
Replace my-domain with your Emedgene domain.
Illumina cloud: my-domain.emg.illumina.com
Legacy Emedgene cloud: my-domain.emedgene.com
Edit the downloaded batchCases.csv file. See for more details.
Execute the batch cases creator as java script using the command below.
Replace my-domain with your Emedgene domain and my-email with your user email.
A prompt for your Emedgene password will appear, enter the password and press Enter.
In case of validation errors in the input CSV, an output CSV called batchCases_results.csv will be created in the same location with detailed error results.
-l will create a log file in the same location.
More information can be found by running
Supported reference genome assemblies
Both GRCh37/hg19 and GRCh38/hg38 are supported. You can run cases with both reference genomes in the same organization.
Note: Curated and historical data are automatically lifted over on the fly.
Contains the sequences of the chromosomes, the rCRS mitochondrial sequence, unlocalized scaffolds, and unplaced scaffolds. Download .
GRCh37/hg19: hs37d5.fa.gz.
Includes data from GRCh37, the rCRS mitochondrial sequence, Human herpesvirus 4 type 1 and the concatenated decoy sequences. Download .
Annotations from organization databases
Annotations from organization databases appear in various parts of the platform, each showing certain details.
Historic and noise databases
Variant table
Allele frequency—in "[Organization DB] AF (%)" column
Allele count—in "[Organization DB] AF (#)" column
Variant page
Summary tabPopulation summary card
Allele count—in "Allele count" field
Hom/Hemi count—in "Hom/Hemi count" field
The last 10 samples—in "Last 10 samples" field
Population statistics tabOrganization DBs
Allele frequency—in "Allele frequency" column
Allele count—in "Allele count" column
Visualization tabPopulation data
"[Organization DB]" tracks display variants from organization databases. Left-click a variant in a track to review variant details:
Allele frequency
Allele count
Color-coded "[Organization DB]" badge based on pathogenicity in the Curated DB—"Known variants" column
Summary tabClinical significance card
Color-coded "[Organization DB]" badge based on pathogenicity in the Curated DB
Clinical significance tabClinical significance card
Color-coded "[Organization DB]" badge based on pathogenicity in the Curated DB
Joint calling in Emedgene
Classic joint calling consists of calling variants "simultaneously across all sample BAMs, generating a single call set for the entire cohort." (GATK.broadInstitute.org)
When running from BAM or FastQ samples on Emedgene, we do not apply a classic joint calling but a BAM look-up methodology.
This methodology consists of retrieving coverage information from BAM during the VCF merging process. Thus, if a variant does not exist in a parental sample, the algorithm will check the coverage in that position using data from the BAM file. The position will be considered as "REF" allele if it is covered (depth > 3), and "No coverage" or "N/A" (./. in the VCF FORMAT/GT field), if it is below that threshold or has no coverage.
This process involves the creation of a “genome coverage” file as a separate preliminary step. The coverage file could also be provided via a BED or a gVCF file.
BAM look-up approach is slightly different from classic joint calling used by the joint calling option in DRAGEN and other variant callers, and therefore will not produce identical results.
However, it is important to mention that Emedgene platform supports joint called VCF files, as well.
Remark: If a coverage file (ie. BED, BAM, gVCF) is not provided, then it is not possible to estimate the presence of REF allele in empty positions. As a consequence, "No_coverage" value will be assigned to those variants, which can affect the .
Limitation: It should be noted that the current data pipeline has a limitation stemming from the way it merges variants from different samples into the same case (e.g., in a trio). Since it is based on bcftools, variants are identified by the chromosome number, start position, reference allele, and alternate allele. However, it does not take into account the size of the variant itself. As a result, this may sometimes lead to inaccurate merging of CNV-type variants that differ in size. That limitation is not present when joint calling is used.
Individual case page
The user can enter a specific case from the Cases tab by clicking Full details in the corresponding row of the case table.
The individual case page includes:
Top bar—displays a Case ID and Case status and includes Case interpretation, Edit case info, and Report preview buttons
Candidates tab—highlights a shortlist of variants, suggested to be reviewed first - Most Likely Candidates and Candidates
—illustrates quality metrics for the sequenced samples
—provides an interactive overview of genomic structure, ideal for analyzing CNV and ROH/LOH events
—provides numerous customizable filters to help you explore the total list of genetic variants in compliance with your organization's standard case review process. You can export shortlisted variants in .xlsx format
—documents versions of all the resources used during case analysis
Individual case page: Top bar
The Top bar in the Individual case page indicates the Case ID and current Case status.
Specifies the QC BED kit used to evaluate coverage depth and breadth. If no kit is specified at analysis launch, NCBI RefSeqGene is used as the default reference
Custom gene coverage
Indicates whether the coverage of genes in the selected panel meets the expected threshold, as defined by the QC BED
Displays the results of relationship validation, confirming whether the submitted pedigree aligns with genetic data
Lab tab
The Lab tab shows sample and case-level quality metrics so you can check data reliability before starting interpretation.
The Lab tab includes:
Summary dashboard—highlights the key quality indicators, with more details provided in the subsequent sections
—displays the results of the relationship validation for each pair of samples in a family tree
—highlights regions that may not have been adequately sequenced
Manage Google Cloud storage
Go to the google cloud Console.
Navigate to IAM & Admin - In the left sidebar, go to IAM & Admin > Service Accounts.
Create a New Service Account: Click on the "Create Service Account" button at the top.
Manage BaseSpace storage
Log in to Emedgene and navigate to Settings in the upper right-hand corner of the page.
Click on the Management tab and then on Add Storage.
Choose Illumina BaseSpace storage type.
Fill Client Key, Client Secret and App Token as provided from BaseSpace (a description on how to get this information is provided below) and click Add storage to complete the setup.
Cases table
Cases table lists key details of all genomic sequencing cases submitted by the organization.
You can the table by hiding, showing, rearranging fields, or adjusting column widths, except for Case ID, which is fixed as the first column and always visible.
Manage Azure Blob storage
Before you proceed to this article, make sure you understand .
In > Management Tab, add or edit the required credentials: CLIENT_ID, CLIENT_SECRET, TENANT_ID, and ACCOUNT_URL.
See the table below to learn where to look for them in your Azure account.
Adding patient info for the proband
Options: Male, Female, Unknown.
The default fixed value for Proband is Test Subject.
Expected format: mm/dd/yyyy.
Options: Affected, Healthy.
The default value for Proband is Affected, but you may change it to Healthy.
To add all relevant phenotypes for the Proband, use one of the following methods:
1. Select sample type
When creating a new case, the first step is to select the sample input type. This determines how your data will be processed and which quality metrics will be available later in the analysis.
You can choose from the following supported formats: , , and .
Use this option if you want the platform to perform secondary analysis and variant calling.
Accepted file types:
.fastq.gz
Default region of interest kits
A region of interest (ROI) BED file determines which genomic regions are included in variant analysis. It functions as a preprocessing filter, determining which variants proceed to annotation and interpretation.
If no custom ROI BED kit is applied to a case, the system applies a default ROI BED file based on the case type. All default ROI BED files are available for download (see ).
Case type
Default region of interest BED
Batch case upload from platform
If you're comfortable with scripting and API usage, you can upload multiple cases at once using those methods. But if you're not a technical expert, don't worry. There is a user-friendly alternative available—importing a CSV file directly through the user interface.
Please follow the steps as described below.
CSV (Comma-Separated Values) is a simple file format used to store data in tabular form. A row represents a sample, and a column represents a data field.
Start by downloading a CSV template with an example line and mandatory and non-mandatory fields from the Add new case page set to Batch mode (see ). Fill the file with your data according to .
Click on the + New case button on the .
Reviewing the Candidates tab
To select variants with a particular tag, use the Filter candidates dropdown menu in the top right corner. You can select from Most Likely, Candidate, Incidental, Carrier, Not Reviewed, or any custom tags used in your organization.
For each variant on the Candidates tab, you can explore the suggested diagnosis, gene symbol, main variant details, and variant tag.
When a variant is found in a gene with no known association with a disease, the possible diagnosis cannot be indicated. Such variants are displayed under the Gene of Unknown Significance title.
All the relevant fitting a сompound heterozygous mode of inheritance are presented together. This refers to both confirmed and assumed compound heterozygosity (cases with at least one parent and singleton cases, respectively).
If you want to inspect the complete variant information, click on the variant bar to continue to the . You can visualize evidence in text or graphical format (Click on the interactive text in the top left corner: Show evidence as text or Show evidence graph to toggle between the two).
Processing multi-nucleotide variants
Unlike single-nucleotide variants (SNVs), a multi-nucleotide variant (MNV) represents a single event involving multiple consecutive bases. In Emedgene, small variants are recognized as those comprising an MNV if they are located within a 2-nucleotide distance.
Emedgene recognizes MNV as a distinct variant type and supports ingestion from VCF, annotation, and filtering.
Each MNV is represented and annotated as:
An MNV itself (eg, AG>TC)
Individual SNVs derived from the MNV (eg, A>T and G>C), for compatibility with existing tools and workflows
Contamination
The Contamination column reports whether a sample shows signs of DNA contamination, helping ensure data reliability before interpretation.
Contamination is detected using calculations, which estimate the proportion of reads that do not match the expected genotype. This estimate is based on the idr_baf score.
idr_baf stands for the interdecile range of the B-allele frequency—calculated as the difference between the 90th and 10th percentiles of the distribution of alt / (ref + alt) ratios across all variant sites.
A larger idr_baf value indicates greater variability in allele balance, which may suggest sample contamination, particularly from another human DNA sample.
Ploidy
The Ploidy column displays results from the DRAGEN Ploidy Estimator, which detects aneuploidies and infers the sex karyotype in whole genome cases.
Ploidy values are derived from the *.ploidy_estimation_metrics.csv DRAGEN output file.
Pass
All autosomes fall within the expected ploidy range. No large‑scale autosomal copy number deviation is detected.
NGS sex validation
The Sex validation column indicates whether the biological sex inferred from genomic data matches the sex information provided during case creation.
This helps identify potential sample mix-ups or metadata errors before interpretation begins.
Sex validation results:
Pass
Reported sex matches the estimated sex
Call rate
The Call rate field displays the percentage of loci on the array for which a genotype call was successfully made.
Call rate is one of the key metrics used to determine array sample , alongside .
A high call rate indicates a high-quality sample and successful genotyping. Low call rates can signify problems with the DNA sample (poor quality or quantity) or issues during the array processing.
Displayed to three decimal places.
Log R deviation
The Log R Deviation (or Log R Ratio standard deviation) quantifies the variability of the the signal intensity for each SNP marker on an array, ie, noise level.
Log R deviation is one of the key metrics used to determine array sample , alongside .
Lower values indicate more consistent signal intensities. A high Log R Deviation can indicate a poor-quality sample or potential issues with CNV calling.
Displayed to three decimal places.
Coverage
Coverage metrics for a target region defined by a QC BED file (or RefSeq coding regions if no kit is provided) included in the Sample quality section:
Average coverage
Average depth of coverage for a target region
% Bases with coverage >10x
percentage of a target region that is covered at a minimum depth of 10x
Array sample quality metrics
Array sample quality
The Quality status provides a quick assessment of array data reliability for each sample:
High
Call rate ≥ 0.99 and Log R dev ≤ 0.2
Low
If either condition is not met
Array sex validation
The Sex validation column indicates whether the biological sex inferred from genomic data matches the sex information provided during case creation.
This helps identify potential sample mix-ups or metadata errors before interpretation begins.
Sex validation results:
Pass
Reported sex matches the estimated sex
Fail
A mismatch was detected between reported and estimated sex.
% Bases with coverage >20x
percentage of a target region that is covered at a minimum depth of 20x
Blue bars represent each of these parameters per sample, while a vertical line represents a general metric across all the samples of the same case type in the account.
N/A
If the QC file not available
Use the Quality status to quickly screen whether a sample meets minimal QC thresholds before starting detailed interpretation.
Time zone awareness. Timestamps follow the system’s configured time zone, which may differ from your local time—especially in international collaborations.
Evidence notes
Evidence notes updated — this log entry includes a link to the relevant variant page for immediate review
Evidence pathogenicity
Variant pathogenicity updated — this log entry includes a link to the relevant variant page for immediate review
Evidence graph
Evidence graph updated — this log entry includes a link to the relevant variant page for immediate review
ACMG pathogenicity
ACMG evidence updated (logs any changes made via the ACMG classification wizard) — this log entry includes a link to the relevant variant page for immediate review
Transcript changes
Reference transcript updated
Important notes
Parental consanguinity—indicates whether the parents are related by blood
Report secondary findings—specifies whether secondary findings analysis was requested
"Path"—the folder within the project where the data is located. This can be used to restrict the user to only be able to access data within the specified folder. Using only “ / “ will allow all folders within your ICA project
Both the MNV and its underlying SNVs display the "Suspected MNP" badge in the Clinical significance tab.
During data processing, MNVs are split into consecutive SNVs. The resulting SNVs are annotated with INFO and FORMAT fields that mirror the original record.
Currently, Emedgene does not fully support MNV functionality. The following features are restricted:
Export to Curate: Blocked because Curate does not support MNVs.
AI Shortlist: MNVs are not included in the AI shortlist.
ACMG Classification: Disabled for MNVs.
From v100.39.0 onward:
Up to v38.0:
Fail
At least one autosome has a median ploidy score below 0.9 or above 1.1.
Hover over the result to see which chromosomes are problematic.
N/A
Ploidy metrics are not available.
Check ploidy early in case review to quickly identify potential large‑scale chromosomal abnormalities.
Verify sex karyotype by confirming whether the sex karyotype inferred from ploidy matches the sex validation results to rule out sample swaps.
A failed ploidy result does not confirm a clinical abnormality. It indicates aberrant copy number estimation and must be interpreted in the context of other QC metrics and genomic visualization.
Ploidy evaluation in the Lab tab is available only for whole genome FASTQ cases.
In whole genome FASTQ cases, ploidy is shown in both the DRAGEN QC report and the Lab tab. The DRAGEN pipeline automatically generates the *.ploidy_estimation_metrics.csv file, which the platform uses to display ploidy results. Under this workflow, ploidy appears consistently across the interface.
In whole genomeBring your own DRAGEN (BYOD) VCF cases, ploidy is shown only in the DRAGEN QC report, never in the Lab tab. When a case is created from VCF, the DRAGEN metrics supplied in a *.metrics.tar.gz archive are used exclusively to generate the DRAGEN report and are not ingested into the Lab tab. The platform displays N/A for Ploidy in the Lab tab.
Ploidy estimation results
Best practices
Ploidy availability by workflow
Fail
A mismatch was detected between reported and estimated sex.
N/A
QC file not available; validation could not be performed.
Sex validation is performed by comparing the observed homozygous/heterozygous genotype ratio on the X chromosome with the expected ratios:
<2 for females
>2 for males
Prerequisites:
Only high-quality SNVs from targeted regions—either kit-specific or RefSeq coding regions—are used for sex validation
A minimum of 50 variants is required to generate a reliable result. If this threshold is not met, sex validation cannot be performed, and no result is displayed
Emedgene uses the sex provided in the sample metadata to determine the expected copy number for sex chromosomes CNVs.
If the sample’s sex is marked as unknown, Emedgene defaults to Female for CNV calling.
If the predicted sex does not match the reported sex, we recommend updating it and re-analyzing the case.
If the sex was marked as unknown during case creation, the system will display the predicted sex instead of a validation status.
CNV calling on sex chromosomes
N/A
QC file not available; validation could not be performed.
If the sex was marked as unknown during case creation, the system will display the predicted sex instead of a validation status.
For DRAGEN versions earlier than 4.2, when ingesting a VCF containing STRs (either a DRAGEN STR VCF or a DRAGEN ExpansionHunter VCF), add the following line to the VCF header:
##source=ExpansionHunterV4.2
Additionally, ensure that the contig lines are present in the VCF headers. If they are missing, please use the following ones:
Service account name: Give your service account a name.
Service account ID: This will be automatically generated based on the name.
Description: Optionally, provide a description for the service account.
Click "Create and Continue".
example:
Assign Roles to the Service Account:
In the Grant this service account access to project step, you’ll assign the necessary roles.
Grant these role:
"storage object viewer" (read-only access)
Create the Service Account:
After assigning the roles, click "Done".
Generate and Download a Key:
Find your newly created service account, click the three dots on the right,
and select "Manage Keys".
Click Add Key > Create New Key and choose the JSON format.
Encode the key in base 64:
use python function: put this function and your json (here named json_file.json) in the same directory and run.\
save the output printed.
Add the above 3 values into the appropriate fields:
Client_credentials_base64: pasting the output of 8.
Bucket: the bucket name.
Path: for default, fill with /
else, put your path in the bucket. Seperate directories with /
Download and install the Google Cloud SDK from the Google Cloud SDK Install page. LINK
Select Your Platform (Windows, macOS, or Linux), download and run.
Initialize and Authenticate with Google Cloud: In the Cloud SDK Shell/terminal, run:
gcloud init
This will open a browser window to authenticate your Google account. Follow the instructions to log in and select your project.
Set CORS Configuration via gcloud:
Create a JSON file (cors.json) on your machine with the CORS rules.
Example\ it should look like:
notice:
origin: if using Illumina cloud:
https://host_name.emg.illumina.com
else, Emedgene cloud:
https://host_name.emedgene.com
Apply CORS Configuration to Your Bucket: run the next command.
gcloud storage buckets update gs://your-bucket-name --cors-file=cors.json
Verify the CORS Configuration:
gcloud storage buckets describe gs://your-bucket-name
Follow the instructions on the BaseSpace CLI Installation Page if needed. Be aware of the Basespace Regional Instance you are working on (us, euc1, aps2, euw2)
On BSSH, login to the workgroup you want to connect as the storage.
Example - connect integration1 workgroup as storage.
Once the BaseSpace CLI is installed, run the authentication command in the terminal.
The command will direct you to a link which requires to login.
After the authentication was completed successfully, find the access token in the config file.
The result should look like -
Populate the App_token with the accessToken value, and Server with the apiServer URL from the BSSH config file.
Client_key will be displayed in subsequent menus, so a descriptive name such as the workgroup name can be used.
Client_secret is unused when the App_token is available and can be set to "x".
Go to the BaseSpace developer portal and login. Be aware of the Basespace Regional Instance you are working on (us, euc1, aps2, euw2)
Go to My Apps and click Create a new Application.
Fill details for the application and click on create an application.
Fill details and press save.
You will need to fill all the fields that it requested, please add “NA” to them.
Go to My Apps and click on your new app. Then go to the credentials tab.
You will find the Client ID (Client Key), Client Secret and App Token to enter to Emedgene platform.
Log in into the desired Emedgene organization.
Go to Settings
Go to Management tab
Click on Add Storage
Select BaseSpace:
Add the information from your “Credentials” of the App previously created in BSSH.
# Linux
$ wget "https://launch.basespace.illumina.com/CLI/latest/amd64-linux/bs" -O $HOME/bin/bs
# Mac
$ wget "https://launch.basespace.illumina.com/CLI/latest/amd64-osx/bs" -O $HOME/bin/bs
# or
$ brew tap basespace/basespace && brew install bs-cli
# Windows
$ wget "https://launch.basespace.illumina.com/CLI/latest/amd64-windows/bs.exe" -O bs.exe
Case type: whole genome, exome, custom panel, or array.
Label
Custom.
Click the pencil icon to add a new label, select an existing one, or remove a label from the case.
Quality
Overall case quality: Passed, Failed, or Not available.
Hover over the icon for a brief summary, or view detailed results in the Lab tab.
Sortable (Passed > Failed > Not available).
Creation date
Date the analysis was initiated.
Sortable.
Due date
Customizable due date.
Click the calendar icon to set a date. To change it, click the existing date and select a new one. Remove the date by clicking the cross icon.
Sortable.
Field
Description
Participants
Users subscribed to case updates.
To receive email alerts for case updates, click the Subscribe icon. To unsubscribe, hover over your avatar and click the button.
Lab directors and other authorized roles can assign cases directly to analysts, making workload management easier.
User groups
defined in Settings; each group appears as its own column.
Case ID
A unique case identifier (EMGXXXXXXXXX).
This field is fixed and cannot be hidden or repositioned in the table.
Please provide this code to Tech Support when reporting any issues.
Value of the client_secret tuple (Value, Secret ID).
Format: #####-#######-######-######
(letters/digits/special chars)
TENANT_ID
ID of the tenant.
Format: ########-####-####-####-############
(letters/numbers)
ACCOUNT_NAME
An arbitrary name that the customer must supply to define the ACCOUNT_URL.
Format: string
CONTAINER_NAME
In Microsoft Entra ID, click on App registrations.
Select New registration.
Fill the name of the application & press "register."
You got to the registered app page: (CLIENT_ID / TENANT_ID) From this you can retrieve: Application ID and Tenant ID. Both are marked in the screenshot.
Press "Certificates & secrets"
Press on "New Client secret"
Fill the "Description" and change expires to 12 months. (or according to your organization policy), than press "Add"
8. Get the CLIENT_SECRET from this page.
Give this App registration roles and read access to the relevant Blob.
Go to Azure Storage accounts
Get into the relevant Storage account
Press on "containers"
Press on the relevant container
Press on "Properties"
Copy the ACCOUNT_URL
Errors for bad connections can be found in CloudWatch on particular FRY log stream
Please follow the steps described below for each phenotype:
Enter an HPO term (e.g., Hypoplasia of the ulna), an HPO ID (e.g., HP:0003022), or a descriptive phenotype name (e.g., Underdeveloped ulna) in the search box.
Select a matching term from a dropdown menu and press Complete after you've added all the terms and additional patient information below.
Paste a list of comma-separated HPO terms or HPO IDs in the search box and press Complete.
Enter the disease name in the search box, select a matching term from a dropdown menu and press Complete. All the associated phenotypes will be automatically added to the Proband Phenotypes.
Selecting a disease only fetches its associated phenotypes for convenience—it does not affect downstream analysis. You can edit this list to match the proband’s clinical presentation. Only the phenotypes you keep or add influence analysis, not the disease selection itself.
To remove any phenotype described for the disease but not observed in your patient, click the button next to the HPO term in the Proband Phenotypes list.
Enter the suspected disease penetrance as a percentage.
Select the appropriate category to indicate the severity of the disease symptoms observed in the patient: Mild, Moderate, Severe, Profound.
Mark the checkbox if applicable.
Paternal and Maternal. Enter the ethnicity name in the search box and select a matching term from a dropdown menu.
1. Fill in the boxes:
Note: The fields marked with (*) are mandatory.
1. Sex (*)
Handling a proband sample with unknown sex
When a sample is user-assigned "Unknown" sex, the system assumes "Female". This affects CNV interpretation on sex chromosomes in case the genetic sex is actually male:
Chromosome X:
CN = 2 is considered reference (REF) for a female genome, so CNVs with two copies are hidden by default. This may cause chromosome X duplications to be missed.
Chromosome Y:
CN = 0 is considered reference (REF) for a female genome, so CNVs with zero copies are hidden by default. This may cause chromosome Y deletions to be missed.
To include these variants in the analysis, enable the in Workbench & Pipeline Settings.
2. Relationship
3. Date of Birth
4. Medical Condition (*)
5. Proband Phenotypes (*)
Notes:
The maximum permissible number of proband phenotypes is 100.
Some diseases may not suggest phenotypes automatically if the source database does not provide them. You can add phenotypes manually in these cases.
When adding patient phenotypes, ensure that all selected HPO terms originate from the “Phenotypic abnormality (HP:0000118)” branch of the HPO ontology.
Terms outside this branch are not supported for case analysis, as they do not represent clinical phenotypes and may lead to incomplete or inaccurate downstream results.
Selection mode
Batch mode
Notes:
A popup notification will appear at the bottom of the page if any input HPO term or HPO ID is unknown.
Only phenotypes from the 'Phenotypic abnormality' HPO branch are currently supported.
6. Proband Suspected Disease Condition.
Note:
Searching for a disease name may return several entries with the same title.
This happens because the disease appears in multiple gene–disease sources, each with its own identifiers and evidence associations. These entries are not merged automatically, so choosing different items may return different sets of phenotypes.
7. Suspected Disease Penetrance
8. Suspected Disease Severity
9. Consanguinity
Note: If consanguinity is identified in the Proband's parents, but this box is not selected in case creation, this will result in a discrepancy alert in the .
10. Patient Ethnicities
2. Select Complete.
.fq.gz
.bam
.cram. Make sure you understand the current limitation for using CRAM files by expanding the section below.
Use when working with a joint VCF file containing multiple samples.
Accepted file types:
.pvcf
.vcf
.pvcf.gz
.vcf.gz
Use for cases where variants have already been called externally, or for cytogenetic array inputs.
This BED file includes every clinically relevant region. It includes:
“RefSeq Curated” and “GENCODE” regions with 50 bp flanking regions on each side of all exons, including coding exons and UTRs, for protein-coding genes
OMIM disease-related RNA genes (flanking 50 bp)
All ClinVar pathogenic variant regions (flanking 50 bp)
Promoter regions (EPDnew human version 006, flanking 50 bp)
Known STR regions (DRAGEN 4.0 specification file)
All microRNA genes (flanking 50 bp, based on HGNC)
Full mtDNA region
For consistency, the GRCh38 version includes the lifted-over regions from GRCh37 (using CrossMap for liftover).
CNV variants are not confined to regions of interest.
Files
Clinical Regions
CNV variants are not confined to regions of interest.
Files
Click on the Switch to batch button in the top right corner. You'll be directed to the Select file page of the Batch upload flow. Note: Here you can download a CSV template in the valid format.
Drag and drop a CSV file into the box or upload it from the file explorer. Wait for file upload and validation to finish.
After validation is complete, you will be directed to the Batch validation page. It features validation results details for you to review:
File name,
Number of rows in the file,
Number of cases to be created
Number of errors found,
Status message
If no errors were detected, a success message will be displayed
If any errors were detected, an error message will be displayed.
You will be given the option to download a file with error details to help you diagnose and correct any issues with the data. Once you've corrected the CSV file, reupload it.
Click on Create. A progress bar will appear on the right as the cases are created (Cases creation page).
If the cases have been created successfully, the Cases summary page will display the total number of cases that were created.
If there were any errors during the batch case creation process, the Cases summary page will display a table indicating the number of cases that were successfully created and the number of cases that failed.
You will have the option to download a CSV file containing two additional columns: Errors and Case ID. The Errors column will contain error messages for samples where case creation failed, while the Case ID column will contain the Case ID of a successfully created case for the lines where case creation was successful.
Caution: Please note that refreshing or leaving the page, exiting the Add new case tab, or power failure of your computer before you've completed a batch case upload will result in loss of the case creation progress.
When using the API or batch upload, note that applying multiple gene lists can inadvertently exceed a combined limit of 10,000 genes across panels. The platform may not provide an explicit error message in such cases. Plan gene-panel combinations carefully.
Be mindful that when contamination is suspected in sequencing data, it could stem from various sources, including true contamination, sample mix-up, library preparation issues, or technical artifacts.
Always confirm the issue with other quality checks.
Hover over the value to display a tooltip showing the HET ratio (proportion of sites that are heterozygous) and the HET count (number of heterozygote calls in sampled sites).
Tips:
Always review contamination results before starting interpretation to rule out technical issues that could explain unexpected variant calls.
Cross-check contamination results with other QC metrics (e.g., depth, ploidy, sex validation) for a more complete picture of sample quality.
Warnings:
Panels may be less reliable: For targeted panels, contamination estimates may be inaccurate due to the limited number of variants available for calculation. Use caution and cross-check with other QC metrics when interpreting these results.
Where can I manage preset groups for my organization?
Learn more
Lab workflow settings
Manage presets and preset groups.
Default preset group
Set the preset group applied automatically when no group is selected.
Presets tab
Use predefined combinations of filters that reflect your laboratory’s SOPs.
This guide provides a step-by-step process for creating a new case via the user interface. Detailed instructions for each step are available in the corresponding pages of the section.
Caution: Please note that refreshing or leaving the page, exiting the Add new case tab, or power failure of your computer before you've completed adding a new case will result in loss of the case creation progress.
Step 1: Start a new case
Click on the Add New Case button on the top navigation panel.
At the Select sample type page, choose the file type for your case analysis (FASTQ, gVCF, or VCF).
Click Next to proceed.
The page is divided into two panels: Create family tree (left) and Add patient information (right).
Use the visual tool to build the pedigree.
Add Clinical Notes (optional) in free text.
Select suspected Inheritance mode(s) (for record only; not used in the analysis).
For each family member:
(use a unique file path unless reusing samples).
Fill in a sample name (for VCF input, this must match the header in the file).
Complete the required patient details: for a and for .
Click Next to proceed to the Case info screen.
Here you define how the analysis will run:
: Choose Array, Custom Panel, Exome, Whole Genome, or Other.
For Exome cases, variants outside exons ±50 bp are automatically filtered.
Carrier Analysis: Optional checkbox. Requires a targeted gene list.
Additional case info (optional):
Indication for testing (free text).
(choose from predefined organization labels; these cannot be changed later).
At the Summary stage, confirm case type, gene list, and other selections.
After the case is created:
The Case ID is displayed.
You may add participants so colleagues receive notifications on status changes or updates.
Bring Your Own Bucket
If you have an Enterprise account and you would like Emedgene-managed DRAGEN solution to save the DRAGEN output files in your own bucket, reach out to techsupport@illumina.com.
Emedgene visualizes data in IGV directly from your AWS S3 bucket. In order to do it, you should enable CORS for the Emedgene application URLs.
Case Type
File Type
Expected effect
FASTQ
This feature is only related to saving Dragen output files in your own bucket when using Dragen through Emedgene (without ICA).
If you are looking to:
Import data from AWS S3 to Emedgene go to
Integrating any data storage to Emedgene go to
Download any data from Emedgene go to
Bring Your Own Bucket, also known as BYOK, enables you to control your DRAGEN file outputs.
Emedgene-managed DRAGEN solution saves the DRAGEN output files in a detected AWS S3 bucket that you have access to using your .
However, if you have an Enterprise account and you would like Emedgene-managed DRAGEN solution to save the DRAGEN output files in your own bucket, reach out to techsupport@illumina.com and follow this steps:
Emedgene requires access to the root folder, which means a dedicated bucket might be appropriated.
Bucket policy should allow Emedgene user access to the bucket.
Example bucket policy:
Emedgene directly from your AWS S3 bucket. In order to do it, you should enable for the Emedgene application URLs.
Example CORS policy:
We will require to run a case and validate the managed DRAGEN pipeline finish successfully and all features are available in the platform.
If a customer enables an AWS S3 Lifecycle policy in order to archive or change the S3 tiers for different files, they might create an adverse effect on the platform.
Case Type
File Type
Expected effect
Candidates tab
The Candidates tab displays all tagged variants, whether tagged by the AI Shortlist or manually by a user.
Variant tagging by the AI Shortlist
Variants are automatically tagged as:
Most Likely Candidates and Candidates
Variants prioritized by the AI Shortlist
Secondary findings
Variants that meet ACMG-defined criteria for secondary findings and automatically tagged with an Incidental tag (if enabled)
Carrier variants
Variants identified by the carrier analysis pipeline (if enabled)
During review in the Candidates tab, additional tags can be applied to a variant alongside the original automatic tag.
A set of the most promising variants based on scores calculated by the AI Shortlist. These variants are initially tagged by the system.
Variant types assessed:
SNVs and indels
CNVs
SVs
mtDNA variants
Secondary findings are variants that are automatically assigned the Incidental tag when they meet the criteria for secondary findings as defined by the American College of Medical Genetics and Genomics (ACMG).
Tagging is applied only when the Secondary findings checkbox is selected during case creation.
A variant is automatically tagged as an incidental (secondary) finding if it meets all of the following criteria:
Classification: Previously classified as pathogenic or likely pathogenic in ClinVar or Curate variant databases
Zygosity: Heterozygous or homozygous (only homozygous for the HFE gene)
Variants identified by the Carrier analysis pipeline. Carrier variants are automatically tagged only if you've selected the Carrier Analysis checkbox while creating a case. Analysis requirements and a list of targeted regions are specified by the organization's manager. This Carrier analysis flow is implemented by request.
Variants that were manually selected to be reported.
How to update a case status
You can update the case status either from the individual case page or from the Cases table.
Finalized case status can be applied only from the individual case page to prevent unintended case completion.
On the
1
Open the case page.
2
In the top bar, select the dropdown () icon next to the current case status.
3
Select the new status you want to apply.
1
Open the Casestab.
2
In the Cases table, locate the relevant case row and select the case status.
3
From the dropdown menu, select the new status.
DRAGEN QC report
The is generated by the Illumina DRAGEN Bio-IT Platform and covers the entire analysis workflow—from raw sequencing reads to variant calls.
DRAGEN QC report formats
Interactive HTML summary
A visual summary that includes interactive plots of key quality metrics.
When available, a DRAGEN report link appears below the sample name in the Sample quality section of the Lab tab.
Clicking the link opens the detailed quality control metrics report in a new browser tab. This integration allows users to assess sample quality directly from the Emedgene interface.
DRAGEN report link in the Sample quality section of the Lab tab.
CSV metric files
A set of detailed CSV files containing sample-level quality metrics. These files are downloadable and support in-depth review and documentation.
Learn more
Requirements by case type
This page explains the requirements for viewing the DRAGEN QC report for each case type.
NGS FASTQ cases
Run a FASTQ case in Emedgene.
Result: Because DRAGEN analysis is integrated into Emedgene secondary analysis pipeline, QC reports are automatically generated in the system.
Run DRAGEN analysis externally. This approach is referred to as "Bring your own DRAGEN (BYOD)".
2
Prepare the DRAGEN QC data. Use one of these options:
DRAGEN report HTML file (v100.40.0+): Download the DRAGEN report as a .report.html file per sample.
or
DRAGEN metrics TAR file: Download DRAGEN QC metrics files and prepare a TAR archive as described .
3
Upload the HTML file or TAR archive together with the sample VCF file.
4
Run the case.
Result:
If you uploaded an HTML file: The system directly visualizes the uploaded HTML file instead of generating the report from metrics files.
If you uploaded a TAR file: The system generates an interactive HTML report from metrics files.
Array cases start from VCF input files.
Prerequisites:
and later
Emedgene v100.39.0 and later
or
1
Run DRAGEN analysis externally. This approach is referred to as "Bring your own DRAGEN (BYOD)".
2
Prepare the DRAGEN QC data. Use one of these options:
DRAGEN report HTML file (v100.40.0+): Download DRAGEN report as a .report.html file per sample.
or
Result:
If you uploaded an HTML file: The system directly visualizes the uploaded HTML file instead of generating the report from metrics files.
If you uploaded metrics files: The system generates an interactive HTML report from metrics files.
Variant effect and severity calculation
Variant effect
For each variant that is mapped to the reference genome, Emedgene uses Ensembl’s Variant Effect Predictor (VEP) and the RefSeq (NCBI) library of transcripts to calculate variant effect. VEP uses a set of consequence terms defined by the , including immediately recognizable terms like “missense_variant” and “frame_shift_variant” as well as some more esoteric ones like “non_coding_transcript_exon_variant”.
The full list of terms, along with detailed descriptions and severity impact categories can be found in the below.
Importantly, each variant has a "main_effect" and "main_gene" chosen based on the most prioritized transcript for this variant. Transcript prioritization depends on many different parameters and on different Emedgene pipeline versions as described .
Variant severity
Variant severity, also known as variant impact, is a subjective assessment of the severity of a variant consequence.
Case status
Case status reflects the current stage of case processing, either by the Emedgene platform or your team. Statuses enable case progress tracking and support a consistent, collaborative case review workflow.
Starting in v100.40.0, the Status column on the Cases page includes a for cases with the In progress, Re-Analysis, or Issue reported status.
You can view and the current case status in the Cases table and in the top bar of the individual case page.
Sample quality section
The Sample quality section in the Lab tab gives you a quick view of the reliability of sequencing or array data used in your case.
The metrics displayed in the Sample quality section and their underlying calculation vary depending on the case type (see below).
NGS case
For family cases, check that no contamination is flagged before relying on inheritance-based filters.
Do not use in isolation: A "Likely" or "Yes" result should not immediately be considered diagnostic — review case setup, sequencing quality, and sample handling first.
STRs
Read depth: 10× or higher
Variant quality: Any value but LOW
Affected gene: Listed in the ACMG SF v3.2 medically actionable gene list for reporting secondary findings in clinical exome and genome sequencing (PMID: 37347242)
*In Emedgene, the terms "incidental findings" and "secondary findings" both refer to secondary findings as defined by the ACMG, due to historical usage.
When Emedgene was first released, the term “incidental findings” was adopted in alignment with the clinical genomics standard at the time. The 2013 ACMG recommendations defined incidental findings as “the results of a deliberate search for pathogenic or likely pathogenic alterations in genes that are not apparently relevant to a diagnostic indication for which the sequencing test was ordered” (PMID: 23788249).
As the field evolved, the ACMG and broader clinical community began to distinguish between “incidental findings” (unexpected, not actively sought) and “secondary findings” (intentionally analyzed and reportable). This shift was reflected in the updated 2016 ACMG guidance (PMID: 27854360).
To reflect this change, Emedgene introduced the term “secondary findings” into the platform. However, “incidental findings” remains in use throughout the platform for technical consistency.
You may combine multiple gene lists into one, or add specific genes to an existing list during case creation. The merged list behaves like any other list in the platform.
Preset group: Select the Preset group appropriate for this case type.
If none is selected, the default Preset group is applied automatically (marked as default).
Consent: Confirm subject consent for extended sharing.
Step 2: Build the family tree and add patient information
The Add New Case flow does not validate that sample IDs are unique or that input files are uncorrupted. Please ensure sample IDs are unique and that input files are valid before creating the case.
If a QC metrics file (metrics.tar.gz) is uploaded from BSSH, it will not be processed.
Keep file names under 255 characters and avoid spaces or parentheses in file paths.
Always ensure sample IDs are unique to prevent case failure.
If using joint gVCF input, place the proband first for accurate insufficient region calculation.
The UI does not allow reusing the same gVCF file for multiple samples.
Some diseases may not suggest phenotypes automatically if the source database does not provide them. You can add phenotypes manually in these cases.
Step 3: Case info screen
Note: Combining/merging gene lists from the Add New Case UI is supported only via the UI — this is not available from the API or batch upload.
Caution: Clicking Next here will finalize case creation. After delivery, only the proband’s phenotypes can be edited without reanalysis.
Step 4: Done screen
Note: In Illumina Cloud environments, users may still appear as available participants even after being removed from an IAM workgroup. These users do not have access to Emedgene, and accidental adding them as participants to a case does not pose any security or access risk.
Combining gene lists at case creation is available via the UI only and cannot be performed through API/batch upload.
API/batch upload cannot add phenotypes for an unaffected parent.
JSON files cannot be uploaded via API/batch upload.
For large files (BAM/CRAM/FASTQ),
browser upload is not recommended
. Use
,
, or cloud-to-cloud transfer instead to avoid incomplete or truncated uploads.
Context
Emedgene uses a specific genome reference assembly file (for example, hg38-alt_masked.cnv.graph.hla.rna-10-r4.0-1.tar.gz) for each DRAGEN version + genome reference (GRCh38 or GRCh37) combination. Both DRAGEN version and genome reference are configured per organization in Workbench & Pipeline settings.
Key requirement
When using CRAM files as input (instead of BAM), the same genome reference assembly file must be used during:
The mapping/alignment stage (which produces the CRAM file)
The variant calling stage (Emedgene secondary pipeline)
A mismatch in reference genome assembly files prevents the system from decompressing the CRAM file, leading to case analysis failure.
Best practices
Confirm reference compatibility with your organization settings before launching a run
If you receive CRAM files from an external lab, verify the specific reference genome file used to generate them
If the reference is unknown or incompatible, convert CRAM → BAM and upload the BAM file instead
Download the key and store it securely, as it is used for authentication in your code or applications.
FASTQ
VCFs
Reanalysis will fail
FASTQ
CSV, etc
Reanalysis will fail
VCF
BAM/CRAM (visualizations)
Visualization will fail
VCF
VCF (input)
Reanalysis will fail
VCF
CSV, etc
Reanalysis will fail
(will be fixed)
FASTQ/BAM/CRAM (input)
Reanalysis will fail (will be fixed)
FASTQ
CRAM (Output)
Reanalysis will fail
FASTQ
VCFs
Reanalysis will fail
FASTQ
CSV, etc
Reanalysis will fail
VCF
BAM/CRAM (visualizations)
Visualization will fail
VCF
VCF (input)
Reanalysis will fail
VCF
CSV, etc
Reanalysis will fail (will be fixed)
FASTQ
FASTQ/BAM/CRAM (input)
Reanalysis will fail
(will be fixed)
FASTQ
CRAM (Output)
Reanalysis will fail
Bring your own bucket is only available for Enterprise level support accounts and require Illumina support for setup.
Bring Your Own Bucket
1. Create an AWS bucket
2. Edit Bucket policy
3. Allow illumina.com and emedgene.com for CORS
4. Test and validate the configuration with Illumina support
The BYOB solution means you managed your own data, meaning if you accidentally deleted or moved the data the integration with Emedgene might break. You are responsible for your DRP and data backup solutions.
import json
import base64
def encode_json_to_base64(json_file):
# Read JSON data from file
with open(json_file, 'r') as file:
json_data = json.load(file)
# Convert the JSON data to a string
json_str = json.dumps(json_data)
# Encode the string to bytes, then to Base64
json_bytes = json_str.encode('utf-8')
base64_bytes = base64.b64encode(json_bytes)
# Convert Base64 bytes back to a string
base64_str = base64_bytes.decode('utf-8')
# Print the Base64-encoded string
print(base64_str)
encode_json_to_base64('json_file.json')
Severity is usually categorized as modifier, low, moderate or high:
Modifier severity is used for non-coding variants or variants affecting non-coding genes, where predictions are difficult or there is no evidence of impact. Inter-genic and non-coding variants are classic examples.
Low severity is used for variants that are assumed to be mostly harmless or unlikely to change protein function. This includes synonymous variants.
Moderate severity is used for non-disruptive variants that might change protein effectiveness, such as missense variants and in-frame insertions/deletions.
High severity is used for variants that are assumed to have a disruptive impact on abundance protein, such as by causing protein truncation, loss of open reading-frame, and/or triggering nonsense mediated decay.
Most of the time, variant effect and variant severity on Emedgene are consistent with VEP. However, genomics is a field defined by exceptions. There are key factors, outlined below, the Emedgene genetic team believes are critical to account for when assigning severity.
For small variants (SNV):
Splice prediction: Small variants will be upgraded to HIGH severity if its splicing prediction is high or MODERATE if its splicing prediction is moderate.
Conservation: Synonymous variants and splice region variants that are highly conserved will be upgraded to MODERATE.
Non-coding RNA disease genes: The severity of a small variant will be upgraded to MODERATE if the variant is within a list of RNA genes known to be associated with disease.
For CNV/SV:
VEP annotates CNVs with overlapping genomic features and designates them with the following effects: transcript amplification (DUP), feature elongation (DUP, INS), feature truncation (DEL), and transcript ablation (DEL). However, the severity assigned by VEP for CNVs does not reflect the complexity of CNV effects on protein function and in our experience is not suitable for genome analysis and filtering.
On Emedgene, variants are annotated in regards to its overlap with three different types of regions: ‘coding regions’, ‘clinical regions’, and ‘full gene’ region (see here for a more detailed description about the BED files used in the system).
The region annotation is then used to assess severity for CNV and SV as follow:
High
Moderate
Low
Modifier
Deletion (DEL)
Coding regions
Clinical Regions and not in Coding regions
Full gene and not in Clinical Regions
No overlap with any BED
Table 1: CNV/SV severity table. For each category of CNV/SV, the types of regions that overlap a given variant required to trigger the severity classification are shown.
For STR variants:
Emedgene is using an internal annotation for STR variants. More details can be provided by request to techsupport@illumina.com.
Known limitations
List of RNA genes known to be associated with disease is updated overtime as part of pipeline update.
Emedgene does not provide VEP annotation for non-coding regulatory data.
Emedgene provides out-of-the-box statuses, as well as the option to create custom statuses to match your case review workflow. To create, remove, or reorder case statuses for your organization, go to Settings > Management > Case statuses.
Each time a case status is updated, the change is logged and recorded in the case activity history.
Download sample-level DRAGEN QC metrics files from the Lab tab.
Bring Your Own Key
Scope
Bring Your Own Key (BYOK) is a security feature that allows organizations to use their own encryption keys to protect their data. This ensures that they maintain control over their encryption keys and, consequently, their data.
BYOK is only available for Enterprise-level support accounts.
BYOK setup
For versions earlier than v100.39.0, BYOK setup requires Illumina Support.
For versions v100.39.0 and later, you can complete the setup from .
Supported Key Management Services
Illumina integrates with leading Key Management Services (KMS), including Azure Key Vault and AWS KMS, so organizations can maintain full control over their encryption keys. These integrations combine Illumina’s Bring Your Own Key (BYOK) feature with your preferred KMS provider to deliver robust key management and enhanced data security.
Azure Key Vault
Azure Key Vault is a cloud service that provides a secure way to store and manage sensitive information like API keys, passwords, and certificates. It offers robust features for key management, including key generation, storage, and lifecycle management.
AWS KMS
AWS Key Management Service (KMS) allows you to create and control encryption keys used to encrypt your data across a wide range of AWS services and applications. It provides centralized management of encryption keys and integrates seamlessly with other AWS services.
Losing the encryption key means that all data encrypted with that key will be inaccessible. This can lead to permanent loss of access to crucial information.
It is crucial to securely store and manage your keys to prevent such risks.
Setup
Azure Key Vault Setup
The API server encrypts the organization's information before storing it in the database and decrypts it when needed (e.g., during pipeline execution). The key vault is managed by the organization.
To configure encryption in Emedgene, you need the following information from Azure Key Vault:
Application tokens:
Client Id
Tenant Id
Client Secret
The key information:
Key URL
1
Navigate to App registrations
2
Click Register to create a new application and and fill in the required details
3
After registration, copy and save the Application (Client) ID
1
In the left menu, select Certificates & Secrets
2
Click New client secret. Copy and save the Value (Client Secret) immediately, as it is shown only once.
1
Click New Key (Create key vault)
2
Specify the key vault name, region (for example, East US), and pricing tier
3
Click Next to go to Access Policies
1
Navigate to the newly created Key vault
2
In the left menu, select Keys, and then select the key
3
Select the current version
Description is coming soon.
The API server will encrypt the client's information before storing it in a database and decrypt that information when needed (e.g., running the pipeline). The key vault is managed by the client, and Emedgene will only be provided with access to encrypt/decrypt functions in that key vault. This guarantees that clients control access to the information.
Illustration of data flow when creating a case in Emedgene platform:
Illustration of data flow when reading a case data from emedgene platform:
A preliminary step to this solution is having a key vault owned by the client, and a key that Emedgene is given access to.
The client will create an access policy in the key vault of type “Application” and provide the matching key and secret to Emedgene. The access policy must contain permissions to perform encrypt and decrypt actions.
In order for Emedgene to integrate with the key, depending on the key vault provider, the client needs to provide the following information:
Client Id
Client Secret
Tenant Id
Key vault name
Since some of our platform search capabilities run directly on the DB, we can’t directly search any data that is encrypted. To overcome this, we will implement a hashing search functionality as follows.
The case data will still be fully encrypted in the DB as it is today
Specific fields we want to make “searchable” - as defined by the customer, we will save their hash value alongside the encrypted data.
Hashing will be done using SHA-256, and will include a secure random generated salt of 32 characters, which will be added to the value.
Illustration of data flow when searching in Emedgene platform:
Illustration of data flow when creating a case with searchable field in Emedgene platform:
Transcript prioritization logic
Each variant has a main_effect and main_gene chosen based on the most prioritized transcript for this variant. This selection influences how variants are displayed, interpreted and classified across the platform.
From v39.0+, Emedgene introduces improvements to Curate transcript prioritization and updates the RNA gene prioritization logic.
Transcript prioritization (v39.0+)
Emedgene uses VEP and EFF for transcript annotations and supports organization-defined canonical and preferred transcripts from Curate.
1
VEP transcripts are prioritized over EFF transcripts.
2
If the case is a Virtual Panel, prioritize transcripts from genes in the case gene list (not applied for Boosted Genes panel types).
3
Prioritize transcripts defined in Curate variants
Curate variant-level preferred transcripts now receive high priority.
Requires the new organization setting, enabled by Illumina Bioinformatics support.
4
Prioritize RNA genes associated with disease
(See Appendix 1: Updated RNA gene list)
This rule does not apply to upstream or downstream RNA variants.
RNA gene prioritization has been refined in v39.0.
5
De-prioritize readthrough biotype transcripts.
6
Prioritize intronic based on variant impact:
HIGH → MODERATE → LOW → MODIFIER
7
Prioritize intronic > UTR > upstream effects
(See Appendix 2 for MODIFIER effect prioritization)
8
Prioritize organization canonical transcripts
Defined in Curate
Always applied; no additional settings required
9
Prioritize canonical transcripts based on APPRIS.
10
Prioritize transcripts from genes in the case gene list.
11
Prioritize genes without a " — " in their symbol.
From version 39.0, Emedgene has changed how RNA genes are prioritized relative to protein-coding genes.
Prior to v39.0, if a variant overlapped an RNA gene from the prioritized list, the RNA transcript was often chosen as the main_gene, even when a protein-coding gene had a more impactful variant.
Starting with v39.0"
Protein-coding genes with stronger effects now take priority over RNA genes.
RNA genes are still considered, but no longer override coding transcripts with higher significance.
This results in more more clinically meaningful main_gene selection.
Here is a list of ordered rules for transcript prioritization:
1
VEP transcripts are prioritized over EFF transcripts.
2
If the case is a virtual panel, prioritize transcripts from genes in the case gene list (but not for Boosted Genes type panels).
3
Prioritize RNA genes associated with disease (See appendix 1 for prioritized list RNA genes). Importantly this does not apply to upstream and downstream RNA variants.
Here is a list of ordered rules for transcript prioritization:
1
VEP transcripts are prioritized over EFF transcripts.
2
If the case is a virtual panel, prioritize transcripts from genes in the case gene list (but not for Boosted Genes type panels).
3
Prioritize RNA genes associated with disease (See appendix 1 for prioritized list RNA genes). Importantly this does not apply to upstream and downstream RNA variants.
NGS sample quality
The overall sample quality indicator summarizes sequencing reliability for each sample. Sample quality is evaluated using different metrics, depending on the sample file type.
Overall FASTQ sample quality
The overall FASTQ sample quality reflects the confidence level that the sequencing data can support accurate variant calling.
For samples processed from FASTQ files, quality is determined based on the lowest result among these metrics:
Warning: If Curate preferred transcripts are enabled, transcript selection may differ from previous versions. This may change the displayed main gene/effect for some variants.
Tip: To ensure consistent results across teams, confirm whether Curate transcript prioritization is enabled for your organization.
If sex validation passed or is unavailable, the system proceeds to evaluate coverage metrics.
2
Evaluate coverage metrics
The system reviews the results for average coverage and the percentage of target bases with coverage ≥20× and evaluates them as low, moderate, or high using the thresholds in Table 1.
3
Assign overall quality
The system uses the lower result to assign the overall FASTQ sample quality.
Passed or N/A
30−45×
70−80%
High
Passed or N/A
>45×
>80%
If sex validation passed or is unavailable, the system proceeds to evaluate error rate.
2
Evaluate error rate
The system reviews the results for error rate and evaluates them as low, moderate, or high using the thresholds in Table 2.
3
Assign overall quality
The system uses the error rate result to assign the overall VCF sample quality.
Client->Emedgene API: Add New Test Request
note right of Emedgene API: Process Request
Emedgene API->Key Vault: PHI
note right of Key Vault: Encrypt
Key Vault->Emedgene API: Encrypted PHI
Emedgene API->Emedgene DB: Store Encrypted PHI
Client->Emedgene API: Get Test Request
emedgene DB->Emedgene API: Encrypted PHI
Emedgene API->Key Vault: Encrypted PHI
note right of Key Vault: Decrypt
Key Vault->Emedgene API: Decrypted PHI
Emedgene API->Client: Decrypted PHI
Client->Emedgene API: Add New Test Request
note right of Emedgene API: Process Request
Emedgene API->Key Vault: PHI
note right of Key Vault: Encrypt
Key Vault->Emedgene API: Encrypted PHI
Emedgene API-> Emedgene DB: Get Salt
Emedgene API-> Emedgene API: Hash Value using Salt
Emedgene API->Emedgene DB: Store Encrypted PHI + Hashed value
Client->Emedgene API: Search string
Emedgene API->AWS Secrets: Get Salt
Emedgene API-> Emedgene API: Hash string using Salt
Emedgene API->Emedgene DB: Search hashed string
Emedgene DB->Emedgene API: Search results
Emedgene API->Client: Search results
Indicator: Grey striped progress bar.
Meaning: The FASTQ‑based case is running DRAGEN secondary analysis.
Queued
Indicator: Empty segmented progress bar.
Meaning: The system has accepted the VCF (either a VCF case or the VCF output from integrated DRAGEN secondary analysis), but processing has not started yet.
Blue segments fill from left to right as the case moves through the five stages of the Emedgene tertiary analysis pipeline.
A filled blue segment indicates a completed stage
A half-filled blue segment indicates the stage in progress
Tooltip showing current stage, elapsed time, and estimated total processing time
Hover over the indicator to view:
The current tertiary analysis stage
Elapsed time since tertiary analysis started
Estimated total processing time (when available).
Normalization
Indicator: Segment 1 is half-filled.
Meaning: The system standardizes and prepares the uploaded data for downstream processing.
Annotation
Indicator: Segment 1 is filled and segment 2 is half-filled.
Meaning: Variants are enriched with genomic, clinical, population, transcript, phenotype, and knowledge base annotations.
Indexing
Indicator: Segments 1-2 are filled and segment 3 is half-filled.
Meaning: The annotated data is indexed to prepare the case for review within the platform.
Quality check
Indicator: Segments 1-3 are filled and segment 4 is half-filled.
Meaning: The system performs quality validation.
AI Shortlist
Indicator: Segments 1-4 are filled and segment 5 is half-filled.
Meaning:
The system performs AI-driven phenotype matching.
The AI Shortlist performs variant prioritization, resulting in the list of for the case.
After all five tertiary analysis stages complete, the case status changes to Delivered.
In case of processing failure, Issue reported status may be supplemented with the progress indicator.
Secondary analysis failure
Indicator: Red striped progress bar.
Meaning: Failure during DRAGEN secondary analysis.
Indicator: No progress indicator is shown.
Meaning: Input VCF file issues.
Tertiary analysis failure at Quality check stage
Indicator: Progress bar shows completed tertiary analysis stages in blue and the failed stage in red.
Meaning: Processing stopped during the stage corresponding to the segment filled in red.
Hover over the indicator to confirm the failed stage. Select the question-mark icon () to view error details.
In Emedgene, case status indicates the current stage of a case: from data upload through analysis, review, and results finalization.
Statuses are assigned either automatically by the system or by authorized users, depending on the workflow stage and user permissions. Different statuses require different IAM scopes/Emedgene roles for assignment and reassignment.
Case statuses by control type
System-controlled: Assigned automatically by the system; cannot be reassigned by users.
User-controlled: Assigned and reassigned by authorized users.
System-assigned, user-reassignable: Assigned automatically by the system but can be reassigned by authorized users.
Out-of-the-box: Default options provided by the platform.
Custom: to align with specific workflows.
Each status represents a distinct stage in the case lifecycle. Figure 1 shows the possible transitions between statuses and the control type for each assignment, indicated by solid and dashed arrows. Table 1 provides an overview of case statuses.
Indicates a custom case processing stage between "Delivered" and "Finalized".
User-controlled
Custom
"Finalized"
The analysis and review of the case by the analyst group have been .
Typically assignment and reassignment of the "Finalized" status is configured to be restricted to organization managers and/or lab directors.
User-controlled
Out-of-the-box
"Trash bin" (v38.0+) or
"Move to trash" (≤v37.0)
Case marked for ; access restricted.
Typically assignment and reassignment of the "Trash bin"/"Move to trash" status is configured to be restricted to organization managers and/or lab directors.
User-controlled
Out-of-the-box
"Pending sequencing"
Case created; awaiting sequencing data.
System-controlled.
Exception: user-reassignable to "Trash bin"
Out-of-the-box
"Issue reported"
The case failed to run.
Please check the integrity of the uploaded files and ensure that the variant caller used is on Emedgene list of accepted .
System-controlled.
Exception: user-reassignable to "Trash bin"
Out-of-the-box
"Re-Analysis"
The system is re-running the AI Shortlist algorithm.
System-controlled
Out-of-the-box
"Uploading"
Data upload in progress.
System-controlled
Out-of-the-box
"In progress"
Case statuses by origin
Case lifecycle
Learn more
Case status
Overview of case status behavior, history, and related workflows.
Case progress indicator (v100.40.0+)
Understand progress indicator states for cases in processing or failure states.
How to update a case status
Change the current case status from the case page or the Cases table.
Case status management
Create custom statuses and reorder them for your organization.
The following are the general format requirements for a CSV file used to create multiple cases:
The file must have a .csv extension.
The file must contain a [Data] header.
The row after [Data] header must include the field names identifying the data in each column. The column names are case-sensitive.
The row after the column name header and each subsequent row represents a sample.
Each column represents a data field.
It is essential that there are no empty rows between the [Data] header and the last sample row.
Number of cases per file can’t be greater than 50.
Must be present in the sample table at all times.
Case Type;
Family Id;
Phenotypes OR
If these fields are left empty, it will result in the creation of an empty sample.
BioSample Name;
Files Names;
Storage Provider Id;
This field is mandatory if Files Names is empty:
Sample Type.
This field is required if the auto option is used for Files Names (only relevant for BSSH):
Default Project.
The sample table may include these supported optional columns.
Assignee ID (v100.40.0+)
Boost Genes
Clinical Notes
The sample table may contain custom columns to suit your specific needs and include any relevant information that is important for your workflow.
Each custom field must be assigned a unique name without spaces. Data from custom columns is saved per case under the Additional information section of .
Field (column) name
Field details
Expected input
Example
(highlighted in red), (highlighted in orange), and fields should be filled in according to the following rules.
Field (column) name
Field details
Expected input
Example
For BSSH, it is necessary to use the actual names (numbers):
instead of aliases
In version 37, we introduced an enhancement to the batch upload process that allows you to provide a human-readable path in their batch CSV for BSSH files.
When a batch CSV includes a human-readable path, the system performs the following validations for paths in BSSH storage:
Single File in the Path:
If the provided path contains exactly one file or dataset, the batch upload proceeds successfully.
Two Files in the Path:
Multiple QCPassed Datasets:
If two datasets in the same path are marked as QCPassed, the batch upload will fail with a descriptive error indicating the conflict.
Excessive Files in the Path:
If more than two files are found for the provided path, the batch upload will fail, instructing the user to provide a more specific or valid path.
Enables customers to use intuitive, human-readable paths in their workflows.
Automatically handles dataset selection based on quality control status.
Supported variant callers
Emedgene provides the tightest integration with DRAGEN for germline variation analysis, providing accuracy, comprehensiveness, and efficiency, spanning variant calling through interpretation and report generation.
Compatibility with DRAGEN and DRAGEN Array Variant Callers
DRAGEN version
Emedgene case pipeline version*
Available callers
4.5
100.40.0+
DRAGEN Array version
Emedgene case pipeline version*
Available callers
*to modify the case pipeline version refer to.
The Emedgene platform supports a variety of variant callers and applies specific quality parameters for each. The quality assessment is an essential step in the Emedgene pipeline because variants with low quality will not be considered by the AI components.
If the variant caller is not supported or not recognized, a default quality function will be applied. The default parameters are built on GT (genotype), depth (DP) and allele bias (AB). These fields are mandatory, and their absence will induce “Low quality” for all variants.
The following variant callers are currently supported on the Emedgene pipeline, providing a header with the variant caller command line should be present within the VCF headers.
Additional callers can be supported on demand under license.
Variant caller / VCF
Supported versions
Notes
Calling methodology
Tertiary analysis of STR variants
Short tandem repeats (STRs) are genomic regions composed of repeated short DNA sequences. When STRs expand beyond the normal range, they can cause mutations known as repeat expansions, which may alter gene expression or function.
Expansion of these sequences in certain genes is the underlying mechanism for a group of inherited conditions called repeat expansion disorders. These disorders primarily affect the nervous and muscular systems, leading to diseases such as Fragile X syndrome, amyotrophic lateral sclerosis and Huntington’s disease.
The platform shows repeat counts per allele and highlights values that fall in normal, intermediate or pathogenic ranges (where known).
STR loci filtering and prioritization
STR variants outside the genomic regions specified in the DRAGEN v4.2 expanded variant catalog are excluded before tertiary analysis. This applies regardless of whether DRAGEN (internal or external) or another pipeline was used for the secondary analysis and irrespective of the DRAGEN version.
Variant calling reliability and availability of meaningful annotations for STR loci can vary. To reduce noise and ensure high-confidence calls, only a curated subset of STR loci is included in AI Shortlist analysis (Table 1). STR loci were curated by Emedgene based on strict criteria to ensure technical accuracy:
Validated genotype–phenotype associations and established pathogenicity. Loci must have well-documented links to disease and specific phenotypes. This avoids reporting variants with unclear implications.
Reliable calling performance. STR calling is technically challenging. Some loci are prone to false positives or inaccurate sizing. The subset includes loci that DRAGEN-STR can call consistently and accurately. This avoids reporting low-quality variants.
Expansion thresholds. Loci with defined pathogenic thresholds (e.g., number of repeats linked to disease) are prioritized. This avoids reporting variants that lack clear interpretation guidelines.
STRs that are not tagged by AI Shortlist are still included in the analysis. This means they will not be automatically prioritized or tagged as Most Likely or Candidate by the AI. However, these loci remain available for manual review and tagging.
Optional.
Indicates whether the will be used. TRUE means that variants in the targeted genes will receive upgraded scores during prioritization by the AI Shortlist algorithm.
Default value is FALSE.
Only considered for proband.
1. TRUE
2. FALSE
TRUE
Case Type
Mandatory.
Only considered for proband.
1. Whole Genome
2. Exome
3. Custom Panel
4. Array
5. Custom case type
Whole Genome
Clinical Notes
Optional
Free text
A 14-year-old boy with a visual acuity of 20/200 in both eyes in whom hearing loss was first noted at 5 years of age on routine screening; audiometry revealed sensorineural hearing loss.
Date Of Birth
Optional
YYYY-MM-DD
2013-01-22
Default Project
Conditionally mandatory.
Must be filled in if the auto option is used for Files Names (only relevant for BSSH).
Free text
GIAB
Due Date
Optional
YYYY-MM-DD
2023-05-03
Execute now
Optional.
Default value is TRUE. Use FALSE if you don’t want to run the case upon uploading the file.
Only considered for proband.
1. TRUE
2. FALSE
FALSE
Family Id
Mandatory
Free text
RM8392
Files Names
Conditionally mandatory.
An empty sample will be created if the field is left blank.
The existing option automatically locates FASTQ files based on the BioSample Name.
Note: If data files for an existing case were sourced from the customer’s external bucket and later removed, attempting to create a case from those files will result in an error.
Learn about the .
With the auto option, BSSH users can automatically locate FASTQ files based on the BioSample Name and Default Project provided.
When using BSSH without the auto option, ensure that your file path is .
1. Semicolon-separated list of paths to .fastq, .fastq.gz, .vcf, .vcf.gz, .bam, .cram, .gt_sample_summary.json, .annotated_cyto.json files without spaces
2. existing
3. auto (BSSH)
Optional.
Must be the id of a previously defined Gene List.
Only considered for proband.
Integer
12345
Kit Id
Optional.
<38.0: ID of a Region of interest BED.
38.0+: ID of a Coverage BED.
Must be the id of a previously defined kit.
Only considered for proband.
Integer
23456
Intersect Bed Id (v38.0+)
Optional.
ID of a Region of interest BED.
Must be the id of a previously defined kit.
Only considered for proband.
Integer
78957
Label Id
Optional.
Must be the id of a previously defined Case Label.
Only considered for proband.
Integer
34567
Opt In
Optional.
Indicates whether the case subject consented to the with your network(s).
Default value is TRUE.
1. TRUE
2. FALSE
FALSE
Phenotypes
Mandatory for proband sample if Phenotypes Id is empty.
List must be under 100.
It is possible to include non-HPO terms if Phenotypes Id is empty.
Semicolon-separated list of HPO phenotype terms
Unaffected is used for non-affected family members.
Abnormal pupillary function;Orthotopic os odontoideum;
Phenotypes Id
Mandatory for proband sample if Phenotypes is empty.
List must be under 100.
Semicolon-separated list of HPO phenotype IDs
HP:0007686;HP:0025375;
Relation
Optional.
Default value is proband.
Values proband, father, mother can be only used once per Family ID.
One sample with Relationproband is required per Family ID.
1. proband
2. mother
3. father
4. sibling
mother
Sample Type
Conditionally mandatory.
Required if Files Names is empty.
Only considered for proband.
1. FASTQ
2. VCF
FASTQ
Selected Preset
Optional.
Must be the name of a previously defined preset group. The specified preset group appears in the Presets tab for the case.
If set to Default, the default preset group is used.
If left empty, no preset is applied.
See an .
1. Free text
2. Default
Exome trio
Storage Provider Id
Conditionally mandatory.
Required if Files Names is not empty.
Must be from the configured storage provider ID list.
Integer
208
Visualization Files
Optional
Semicolon-separated list of paths to sequence alignment data files of extension .bam, .cram, .tn.bw, .baf.bw, .roh.bed, .lrr.bedgraph, .baf.bedgraph
/giab_project/NA24385.bam
If the path contains two files with the same name (for example, two pairs of fastqs in a dataset) , the system will:
Select the dataset marked as QCPassed.
Fail the batch upload if both datasets are marked as QCPassed, as this indicates conflicting data.
More Than Two Files in the Path:
If the path contains more than two files or datasets, the system fails the batch upload, as the path is considered ambiguous or invalid.
Institution
Custom
Free text
GenoMed Solutions
Sample_Received_Date
Assignee ID (v100.40.0+)
Optional.
Users subscribed to case updates.
Appears as Participants in the Case infotab and the Cases table.
Note: In cases with more than one sample, custom fields are only recognized and added to case information if their values appear within the same table row where the Relation field is equal to proband.
Custom field examples:
Batch case .csv file validation rules
Handling a proband sample with unknown sex
When a sample is user-assigned "Unknown" sex, the system assumes "Female". This affects CNV interpretation on sex chromosomes in case the genetic sex is actually male:
Chromosome X:
CN = 2 is considered reference (REF) for a female genome, so CNVs with two copies are hidden by default. This may cause chromosome X duplications to be missed.
Chromosome Y:
CN = 0 is considered reference (REF) for a female genome, so CNVs with zero copies are hidden by default. This may cause chromosome Y deletions to be missed.
To include these variants in the analysis, enable the Include Reference Homozygosity and No Coverage Calls toggle in Workbench & Pipeline Settings.
Terminology clarification: “Selected Preset” field
Despite its name, the Selected Preset field specifies a preset group used in the case, not an individual preset.
Extensive Compatibility with Additional Variant Callers
Internally, this list is referred to as the Emedgenizers list.
An Emedgenizer is a tool that normalizes VCF files to the system’s expected format for each variant caller.
