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.
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
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:
—Genomic analysis workbench, where you can accession, interpret, curate and report on your cases, while also efficiently managing the lab workflow
—A repository for all of your organizational curated knowledge
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.
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.
Sequencing error rate refers to the frequency at which incorrect base calls are made during sequencing process.
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.
Reviewing a case
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.
Array sample quality metrics
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
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:
##source=DRAGEN <version>
with
##source=MANTA-DRAGEN <version>
Example:
Replace
##source=DRAGEN 05.121.645.4.0.3
with
##source=MANTA-DRAGEN 05.121.645.4.0.3
Note: Variant types currently annotated and displayed in Emedgene are DEL, DUP and INS.
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 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.
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
—enables customization of the table view, including grouping and filtering of cases
—opens when a case is selected, providing additional information
Create a family tree
Add new case page > Family tree screen > Create family tree panel
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.
How to group cases
To organize cases by status, navigate to the Cases table, click on Group on the navigation panel, and select Status. To remove the grouping, select None.
Case status
The case status reflects the current stage of case processing, either by the Emedgene platform or a genomic analyst.
Case statuses help teams:
Monitor case progress
Track ownership
Maintain workflow consistency across the organization
You can view the current status of a case in the following locations:
Status column in the – for a quick overview across multiple cases
of the individual case page – for immediate visibility while reviewing a case
panel, under Case-related activities – to track status log
There are out-of-the-box statuses provided by the platform and the option to create custom statuses to suit your case review workflow. Go to Settings > Management > to create, remove, or reorder case statuses for your organization.
Cases table navigation panel
The Cases table navigation panel provides several tools to help you customize your table view and manage cases. It includes the following components:
Filters menu
Use this to narrow down the list of cases.
menu
Choose which columns are visible in the table and define their order
button
Permanently delete cases currently in the trash. Use with caution, as this action cannot be undone
How to delete cases
In order to prevent accidental data loss, deleting cases in Emedgene includes a staging step before permanent case deletion.
1
Move a case to trash
to Move to trash (≤v37.0) or Trash bin
Help
Click on the question mark icon of the top navigation panel to open the Help dropdown menu.
From there, you can access:
Help Center. Feeling curious? Dive right in.
Feature requests. Submit your ideas.
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
Individual case page
The user can enter a specific case from the by clicking Full details in the corresponding row of the case table.
The Individual case page includes:
—displays a Case ID and and includes Case interpretation, Edit case info, and Report preview buttons
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
Diagnostic Yield card presents the proportion of "solved" cases out of the total number of the organization's cases of the same type.
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.
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.
Case type and region of interest
Add new case page > Case info screen > Select case type
Select case type
Select the case type in order to define the proper analysis of your case.
NGS sample quality
The overall sample quality indicator provides a quick assessment of sequencing reliability for each sample.
Sample quality is evaluated using the following metrics:
Average depth of coverage
Mean coverage across the target regions
% bases covered >20x
Percentage of bases in the target regions covered at a depth greater than 20×, indicating reliable coverage
Labeling a case
You have the flexibility to manage Case labels at any time: create, add, or remove them directly in the .
Adding labels to a case provides the ability to quickly mark cases for specific use cases and an easy filtering of cases sub set in the cases page.
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:
—highlights the key quality indicators, with more details provided in the subsequent sections
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
Summary dashboard
Summary dashboard provides a quick overview of key quality indicators at both the case and sample levels.
Included metrics:
Displays the overall case quality status
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.
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.
Sequencing lab information section
Sequencing lab information section reports sequencing run technicalities as indicated during case creation:
Lab
Instrument
Reagents
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. This report can be from the Sample quality section of the Lab tab.
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
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.
What's New. Stay updated with the latest release notes.
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
If the sex was marked as unknown during case creation, the system will display the predicted sex instead of a validation status.
Status Diagram card displays the total number of the organization's submitted cases as well as the numbers of cases under each status.
Stale Cases card highlights the cases that are stuck at one of the intermediate stages of the analysis, and are not finalized.
Righthand panel
Network Activities panel displays a timeline of activities performed by multiple users within the organization. This log includes activity like creating a case, verifying a filter preset, changing a Case status, generating a report, and more.
Error rate
Sequencing error rate. Reflects general sequencing accuracy
% mapped reads
Proportion of reads successfully mapped to the reference genome
Contamination check
Detects mixed or low-quality samples that may affect interpretation
These metrics give an overall confidence level for whether the sequencing data can support accurate variant interpretation.
Fail
A mismatch was detected between reported and estimated sex.
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.
Only one column can be used for sorting at a time.
Kit type
Expected coverage
Protocol
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.
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
Pedigree status
Displays the results of relationship validation, confirming whether the submitted pedigree aligns with genetic data
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.
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
% 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.
Creating multiple cases
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.
CNV overall ploidy
The CNV overall ploidy field displays the ploidy value extracted from the CNV VCF header. If no CNV VCF file is provided, "N/A" is displayed.
Displayed to three decimal places.
The value is shown as is. The system does not validate or flag abnormal ploidy values. Interpret ploidy in context.
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
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.
Percentage of mapped reads
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.
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
(v38.0+).
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.
Warning: Once trash folder is emptied, this action cannot be undone. Review cases pending deletion before proceeding!
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
Candidates tab—highlights a shortlist of variants, suggested to be reviewed first - Most Likely Candidates and Candidates
Lab tab—illustrates quality metrics for the sequenced samples
Genome view—provides an interactive overview of genomic structure, ideal for analyzing CNV and ROH/LOH events
Analysis tools tab—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
Versions tab—documents versions of all the resources used during case analysis
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 Kit management.
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.
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.
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.
1. Prepare a CSV file
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 .
2. Upload a CSV file
Click on the + New case button on the .
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.
3. Review file validation results
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,
4. Create cases
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.
API/batch upload limitations
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.
Go to the Filters menu in the Cases table navigation panel
2
Under Field, select the field you want to filter by
3
Under List, select a value from the dropdown or manually enter one
How to remove a filter
1
Go to the Filters menu in the Cases table navigation panel
2
To remove a specific filter, click the X icon next to it
How to clear all filters
In the Cases table navigation panel, click the X icon next to the Filters menu
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:
Whole Genome
Exome
Custom Panel
Array
Sample type—the format of the sample files used in the case:
FASTQ: *.fastq.gz, *.fq.gz, *.bam, *.cram.
Project VCF: *.pvcf, *.vcf, *.vcf.gz, *.pvcf.gz
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
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
Patient Information—basic demographic details:
Sex. Specified by the user
Age. Automatically calculated in years based on the provided date of birth
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 [email protected].
Family tree legend
While adding a new case, you will build a pedigree and annotate each of the samples with data required for analysis (Add new case page > Family tree screen).
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):
2. Icon color intensity denotes whether sample files have been uploaded for the particular individual:
3. Icon line type indicates whether the sample is considered or excluded during analysis (relevant to samples with uploaded files only):
Preset group
You can implement different combinations of Presets to be used for different case types (i.e. Presets for exome may be different from Presets for genome) as defined by your SOPs to further streamline case review.
The combination of Presets is referred to as a Preset group.
Select a Preset group to display in the case
Preset group selection is available in the Case info screen of the Add new case flow while creating or a case.
Where can I manage Preset groups?
To manage filter Preset groups, navigate to > > :
From here, you can create ( /, , and Preset groups as needed.
Here, you can set a Preset group as default, so it will be used unless another Preset group is selected during .
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
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. , , and credentials is available for users with Manager and Manage S3 Credentials .
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.
How to create a key pair
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.
How to deactivate a key pair
In Settings > Management > S3 Credentials, click on Deactivate in the corresponding key pair card.
How to activate an inactive key pair
In Settings > Management > S3 Credentials, click on Activate in the corresponding key pair card.
How to delete a key pair
In Settings > Management > S3 Credentials, click on Delete in the corresponding key pair card. Only inactive key pairs can be deleted.
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.
Review interactive DRAGEN report
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 quickly assess sequencing quality and confidently interpret results—without leaving the Emedgene interface.
on the ICA project—either granted individually or via entire workgroup
How to get your ICA credentials:
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
Name the key
To connect ICA:
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
Add a sample
Add new case page > Family tree screen > Add patient information panel > Add sample section
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
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).
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.
Limitations
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:
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
Both the MNV and its underlying SNVs display the "Suspected MNP" badge in the .
Up to v38.0:
During data processing, MNVs are split into consecutive SNVs. The resulting SNVs are annotated with INFO and FORMAT fields that mirror the original record.
SNVs that comprise an MNV display the "Suspected MNP" badge in the .
Download DRAGEN QC metrics files
Sample-level DRAGEN QC metric files for all samples in a case can be downloaded by clicking the download icon next to the Sample quality section title.
For NGS cases, the report includes coverage and mapping statistics.
For array cases, metrics include array QC values such as call rate, autosomal call rate, and Log R dev.
How to customize Cases table view
How to select columns to be displayed
A. Show or hide columns via the Fields menu
1
Click Fields
2
In Fields menu, use the toggle switch next to each field name to show or hide columns based on your preferred view
B. Hide a column directly from the Cases table
1
In the Cases table, click the column title you want to hide
2
From the dropdown menu, select Hide column
How to change column order
You can reorder columns in three ways.
A. Drag and drop the column
1
Hover over the column title
2
Click the six-dot icon that appears on the left to the title
3
Drag and drop the column
B. Reorder columns via the Fields menu
1
Click Fields in Cases table navigation panel
2
In Fields menu, hover over the field name
3
Click the six-dot icon that appears on the left to the title
C. Move a column using a dropdown menu
1
Click the column header
2
From the dropdown menu, select Move left or Move right
How to adjust column width
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
In the top bar of the individual case page, click the dropdown icon next to the current case status
2
From the dropdown menu, select the new status you want to apply
B. In the
In the Cases table, click the current case status of the relevant case
From the dropdown menu, select the new status you want to apply
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.
Incidental (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.
In Emedgene, the terms incidental findings and secondary findings both refer to ACMG-defined secondary findings
Transcript prioritization logic
Emedgene uses VEP and EFF for transcript annotations and in upcoming versions will be adding Illumina Connected Annotations.
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.
Transcript prioritization v37.0+
Here is a list of ordered rules for transcript prioritization:
1
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:
TARGETED
STAR_ALLELE
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
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:
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.
Gene list
> Case info screen > Select genes list
Select gene list
You can limit analysis to a gene list in the platform while creating a case. Choose between:
Adding patient info for the non-proband samples
> Family tree screen > Add patient information panel > Patient info section
1. Fill in the boxes:
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 .
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.
Batch case upload via CLI
Prerequisites
Download and install node js platform via
Minimum version required: 16
Upgrade existing installation: nvm install --lts
Ploidy
The Ploidy column provides results from the DRAGEN Ploidy Estimator, which is designed to detect aneuploidies and determine the sex karyotype in whole genome cases.
Ploidy estimation results:
Pass
All autosomes fall within the expected ploidy range.
Fail
At least one autosome shows a median score outside the expected thresholds (below 0.9 or above 1.1).
Contamination
The Contamination column reports whether a sample shows signs of DNA contamination, helping ensure data reliability before interpretation.
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.
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
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
Individual case page: Top bar
The Top bar in the Individual case page indicates the Case ID and current .
Boosted gene list—AI Shortlist analyzed variants in all genes, but variants in the gene list were given higher priority
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
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 ethnicity—ethnic background of the proband’s parents
Parental consanguinity—indicates whether the parents are related by blood
Report secondary findings—specifies whether secondary findings analysis was requested
Clinical note—free-text notes provided at the time of launching the analysis
STR_REPEAT_EXPANSION
MRJD
FORCED_GENOTYPING
SMALL_VARIANT
CNV_READ_DEPTH
SV_SPLIT_END
UNKNOWN
Identical Variant Criteria
Variants are considered identical if they share the same:
Chromosome
Position
Reference allele (REF)
Alternate allele (ALT)
Limitation
When applied to Copy Number Variants (CNVs), this approach may merge variants even if they have different lengths.
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)
Assigning variant tags during review
During review in the Candidates tab, additional tags can be applied to a variant alongside the original automatic tag.
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.
Tagging criteria
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)
Allele frequency: Less than 5%
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.
Carrier
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.
When you hover over a failed result, the system displays which chromosomes are problematic.
N/A
Case type is not Whole genome
or
QC file not available; validation could not be performed.
The ploidy calculation uses values from the *.ploidy_estimation_metrics.csv file.
Tips:
Use ploidy checks early in case review to spot potential large-scale chromosomal abnormalities.
Always confirm whether the sex karyotype inferred from ploidy matches the sex validation results to rule out sample swaps.
Failed results do not confirm clinical abnormalities. They only indicate a deviation in copy number estimation and should be reviewed in context of other QC metrics and visualization.
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.
4
In the Storage card, click Add Storage
5
Select Illumina Connected Analytics (not Illumina Connected Analytics V1!) from the Storage type dropdown
"Project"—the name of the Project in ICA that contains and will contain the data you want to connect
"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
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.
Contamination check results:
N/A
No data is available (older cases or when idr_baf = 0.000).
No
No contamination detected (idr_baf < 0.200).
Unlikely
Possible contamination, but evidence is weak (0.200 ≤ idr_baf < 0.241).
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.
For family cases, check that no contamination is flagged before relying on inheritance-based filters.
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.
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.
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.
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.
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).
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
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.
* 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.
* Full color - the sample has files loaded in the case;
* Faded color - no sample files are available.
* 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.\
. The platform continues to use the “incidental” label in certain places for technical consistency, though the modern clinical standard is “secondary findings.”
Tagging criteria
A variant is automatically tagged as a 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)
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)
ACMG SF v3.3 (2025 release; requires pipeline v100.39.0+)
Includes all v3.2 genes plus newly added genes:
PLN
ABCD1
CYP27A1
This brings the total to 84 reportable genes.
Historical note
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 patient consent and your institution’s reporting policies.
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.
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.
Prioritize based on impact in the following order: HIGH > MODERATE > LOW > MODIFIER.
6
Prioritize introns over UTR over upstream (Appendix 2: MODIFIER effects prioritization).
7
Prioritize organization canonical transcripts (Defined in Curate. Always applied, no settings needed).
8
Prioritize canonical transcripts (Based on Appris).
9
Prioritize transcripts from genes in the case gene list.
10
Prioritize gene without “-” in their Name.
Transcript prioritization before v37.0
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.
4
De-prioritize transcripts.
5
Prioritize based on in the following order: HIGH > MODERATE > LOW > MODIFIER.
6
Prioritize introns over UTR over upstream (Appendix 2: MODIFIER effects prioritization).
7
Prioritize organization canonical transcripts (Defined in Curate, this parameter has to be implemented upon request).
8
Prioritize canonical transcripts (Based on ).
Appendixes
Appendix 1: List of RNA genes associated with disease
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.
The Activity tab logs the following actions:
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
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
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.
Important notes
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
Time zone awareness. Timestamps follow the system’s configured time zone, which may differ from your local time—especially in international collaborations.
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.
Batch mode
After selecting batch mode, paste a list of comma-separated gene symbols in the search box in the right panel (Candidate Genes).
Gene list modes
You can choose between two different modes of a gene list feature:
1. In silico panel
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.
2. Boosted genes
Analysis is performed for variants in all the genes. Variants in the targeted genes get upgraded scores during prioritization by the AI Shortlist algorithm.
Add new case page
Note:
The fields marked with (
*
) are mandatory.
Note: Please omit the Patient ethnicities field for non-proband samples.
1. Sex (*)
Options: Male, Female, Unknown.
2. Relationship
Indicates the family relationship of a subject to the Proband automatically inferred from the pedigree. Options: Father, Mother, Sibling, Child, Other.
3. Date of Birth
Expected format: mm/dd/yyyy.
4. Ignore Sample
Mark the checkbox if you want to exclude the sample from the AI Shortlist analysis and Inheritance filters while preserving genotype data.
5. Add Proband's phenotypes
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.
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
Phenotypes not shared with a Proband. They can be added one by one (Selection mode) or in batch (Batch mode).
Selection 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.
Batch mode
Paste a list of comma-separated HPO terms or HPO IDs in the search box and press Complete.
2. Click on Complete once all the information is added.
Add new case page
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
How to edit storage information:
Click Manage on the right to the storage details.
How to remove a link to storage:
Click Delete on the right to the storage details.
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:
Contains the sequences of the chromosomes, the rCRS mitochondrial sequence, unlocalized scaffolds, and unplaced scaffolds. Download here.
GRCh37/hg19: hs37d5.fa.gz.
Includes data from GRCh37, the rCRS mitochondrial sequence, Human herpesvirus 4 type 1 and the concatenated decoy sequences. Download here.
. 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).
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.
Via Command Line
Prerequisite
Install BaseSpace CLI (Command Line Interface)
Follow the instructions on the if needed. Be aware of the Basespace Regional Instance you are working on (us, euc1, aps2, euw2)
Authenticate
On BSSH, login to the workgroup you want to connect as the 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".
Via BaseSpace Developer Portal
Go to the BaseSpace and login. Be aware of the Basespace Regional Instance you are working on (us, , , )
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.
Adding BSSH account to your Emedgene account
Log in into the desired Emedgene organization.
Go to Settings
Go to Management tab
Click on Add Storage
Add the information from your “Credentials” of the App previously created in BSSH.
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: FASTQ, Project VCF, and VCF.
FASTQ
Use this option if you want the platform to perform secondary analysis and variant calling.
Accepted file types:
.fastq.gz
.fq.gz
.bam
Current limitation: CRAM input and reference compatibility
Context
Emedgene uses a specific (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.
Project VCF
Use when working with a joint VCF file containing multiple samples.
Accepted file types:
.pvcf
.vcf
.pvcf.gz
Make sure the proband sample is listed first to ensure correct downstream calculations.
VCF
Use for cases where variants have already been called externally, or for cytogenetic array inputs.
Accepted file types:
.vcf
.vcf.gz
.targeted.json
Array results can be visualized in Genome View and the IGV tab, and sample-level quality metrics are available under the Lab tab.
Tips:
Choose the input type carefully — it cannot be changed after the case is created.
Warning:
If files are incomplete or corrupted, the case may still be created but will fail during processing. Double-check your files before uploading.
Cases table
Cases table lists key details of all genomic sequencing cases submitted by the organization.
You can customize 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.
Cases table fields
Field
Description
Sample quality section
Sample quality metrics
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:
NGS case
Array case
DRAGEN QC
For , users can review the results of : interactive DRAGEN QC report and DRAGEN QC metric files.
DRAGEN QC for array samples is available from version 100.39.0 onwards.
Pedigree section
The Pedigree section displays relatedness metrics and the results of relationship validation for each pair of samples in the family tree.
Included metrics
Relatedness coefficient (observed)
Shows the observed coefficient of relatedness between sample pairs. The expected coefficient is available via hover tooltip for quick comparison.
IBS0
Identity by state 0—a number of genomic loci where two individuals share zero alleles. This occurs when the two individuals are opposite homozygotes for a biallelic SNP.
This metric is calculated across a set of biallelic SNPs and is inversely related to the degree of genetic similarity between the individuals. A low IBS0 count suggests a higher degree of overall genetic similarity, but it is an indirect and limited measure of genetic relatedness that requires interpretation alongside other metrics.
Relationship validation result
Summarizes the outcome of the relationship validation, confirming whether the observed data aligns with the expected pedigree structure.
Relationship validation calculation
Relationship validation is done by based on:
Relatedness coefficient (𝑟)—a measure of how much two individuals share alleles from a common ancestor, indicating the probability that alleles at the same genome location are identical by descent
IBS0 (Identity by state 0)—a number of genomic loci where two individuals share zero alleles, ie, they are opposite homozygotes
IBS2 (Identity by state 2)—a number of genomic loci where two individuals share two alleles, meaning they have the exact same genotype
Peddy takes the inferred relationships from the genetic data and cross-references them against the declared relationships. For every pair of individuals in a cohort, Peddy calculates a coefficient of relatedness from the genotypes observed at the sampled sites.
For each possible pair of samples in a pedigree, the expected relatedness coefficient based on declared family relation is compared with the observed relatedness coefficient (𝑟). IBS0 value helps to differentiate between sibling and parent–child relationships, both expected to have ~50% relatedness coefficient (see table).
Inferred relationship
Expected IBS0 number
Expected 𝑟 (%)
Observed 𝑟 (%) and interpretation
Prerequisites for accessing the DRAGEN QC report
NGS case
Option 1: FASTQ case
1
Run a FASTQ case in Emedgene.
2
Since DRAGEN analysis is integrated into Emedgene secondary analysis pipeline, QC reports are automatically generated in the system.
Option 2: VCF case—Bring your own DRAGEN (BYOD)
1
Run DRAGEN analysis externally.
2
a TAR archive containing DRAGEN QC metrics files.
3
Upload the TAR archive and the sample VCF file to Emedgene.
This workflow is supported for and .
Array case
Array cases start from VCF input files. DRAGEN QC for array cases is supported on Emedgene v100.39.0 and later.
VCF case—Bring your own DRAGEN (BYOD)
1
Run DRAGEN analysis externally using .
2
Upload the .annotated_cyto.json DRAGEN QC metrics file, the sample VCF file, and the .gt_sample_summary.json file to Emedgene.
This workflow is supported for and .
Adding patient info for the proband
Add new case page > Family tree screen > Add patient information panel > Patient info section
1. Fill in the boxes:
Note:
Default region of interest kits
A region of interest (ROI) BED file determines which genomic regions will be included in the variant analysis. It functions as a preprocessing filter, determining which variants proceed to annotation and interpretation.
Default ROI kits by case type
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
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
Keep file paths simple (avoid spaces, parentheses, or very long names >255 characters). This helps prevent errors during upload.
For large files (BAM/CRAM/FASTQ), browser upload is not recommended. Use Batch Upload, CLI, or cloud-to-cloud transfer instead to avoid incomplete or truncated uploads.
A customizable field that allows you to assign custom .
Click the pencil icon to add a new label, select an existing one, or remove a label from the case.
Participants
Users involved in the case who subscribed to 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
as defined in Settings. Each group appears as a separate column in the table.
Case ID
A unique identifier assigned to each case by Emedgene, formatted as EMGXXXXXXXXX. This field is fixed and cannot be hidden or repositioned in the table. Share this code with Tech Support when reporting issues.
Proband ID
The identifier of the proband. For single case creation, this corresponds to the Sample Name; for batch case creation, it corresponds to the BioSample Name of the test subject.
Phenotypes
Proband phenotypes as submitted by the user.
Status
The current case status in the system. Custom statuses can be added in the Management tab under Settings, and their order can be rearranged via drag-and-drop.
You can update the status directly from the Cases table by clicking the status badge and selecting a new status from the dropdown menu.
Creation date
The date the analysis was initiated. This is saved automatically.
The field is sortable.
Due date
A customizable field that allows you to set, change, or remove a due date.
Click the calendar icon to set a date. To change it, click the existing date and select a new one. To remove it, click the cross icon next to the date.
The field is sortable.
Quality
Indicates the overall case quality. Hover over the icon for a brief summary, or view detailed results in the Lab tab.
The field is sortable.
Type
Indicates the case type (whole genome, exome, custom panel, array).
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
The default fixed value for Proband is Test Subject.
3. Date of Birth
Expected format: mm/dd/yyyy.
4. Medical Condition (*)
Options: Affected, Healthy.
The default value for Proband is Affected, but you may change it to Healthy.
5. Proband Phenotypes (*)
To add all relevant phenotypes for the Proband, use one of the following methods:
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
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.
Batch mode
Paste a list of comma-separated HPO terms or HPO IDs in the search box and press Complete.
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.
Extract HPO terms from the file uploaded in the Clinical Notes section
In the Clinical Notes section upload a description of the clinical presentation in .pdf, .xls, .txt, .doc, .jpeg, or .jpg format. Among the extracted HPO terms for Phenotypes and Diseases select the ones you want to add to Proband's Phenotypes.
6. Proband Suspected Disease Condition.
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.
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
Enter the suspected disease penetrance as a percentage.
8. Suspected Disease Severity
Select the appropriate category to indicate the severity of the disease symptoms observed in the patient: Mild, Moderate, Severe, Profound.
9. Consanguinity
Mark the checkbox if applicable.
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 Lab tab.
10. Patient Ethnicities
Paternal and Maternal. Enter the ethnicity name in the search box and select a matching term from a dropdown menu.
2. Click on Complete once all the information is added.
).
Case type
Default region of interest BED
Research Genome
None
Whole Genome
Exome
Custom Panel
Default ROI kit details
Full Genes
A wide range of genomic regions BED file. It contains:
"RefSeq ALL" transcripts and "GENCODE" full genes regions with 5Kbp upstream and 5Kbp downstream
Within this range, all “Clinical Regions” are included
All dosage regions (HI/TS sig level 1, 2 or 3)
Moreover, liftover versions of both reference regions were included, for the current and previous range versions.
Sources:
Liftover done using CrossMap (v0.5.2), chain hg19ToHg38.over.chain.gz
NCBI RefSeq regions are based on the release 105 (hg19) and 110 (hg38)
Gencode regions are based on the release V19 (hg19) and V41 (hg38)
All microRNA genes based on HGNC miRNA definition December 2022
ClinVar variants (ClinVar Dec 2022) with any pathogenic or likely pathogenic significance (and some drug responses that are affiliated with pathogenicity)
50K STR regions based on the DRAGEN 4.0 Specification file
CNV variants are not confined to regions of interest.
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 [email protected].
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
FASTQ/BAM/CRAM (input)
Reanalysis will fail (will be fixed)
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 is only available for Enterprise level support accounts and require Illumina support for setup.
Bring Your Own Bucket
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 [email protected] and follow this steps:
1. Create an AWS bucket
Emedgene requires access to the root folder, which means a dedicated bucket might be appropriated.
2. Edit Bucket policy
Bucket policy should allow Emedgene user access to the bucket.
Example bucket policy:
3. Allow illumina.com and emedgene.com for CORS
Emedgene directly from your AWS S3 bucket. In order to do it, you should enable for the Emedgene application URLs.
Example CORS policy:
4. Test and validate the configuration with Illumina support
We will require to run a case and validate the managed DRAGEN pipeline finish successfully and all features are available in the platform.
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.
Managing AWS S3 Lifecycle policy
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
Manage Google Cloud storage
Google Cloud Storage Credentials update procedure
How to get the client credentials?
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.
Fill in the Service Account Details:
Service account name: Give your service account a name.
Service account ID: This will be automatically generated based on the name.
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:
Add the storage provider to Emedgene platform:
Add the above 3 values into the appropriate fields:
Client_credentials_base64: pasting the output of 8.
Bucket: the bucket name.
CORS - Visualisation
Download and install the Google Cloud SDK from the Google Cloud SDK Install page.
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.
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
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 Sequence Ontology (SO), 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 link 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 here.
Variant severity
Variant severity, also known as variant impact, is a subjective assessment of the severity of a variant consequence.
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.
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 (dbscSNV > 0.6 or max spliceAI > 0.8) or MODERATE if its splicing prediction is moderate (max spliceAI > 0.2 or dbscSNV > 0.5).
Conservation: Synonymous variants and splice region variants that are highly conserved (GERP score > 0.9 or PhastCons100 > 0.2) 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. The current list of RNA genes is:
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 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
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 [email protected].
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.
Creating a single case
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 .
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.
Case statuses in a case lifecycle
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 for assignment and reassignment.
Case statuses are grouped into three categories based on control type:
System-controlled: Assigned automatically by the system; cannot be reassigned by users.
Prepare DRAGEN QC metrics files to be included in a NGS VCF case
When creating NGS cases that start from VCF, you can create a browsable from the DRAGEN metrics files. Due to security restrictions, CSV files are not directly ingested, but they can be included when packaged in a TAR file.
Navigate to local directory containing metrics files for a specific sample.
Define sample name as a variable samplename="NA12878".
Manage Azure Blob storage
Before you proceed to this article, make sure you understand .
Update Azure Blob Storage Credentials
In > Management Tab, add or edit the required credentials: CLIENT_ID, CLIENT_SECRET, TENANT_ID
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.
Coding regions
Clinical Regions and not in Coding regions
Full gene and not in Clinical regions
None
Deletion (DEL)
Coding regions
Clinical Regions and not in Coding regions
Full gene and not in Clinical Regions
No overlap with any BED
Gain (DUP)
Intragenic (coding regions but not entire gene region)
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')
Add Clinical Notes (optional) in free text, or upload a clinical presentation file (.pdf, .xls, .txt, .doc, .jpeg, .jpg).
HPO terms for phenotypes and diseases will be extracted and can be linked to the proband.
Select suspected Inheritance mode(s) (for record only; not used in the analysis).
Decide whether to include Secondary findings in the proband for the AI Shortlist (checkbox).
Add patient information (right)
For each family member:
Add a sample (use a unique file path unless reusing samples).
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.
Fill in a sample name (for VCF input, this must match the header in the file).
Some diseases may not suggest phenotypes automatically if the source database does not provide them. You can add phenotypes manually in these cases.
Click Next to proceed to the Case info screen.
Step 3: Case info screen
Here you define how the analysis will run:
Case type: 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.
:
Select an enrichment kit (if applicable) or "No kit".
If provided, kit details (Lab, Machine, Reagents, Expected coverage) will be used to compare coverage depth and breadth.
If no kit is provided, RefSeq coding regions will be used as reference.
options:
All genes
Phenotype-based genes
Existing gene list
: 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.
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.
Additional case info (optional):
Indication for testing (free text).
Labels (choose from predefined organization labels; these cannot be changed later).
At the Summary stage, confirm case type, gene list, and other selections.
Caution: Clicking Next here will finalize case creation. After delivery, only the proband’s phenotypes can be edited without reanalysis.
Step 4: Done screen
After the case is created:
The Case ID is displayed.
You may add participants so colleagues receive notifications on status changes or updates.
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.
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.
Combine the find and tar commands to package the files into a tar.gz file with the following extension *.metrics.tar.gz.
Command to find files matching the required patterns:
Upload the metrics.tar.gz file to the storage location used for creating cases.
Add metrics.tar.gz to case creation API JSON payload using the corresponding storage ID.
Ensure that if the extension is not contained in the filename (e.g. files from BaseSpace) that "sample_type": "dragen-metrics" is set within the JSON payload.
See the table below to learn where to look for them in your Azure account.
Emedgene setting
Corresponidng client (Azure) setting
CLIENT_ID
application_id.
Format: ########-####-####-####-############
(letters/numbers)
CLIENT_SECRET
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
An arbitrary name that the customer must supply to define the ACCOUNT_URL.
Format: string
ACCOUNT_URL
Blob Integration Setup
Create an App registration
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.
Azure Blob configuration
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
For Internal support:
Errors for bad connections can be found in CloudWatch on particular FRY log stream
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
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
(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.
Risk of losing a key
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.
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
Create a new application
1
Navigate to App registrations
2
Click Register to create a new application and and fill in the required details
3
Add a client secret
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.
Create a new key
1
Click New Key (Create key vault)
2
Specify the key vault name, region (for example, East US), and pricing tier
3
Find key details
1
Navigate to the newly created Key vault
2
In the left menu, select Keys, and then select the key
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
Searching Encrypted Fields
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:
Appendix
Appendix: Control flows text
Write:
Read
Write Searchable
Read Searchable
Genes coverage section
The Genes coverage section helps you quickly identify parts of genes that may not have been adequately sequenced in your case. This insight is particularly important when assessing sequencing quality, interpreting uncertain findings, or deciding if further validation is needed.
While variant callers provide base-by-base coverage, Emedgene simplifies the view by showing average coverage per region. This makes it easier for you to spot undercovered genes at a glance, even when individual positions may appear sufficiently covered. By smoothing out local fluctuations, average coverage helps you prioritize regions that might require further review and complements DRAGEN's fine-grained metrics with a broader, more interpretable view.
How coverage is calculated
Coverage metrics are generated differently depending on the type of input data used for your case:
1. FASTQ / BAM or gVCF-based cases
If your case was started from FastQ/BAM or gVCF, coverage is inferred from gVCF reference blocks (also called GVCFBlocks).
These blocks are segments of the genome where genotype quality (GQ) is consistent.
A new block is created whenever there's a significant change in GQ, which results in a highly segmented and detailed representation of local sequencing quality.
2. VCF + BAM or VCF + BED-based Cases
If your case includes VCF and BAM or VCF and BED, coverage is calculated directly from the aligned reads or from predefined BED intervals.
Coverage is calculated as the true base-by-base average across the entire region.
This method avoids the variability of gVCF segmentation and gives a precise coverage profile for each region.
Tip: Before comparing coverage values across cases, check whether the case was processed from FASTQ/gVCF or VCF with alignment files (BAM/CRAM). The calculation method differs, so values may not be directly comparable.
Limitation: Coverage estimation is not supported for VCF + CRAM cases. If a CRAM file is used with a VCF file, as opposed to a BAM or a BED file, the Genes Coverage table will remain empty for virtual panel cases.
Regions evaluated for coverage
Coverage is compared against expected regions defined in:
Emedgene's reference BED file, or
Your test’s custom KIT BED file
Each region is defined by:
Chromosome
Start & end positions
Name and strand (Optional)
Coverage assessment
Emedgene uses the tool bedtools intersect to compare each expected region from the regions used for coverage assessment against actual read coverage. The system captures:
How much of the region overlaps with sequenced data
Depth of coverage per segment
Coverage statistics
Each region includes these metrics:
Metric
Description
Note:
Genes with insufficient coverage is available only for FASTQ-based cases.
Warning:
Minimum depth for FASTQ / BAM / gVCF-based cases does not represent minimum depth but Minimum average depth within the GVCF block.
How to use the coverage tool
You can interactively explore gene-level coverage details using the Genes with Insufficient Coverage tab. This tool is currently available only for FASTQ-based cases.
Here’s what you can do:
Search for a specific gene or a list of genes.
Filter results based on coverage thresholds:
≤0x
To check coverage for a gene:
Enter the gene symbol in the search box and select it.
Choose your desired coverage filter from the dropdown.
Review the results in the table or download the data.
To look up the coverage for multiple genes that are saved as a :
Click the Add Gene List button and select any of your pre-loaded gene lists.
To further filter regions:
By maximum depth of coverage
Select Coverage, then choose the highest allowable coverage value from the dropdown list,
By percentage of bases covered >20×
Select % of Bases Gt20, then choose the highest allowable percentage from the dropdown list.
Visual review in allows manual variant confirmation by inspecting aligned reads at specific genomic regions.
To inspect poorly covered regions of a gene in the desktop IGV browser:
Click on More details in the row corresponding to the gene of interest. This opens a pop-up with coverage details for the selected gene.
In the pop-up, select View on IGV to open the region in the IGV desktop application.
To download data
Click the Download button to export the full list of low-coverage regions as a *_insufficient_regions.tsv file. Each row includes region coordinates and all metrics.
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 variant callers.
System-controlled.
Exception: user-reassignable to "Trash bin"
Out-of-the-box
"Reanalysis"
The system is re-running the AI Shortlist algorithm.
System-controlled
Out-of-the-box
Coverage for a region is based on the median coverage of each gVCF block. If a region spans multiple blocks, the reported value is the average of those medians.
Within a region (like an exon), you’ll often see multiple blocks. Emedgene aggregates them to show you:
Average depth
Minimum depth
Depth range
Occasionally, some blocks may be unusually large and may miss internal variation—for example, in genes like XIAP, one block could span an entire region despite having uneven coverage inside.
≤5x
≤10x
≤20x
or All
Download tables or genomic coordinates for regions with poor coverage.
Click More details to open a pop-up with exact genomic coordinates of low-coverage blocks.
Click More details to inspect the specific coordinates of undercovered regions.
Min Depth
Lowest depth in the region (for gVCF-based cases: lowest avg depth in a block)
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.
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
CSV format requirements
General CSV format requirements
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.
CSV schema
1. Mandatory fields
Must be present in the sample table at all times.
Case Type;
Family Id;
Phenotypes OR Phenotypes Id.
2. Conditionally mandatory fields
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.
3. Optional fields
The sample table may include these supported optional columns.
Boost Genes
Clinical Notes
Date Of Birth
Due Date
4. Custom fields
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 .
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:
Field (column) name
Expected input
Field details
Example
Batch case .csv file validation rules
(highlighted in red), (highlighted in orange), and fields should be filled in according to the following rules.
Field (column) name
Expected input
Field details
Example
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.
Required BSSH file path format:
For BSSH, it is necessary to use the actual names (numbers):
instead of aliases
Human-readable path for BSSH files in batch CSV
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.
Validations
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:
Error Scenarios
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.
Benefits
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 version
Available callers
DRAGEN Array version
Emedgene version
Available callers
Extensive Compatibility with Additional Variant Callers
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.
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.
Additional callers can be supported on demand under license.
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
Date "YYYY-MM-DD"
Optional
2013-01-22
Default Project
Free text
Conditionally mandatory.
Must be filled in if the "auto" option is used for Files Names (only relevant for BSSH).
GIAB
Due Date
Date "YYYY-MM-DD"
Optional
2023-05-03
Execute now
1. "TRUE"
2. "FALSE"
Optional.
Default value is "TRUE". Use "FALSE" if you don’t want to run the case upon uploading the file.
Only considered for proband.
FALSE
Family Id
Free text
Mandatory
RM8392
Files Names
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)
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 .
Optional.
Must be the id of a previously defined Gene List.
Only considered for proband.
12345
Kit Id
integer
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.
23456
Intersect Bed Id (38.0+)
integer
Optional.
ID of a Region of interest BED.
Must be the id of a previously defined kit.
Only considered for proband.
78957
Label Id
integer
Optional.
Must be the id of a previously defined Case Label.
Only considered for proband.
34567
Opt In
1. "TRUE"
2. "FALSE"
Optional.
Indicates whether the case subject consented to the with your network(s).
Default value is "TRUE".
FALSE
Phenotypes
Semicolon-separated list of HPO phenotype terms
"Unaffected" is used for non-affected family members.
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.
Abnormal pupillary function;Orthotopic os odontoideum;
Phenotypes Id
Semicolon-separated list of HPO phenotype IDs
Mandatory for proband sample if Phenotypes is empty.
List must be under 100.
HP:0007686;HP:0025375;
Relation
1. "proband"
2. "mother"
3. "father"
4. "sibling"
Optional.
Default value is "proband".
Values "proband", "father", "mother" can be only used once per Family ID.
One sample with Relation "proband" is required per Family ID.
Mother
Sample Type
1. "FASTQ"
2. "VCF"
Conditionally mandatory.
Required if Files Names is empty.
Only considered for proband.
FASTQ
Selected Preset
1. Free text
2. "Default"
Optional.
Must be the name of a previously defined Preset. If set to default, the default Preset will be applied. If left empty, no Preset will be applied.
High quality candidates
Storage Provider Id
Integer
Conditionally mandatory.
Required if Files Names is not empty.
Must be from the configured storage provider ID list.
208
Visualization Files
Semicolon-separated list of paths to sequence alignment data files of extension .bam, .cram; .tn.bw, .baf.bw, .roh.bed, .lrr.bedgraph, .baf.bedgraph
Optional
/giab_project/NA24385.bam
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.
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
Free text
Custom
GenoMed Solutions
Sample_Received_Date
Free text
Custom
24-02-2022
Sample_Type
Free text
Custom
BioSample Name
Free text
Conditionally mandatory.
An empty sample will be created if the field is left blank.
NA24385
Boost Genes
1. "TRUE"
2. "FALSE"
Optional.
Indicates whether the Boost genes mode 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.
