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.

​

The top navigation panel serves as a guide to the platform. It includes:

1. Case search bar

2. Dashboard tab

3. Cases tab

4. button

5. dropdown menu to switch between and

6. dropdown menu under a question mark icon

7. dropdown menu activated by clicking the username or profile picture

The Cases tab provides an overview of genomic sequencing cases submitted by the organization, as well as individual case details.

1. —displays a list of cases along with key details

2. —enables customization of the table view, including grouping and filtering of cases

3. —opens when a case is selected, providing additional information

The Family tree tab includes the following information:

• Pedigree diagram. Pedigree legend can be found .

• Sample details for each family member:

  • Phenotypes. For family members other than the test subject, phenotypes are categorized as:

You can sort cases by Creation date, Due date, Quality, or Resolution.

• Hover over the column header and click the up or down arrow to sort in ascending or descending order.

• 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.

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 .

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.

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:

1. TARGETED

2. STAR_ALLELE

3. STR_REPEAT_EXPANSION

The header on the Individual Case page shows the Case ID and current .

• Change the

• Reanalyze the case

• and write interpretation notes

To open a case:

• 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.

• Alternatively, double-click the row.

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.

Sequencing lab information section reports sequencing run technicalities as indicated during case creation:

• Lab

• Instrument

• Reagents

• Kit type

• Expected coverage

• Protocol

The Emedgene platform is divided into two applications:

• Analyze—genomic analysis workbench

• Curate—the knowledge management system

To switch from Analyze to Curate:

Go to the nine-dot app launcher icon located on the top navigation panel and select Curate from the dropdown menu.

To switch from Curate to Analyze:

Go to the nine-dot app launcher icon located on the Curate navigation panel and select Analyze from the dropdown menu.

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

Note: Variant types currently annotated and displayed in Emedgene are DEL, DUP and INS.

Welcome to Emedgene, where we unlock genomic insights for hereditary disease and streamline your tertiary analysis workflows.

So you've signed in and can't wait to get started? Here we will guide you through the platform architecture, case creation, and results review. You can dive a bit deeper by following the links and exploring manuals for the platform's applications:

• Analyze: Genomic analysis workbench, where you can accession, interpret, curate and report on your cases, while also efficiently managing the lab workflow

• Curate: A repository for all of your organizational curated knowledge

Get help

Contact Illumina technical support at techsupport@illumina.com.

The platform is operated from the .

From here you can enter:

To enter the flow, click on the namesake button on the . Here:

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.

• Group by menu Organize your cases by case status

• Fields 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

You can use the Case search tab in the top bar to search for cases by the Case ID or Proband ID.

How to group cases

How to remove grouping

In order to prevent accidental data loss, deleting cases in Emedgene includes a staging step before permanent case deletion.

Result:

After deletion is confirmed:

• All cases marked Trash bin are permanently removed

• An activity entry is recorded

• Email notifications are sent to users who have opted in

Click on the icon in the top navigation panel to open the Help dropdown menu.

From there, you can access:

• Help Center: Find feature guides, step-by-step instructions, and tips to help you get the most out of the platform.

• Walkthroughs: View short interactive demos of workflows (in development).

• Feature requests: Share your ideas and feedback.

• What's new: Stay updated with the latest release notes.

• About: View general information such as your organization name and platform version.

To directly import files from your own storage, link it to an organization's storage in Emedgene.

How to link your storage to Emedgene:

Click Manage on the right to the storage details.

Click Delete on the right to the storage details.

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

1. Fill in the boxes:

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.

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.

Paste a list of comma-separated HPO terms or HPO IDs in the search box and press Complete.

Select case type

Select the case type in order to define the proper analysis of your case.

Select region of interest

Users can utilize a custom region of interest (ROI) BED file to limit analysis results to variants within the designated regions. A ROI BED determines which genomic regions will be included in the variant analysis.

If no custom ROI BED is selected, the system uses the default ROI BED file based on the case type.

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.

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.

After selecting a coverage BED file, the available reference sequences for this kit will be displayed.

Specify sample preparation details

Specify details such as laboratory name, sequencing machine used, sequencing reagent kit, and expected coverage.

Select a gene list

You can limit analysis to a gene list in the platform while creating a case. Choose between:

1. All genes

No limitation of the analysis.

2. Existing gene list

Select one of the previously added gene lists from a dropdown list.

3. Create a new gene list

Generate a new virtual panel: add a List title and then add all the gene symbols one by one (Selection mode) or in a batch (Batch mode).

A new gene list can be comprised from a combination of configured gene lists and/or individual genes.

A gene list can by configured to hold up to 10,000 genes.

A new gene list can be created by combining configured gene lists and/or individual genes. Each gene list can be configured to contain up to 10,000 genes.

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).

You can choose between two different modes of a gene list feature:

Selected by default.

AI Shortlist is limited to the selected gene panel, no variants in other genes are considered in the results. If this in silico panel is used for analysis of exome or genome data, the gene restriction may be lifted during manual analysis to "open-up" the entire exome or genome for analysis.

Analysis is performed for variants in all the genes. Variants in the targeted genes get upgraded scores during prioritization by the AI Shortlist algorithm.

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.

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 gene-related disease, gene symbol, main variant details, and variant tag.

When a variant is found in a gene with no known disease association, the gene-related disease cannot be displayed. Such variants appear under the Gene of Unknown Significance heading.

All the relevant Most Likelies and Candidates 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 Evidence page. 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).

The Lab tab shows sample and case-level quality metrics so you can check data reliability before starting interpretation.

The Lab tab includes:

• Summary dashboard: highlights the key quality indicators, with more details provided in the subsequent sections

• Sequencing lab information section: reports sequencing run technicalities

• Case quality section: summarizes the data quality of the case

• : highlights quality metrics for each sample

• : displays the results of the relationship validation for each pair of samples in a family tree.

• : highlights regions that may not have been adequately sequenced

The Summary dashboard provides a quick overview of key quality indicators at the case and sample levels.

Included metrics

• Case quality — displays the overall case quality status.

• Sample quality — reflects the sample quality status.

• Evaluation kit — specifies the QC BED kit used to evaluate coverage depth and breadth. If no kit is specified when analysis launches, NCBI RefSeqGene is used as the default reference.

• Custom gene coverage — indicates whether coverage of genes in the selected panel meets the expected threshold defined by the QC BED.

• — displays relationship-validation results and confirms whether the submitted pedigree matches the genetic data.

The Dashboard tab depicts an overview of the user activity on the Emedgene platform and provides a glance at key performance indicators for an organization.

Lefthand panel

• The Diagnostic Yield card shows the percentage of cases classified as Resolved out of the total number of cases of the same type.

• The Status Diagram card shows the total number of cases submitted by the organization and the count of cases for each status.

• The Stale Cases card highlights cases stalled at intermediate stages of analysis that haven't been finalized.

Righthand panel

• The Network Activities panel displays a timeline of user activities within the organization. This log includes activity like creating a case, verifying a filter preset, changing a , generating a report, and more.

The Case details panel provides comprehensive information about a particular case.

The Case details panel is organized into three tabs:

• Case info: Displays technical, operational, and clinical information about the case.

• Family tree: Shows a graphical pedigree and sample details for each family member.

Labels help you mark cases for specific uses and filter case subsets in the Cases tab. You can , , or case labels in the .

You can filter cases using most of the fields, as well as by the case outcome category (Resolved / Not resolved), which is the only filter not displayed as a column.

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.

While adding a new case, you will build a pedigree and annotate each of the samples with data required for analysis.

After the case has been created, the family tree is available in the panel (righthand panel of the Cases page).

1. 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).

A preset group is a reusable set of filter presets applied for specific case types, as defined by your laboratory SOPs.

Select a preset group in the Case info screen during case creation or .

• The group selected for the case determines which presets appear in the Presets tab of the Filtering panel.

• If no group is selected, the system automatically applies the default preset group defined in Lab workflow settings.

• Download and install node js platform via Minimum version required: 16 Upgrade existing installation: nvm install --lts

1. 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

For DRAGEN versions earlier than 4.2, when ingesting a VCF containing STRs (either a DRAGEN STR VCF or a DRAGEN ExpansionHunter VCF), add the following line to the VCF header:

Additionally, ensure that the contig lines are present in the VCF headers. If they are missing, please use the following ones:

GRCh37

GRCh38

Both GRCh37/hg19 and GRCh38/hg38 are supported. You can run cases with both reference genomes in the same organization.

• GRCh38/hg38: Multigenome Graph hg38-alt_masked.cnv.graph.hla.rna-10-r4.0-1.tar.gz.

  Pre-built multigenome hash tables for hg38. The hash table builds include DNA, RNA, CNV, and HLA tables. Download .

• GRCh37d5: Multigenome Graph hs37d5-cnv.graph.hla.rna-10-r4.0-1.tar.gz

Unlike single-nucleotide variants (SNVs), a multi-nucleotide variant (MNV) represents a single event involving multiple consecutive bases. In Emedgene, small variants are recognized as those comprising an MNV if they are located within a 2-nucleotide distance.

Emedgene recognizes MNV as a distinct variant type and supports ingestion from VCF, annotation, and filtering.

Each MNV is represented and annotated as:

• An MNV itself (eg, AG>TC)

• Individual SNVs derived from the MNV (eg, A>T and G>C), for compatibility with existing tools and workflows

Both the MNV and its underlying SNVs display the "Suspected MNP" badge in the

Annotations from organization databases appear in various parts of the platform, each showing certain details.

• Allele frequency—in "[Organization DB] AF (%)" column

• Allele count—in "[Organization DB] AF (#)" column

• Summary tab Population summary card

The user can enter a specific case from the by clicking Full details in the corresponding row of the case table.

1. —displays a Case ID and and includes Case interpretation, Edit case info, and Report preview buttons

2. —highlights a shortlist of variants, suggested to be reviewed first - Most Likely Candidates and Candidates

You can update the case status either from the individual case page or from the Cases table.

Finalized case status can be applied only from the individual case page to prevent unintended .

The Sample quality section in the Lab tab gives you a quick view of the reliability of sequencing or array data used in your case.

The metrics displayed in the Sample quality section and their underlying calculation vary depending on the case type (see below).

NGS sample quality metrics provide an overview of QC validation and coverage results for each sample.

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.

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.

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.

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.

To streamline case review, the AI Shortlist pre-selects the list of variants likely to be causative for each case: Most Likely Candidates and Candidates.

Most Likely Candidates

Variants that are most promising for solving the case. This list is limited to 10 top-scored variants but may include more if more than one variant is tagged per gene (suggesting compound heterozygosity). We can change the Most Likely Candidates number limit upon request.

Candidates

Several dozen highly scored variants worth considering.

The ranking of variants by AI Shortlist considers:

• SNVs

• CNVs

• SNV + CNV compound heterozygotes

• SVs

• mtDNA variants

• STRs

The AI Shortlist rates variants based on predicted variant effects, alternative allele frequency, familial segregation pattern, phenotypic match, in silico predictions, and other relevant information from scientific papers and databases.

During the case review, you can untag variants selected by the AI Shortlist or manually tag ones not selected by the AI Shortlist.

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 quality, alongside log R deviation.

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.

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 quality, alongside call rate.

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.

Sequencing error rate refers to the frequency at which incorrect base calls are made during sequencing process.

Error rate is calculated as number of low quality variants / total variants.

Blue bars represent each of these parameters per sample, while a vertical line represents a general metric across all the samples of the same case type in the account.

Use these options to customize the Cases table view for your workflow.

• Select which columns are displayed

• Change column order

• Adjust column width

How to select columns to be displayed

Show or hide columns via the Fields menu

You can reorder columns in three ways: drag and drop the column, reorder columns via the Fields menu, or move a column using a dropdown menu.

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

• .cram. Make sure you understand the current limitation for using CRAM files by expanding the section below.

Use when working with a joint VCF file containing multiple samples.

Accepted file types:

• .pvcf

• .vcf

• .pvcf.gz

Use for cases where variants have already been called externally, or for cytogenetic array inputs.

Accepted file types:

• .vcf

• .vcf.gz

• .targeted.json

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. 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.

Tagging criteria

A variant is automatically tagged as a secondary finding if it meets all of the following criteria:

1. Classification: Previously classified as pathogenic or likely pathogenic in ClinVar or Curate variant databases

2. Zygosity: Heterozygous or homozygous (only homozygous for the HFE gene)

3. Allele frequency: Less than 5%

4. Read depth: 10× or higher

5. Variant quality: Any value except LOW

6. Affected gene: Listed in the ACMG SF v3.2 or 3.3 gene list for reporting secondary findings (PMID: 37347242, 40568962)

ACTA2, ACTC1, ACVRL1, APC, APOB, ATP7B, BAG3, BMPR1A, BRCA1, BRCA2, BTD, CACNA1S, CALM1, CALM2, CALM3, CASQ2, COL3A1, DES, DSC2, DSG2, DSP, ENG, FBN1, FLNC, GAA, GLA, HFE, HNF1A, KCNH2, KCNQ1, LDLR, LMNA, MAX, MEN1, MLH1, MSH2, MSH6, MUTYH, MYBPC3, MYH11, MYH7, MYL2, MYL3, NF2, OTC, PALB2, PCSK9, PKP2, PMS2, PRKAG2, PTEN, RB1, RBM20, RET, RPE65, RYR1, RYR2, SCN5A, SDHAF2, SDHB, SDHC, SDHD, SMAD3, SMAD4, STK11, TGFBR1, TGFBR2, TMEM127, TMEM43, TNNC1, TNNI3, TNNT2, TP53, TPM1, TRDN, TSC1, TSC2, TTN, TTR, VHL, WT1.

Includes all v3.2 genes plus newly added genes:

• PLN

• ABCD1

• CYP27A1

This brings the total to 84 reportable genes.

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.

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.

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 step 2). Fill the file with your data according to CSV format requirements.

2. Upload a CSV file

1. Click on the + New case button on the top navigation panel.

2. 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.

3. Drag and drop a CSV file into the box or upload it from the file explorer. Wait for file upload and validation to finish.

After validation is complete, you will be directed to the Batch validation page. It features validation results details for you to review:

• File name,

• Number of rows in the file,

• Number of cases to be created

• Number of errors found,

1. Click on Create. A progress bar will appear on the right as the cases are created (Cases creation page).

2. If the cases have been created successfully, the Cases summary page will display the total number of cases that were created.

3. 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.

The Sex validation column indicates whether the biological sex inferred from genomic data matches the sex information provided during case creation. This helps identify potential sample mix-ups or metadata errors before interpretation begins.

Sex validation results:

• Pass

  Reported sex matches the estimated sex

• Fail A mismatch was detected between reported and estimated sex.

• N/A QC file not available; validation could not be performed.

Array sample quality metrics provide an overview of QC validation and call-rate results for each sample.

The Case info tab includes the following information:

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

• Real-time collaboration – Enables teams to monitor each other’s updates as they happen, ensuring transparency

Before you proceed to this article, make sure you understand .

In > Management Tab, add or edit the required credentials: CLIENT_ID, CLIENT_SECRET, TENANT_ID, and ACCOUNT_URL.

See the table below to learn where to look for them in your Azure account.

Case status reflects the current stage of case processing, either by the Emedgene platform or your team. Statuses enable case progress tracking and support a consistent, collaborative case review workflow.

Starting in v100.40.0, the Status column on the Cases page includes a for cases with the In progress, Re-Analysis, or Issue reported status.

You can view and the current case status in the Cases table and in the top bar of the individual case page.

The Candidates tab displays all tagged variants, whether tagged by the AI Shortlist or manually by a user.

Variants are automatically tagged as:

• Most Likely Candidates and Candidates

  Variants prioritized by the AI Shortlist

• Secondary findings

  Variants that meet ACMG-defined criteria for secondary findings and automatically tagged with an Incidental tag (if enabled)

The Contamination column reports whether a sample shows signs of DNA contamination, helping ensure data reliability before interpretation.

Contamination is detected using calculations, which estimate the proportion of reads that do not match the expected genotype. This estimate is based on the idr_baf score.

idr_baf stands for the interdecile range of the B-allele frequency—calculated as the difference between the 90th and 10th percentiles of the distribution of alt / (ref + alt) ratios across all variant sites.

A larger idr_baf value indicates greater variability in allele balance, which may suggest sample contamination, particularly from another human DNA sample.

No contamination detected: idr_baf < 0.200.

The Ploidy column shows results from the DRAGEN Ploidy Estimator.

The estimator detects aneuploidies and infers sex karyotype in whole genome cases.

Ploidy values come from the DRAGEN *.ploidy_estimation_metrics.csv output file.

All autosomes fall within the expected ploidy range. No large-scale autosomal copy number deviation is detected.

At least one autosome has a median ploidy score below 0.9 or above 1.1.

Hover over the result to identify the affected chromosomes.

Ploidy metrics are not displayed in Sample quality. This occurs when:

The Sex validation column indicates whether the biological sex inferred from genomic data matches the sex information provided during case creation.

This helps identify potential sample mix-ups or metadata errors before interpretation begins.

Sex validation results:

• Pass

  Reported sex matches the estimated sex

• Fail A mismatch was detected between reported and estimated sex.

This page explains the requirements for viewing the DRAGEN QC report for each case type.

Run a FASTQ case in Emedgene.

Result: Because DRAGEN analysis is integrated into Emedgene secondary analysis pipeline, QC reports are automatically generated in the system.

Prerequisite: or

The is generated by the Illumina DRAGEN Bio-IT Platform and covers the entire analysis workflow—from raw sequencing reads to variant calls.

A visual summary that includes interactive plots of key quality metrics.

When available, a DRAGEN report link appears below the sample name in the Sample quality section of the Lab tab.

Clicking the link opens the detailed quality control metrics report in a new browser tab. This integration allows users to assess sample quality directly from the Emedgene interface.

A set of detailed CSV files containing sample-level quality metrics. These files are and support in-depth review and documentation.

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

Identification

Proband phenotypes

Google Cloud Storage Credentials update procedure

How to get the client credentials?

1. Go to the google cloud Console.

2. Navigate to IAM & Admin - In the left sidebar, go to IAM & Admin > Service Accounts.

1. Create a New Service Account: Click on the "Create Service Account" button at the top.

1. 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.

   • Description: Optionally, provide a description for the service account.

   Click "Create and Continue".

   example:

1. Assign Roles to the Service Account:

   • In the Grant this service account access to project step, you’ll assign the necessary roles.

   • Grant these role:

     • "storage object viewer" (read-only access)

• Add the above 3 values into the appropriate fields:

  • Client_credentials_base64: pasting the output of 8.

  • Bucket: the bucket name.

  • Path: for default, fill with / else, put your path in the bucket. Seperate directories with /

1. Download and install the Google Cloud SDK from the Google Cloud SDK Install page.

2. Select Your Platform (Windows, macOS, or Linux), download and run.

3. 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

1. Apply CORS Configuration to Your Bucket: run the next command. gcloud storage buckets update gs://your-bucket-name --cors-file=cors.json

2. Verify the CORS Configuration: gcloud storage buckets describe gs://your-bucket-name

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

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)

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".

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.

1. Log in into the desired Emedgene organization.

2. Go to Settings

3. Go to Management tab

4. Click on Add Storage

1. Add the information from your “Credentials” of the App previously created in BSSH.

1. Fill in the boxes:

1. Sex (*)

Options: Male, Female, Unknown.

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:

1. One by one (Selection mode),

2. , or

3. Automatically infer disease-associated phenotypes (see below).

Please follow the steps described below for each phenotype:

1. 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.

2. Select a matching term from a dropdown menu and press Complete after you've added all the terms and additional proband information below.

Paste a list of comma-separated HPO terms or HPO IDs in the search box and press Complete.

Enter the disease name in the search box, select a matching term from a dropdown menu and press Complete. All the associated phenotypes will be automatically added to the Proband Phenotypes.

Selecting a disease only fetches its associated phenotypes for convenience—it does not affect downstream analysis. You can edit this list to match the proband’s case. 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 the proband, click the button next to the HPO term in the Proband Phenotypes list.

Enter the suspected disease penetrance as a percentage.

Select the appropriate category to indicate the severity of disease symptoms observed in the proband: Mild, Moderate, Severe, Profound.

Mark the checkbox if applicable.

Paternal and Maternal. Enter the name in the search box and select a matching term from a dropdown menu.

A region of interest (ROI) BED file determines which genomic regions are included in variant analysis. It functions as a preprocessing filter, determining which variants proceed to annotation and interpretation.

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 Default ROI kit details).

A BED file covering a wide range of genomic regions. It contains:

• "RefSeq ALL" transcripts and "GENCODE" full gene regions, with 5 Kbp upstream and 5 Kbp 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 are included for the current and previous range versions.

• Liftover is done using CrossMap (v0.5.2), chain hg19ToHg38.over.chain.gz

• NCBI RefSeq regions are based on release 105 (hg19) and release 110 (hg38)

• GENCODE regions are based on release V19 (hg19) and release V41 (hg38)

• All microRNA genes are based on the HGNC miRNA definition from December 2022

Download files used in v100.39.0+

Download files used up to v38.0

This BED file includes regions relevant to disease interpretation, specifically:

• “RefSeq Curated” and “GENCODE” regions with 50 bp flanking regions on each side of all exons, including coding exons and UTRs, for protein-coding genes

• OMIM disease-related RNA genes (flanking 50 bp)

• All ClinVar pathogenic variant regions (flanking 50 bp)

• Promoter regions (EPDnew human version 006, flanking 50 bp)

For consistency, the GRCh38 version includes the lifted-over regions from GRCh37 (using CrossMap for liftover).

Download files used in v100.39.0+

Download files used up to v38.0

Each variant has a main_effect and main_gene chosen based on the most prioritized transcript for this variant. This selection influences how variants are displayed, interpreted and classified across the platform.

From 100.39 case pipeline and up, Emedgene introduces improvements to Curate transcript prioritization and updates the RNA gene prioritization logic.

Transcript prioritization (case pipeline v100.39+)

Emedgene uses VEP and EFF for transcript annotations and supports organization-defined canonical and preferred transcripts from Curate.

From v100.39, Emedgene has changed how RNA genes are prioritized relative to protein-coding genes.

Prior to v100.39, if a variant overlapped an RNA gene from the prioritized list, the RNA transcript was often chosen as the main_gene, even when a protein-coding gene had a more impactful variant.

Starting with v100.39 case pipeline

• Protein-coding genes with stronger effects now take priority over RNA genes.

• RNA genes are still considered, but no longer override coding transcripts with higher significance.

This results in more more clinically meaningful main_gene selection.

Here is a list of ordered rules for transcript prioritization:

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.

• High severity is used for variants that are assumed to have a disruptive impact on abundance protein, such as by causing protein truncation, loss of open reading-frame, and/or triggering nonsense mediated decay.

Most of the time, variant effect and variant severity on Emedgene are consistent with VEP. However, genomics is a field defined by exceptions. There are key factors, outlined below, the Emedgene genetic team believes are critical to account for when assigning severity.

For small variants (SNV):

1. Splice prediction: Small variants will be upgraded to HIGH severity if its is high or MODERATE if its splicing prediction is moderate.

2. Conservation: Synonymous variants and splice region variants that are will be upgraded to MODERATE.

3. Non-coding RNA disease genes: The severity of a small variant will be upgraded to MODERATE if the variant is within a known to be associated with disease.

For CNV/SV:

VEP annotates CNVs with overlapping genomic features and designates them with the following effects: transcript amplification (DUP), feature elongation (DUP, INS), feature truncation (DEL), and transcript ablation (DEL). However, the severity assigned by VEP for CNVs does not reflect the complexity of CNV effects on protein function and in our experience is not suitable for genome analysis and filtering.

On Emedgene, variants are annotated in regards to its overlap with three different types of regions: ‘coding regions’, ‘clinical regions’, and ‘full gene’ region (see 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:

Table 1: CNV/SV severity table. For each category of CNV/SV, the types of regions that overlap a given variant required to trigger the severity classification are shown.

For STR variants:

Emedgene is using an internal annotation for STR variants. More details can be provided by request to techsupport@illumina.com.

Known limitations

• List of RNA genes known to be associated with disease is updated overtime as part of pipeline update.

• Emedgene does not provide VEP annotation for non-coding regulatory data.

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 .

Emedgene directly from your AWS S3 bucket. In order to do it, you should enable for the Emedgene application URLs.

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 .

1. Click on the Add New Case button on the top navigation panel.

2. At the page, choose the file type for your case analysis (FASTQ, gVCF, or VCF).

3. Click Next to proceed.

The Case quality section shows the results of and the . Case quality is based on validation of the proband sample.

Use case quality to judge, at a glance, whether a case has enough high-quality, well-annotated variant data to support interpretation.

Case quality is based on five checks of the proband sample:

• Chromosome validation

• gnomAD validation

The overall sample quality indicator summarizes sequencing reliability for each sample. Sample quality is evaluated using different metrics, depending on the sample file type.

The overall FASTQ sample quality reflects the confidence level that the sequencing data can support accurate variant calling.

For samples processed from FASTQ files, quality is determined based on the lowest result among these metrics: