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.

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.

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

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

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

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

Look around

The platform is operated from the .

By clicking on the corresponding buttons, you can enter:

• tab

• page

• menu

• dropdown menu

Create a case

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

Examine the analysis results

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:

The Cases table navigation panel provides several tools to help you customize your table view and manage cases. It includes the following components:

• menu Use this to narrow down the list of cases.

• menu Organize your cases by case status

• menu Choose which columns are visible in the table and define their order

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.

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.

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.

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

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

• Lab

• Instrument

• Reagents

• Kit type

• Expected coverage

• Protocol

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.

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.

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

• 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.

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

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 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 Analysis Tools tab includes features to support case review:

• Filters panel (expandable panel on the left): Provides adjustable filters and filter presets to refine variant selection

• Variant table (main page body): Displays analysis results in a customizable, sortable table

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.

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.

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

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.

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:

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):

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):

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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.

How to select columns to be displayed

A. Show or hide columns via the Fields menu

B. Hide a column directly from the Cases table

How to change column order

You can reorder columns in three ways.

A. Drag and drop the column

B. Reorder columns via the Fields menu

C. Move a column using a dropdown menu

How to adjust column width

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

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

1. In Settings > Management > S3 Credentials, click on Create Access Key.

2. 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.

Available filters

You can filter cases using the following fields:

• Case ID

• Sample ID (Proband ID)

• Status

• Type

• Label

• Resolved: Resolved or Not resolved

• Participants

How to apply filters

How to remove a filter

How to clear all filters

In the Cases table navigation panel, click the X icon next to the Filters menu

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.

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

The Cases tab includes:

1. Cases table—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

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.

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 .

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.

How to update a case status:

A. On the Individual case page

B. In the

1. In the Cases table, click the current case status of the relevant case

2. From the dropdown menu, select the new status you want to apply

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.

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

Included metrics:

• Case quality Displays the overall case quality status

• Reflects sample quality status

• Evaluation kit

  Specifies the QC BED kit used to evaluate coverage depth and breadth. If no kit is specified at analysis launch, NCBI RefSeqGene is used as the default reference

• Custom gene coverage Indicates whether the coverage of genes in the selected panel meets the expected threshold, as defined by the QC BED

• Displays the results of relationship validation, confirming whether the submitted pedigree aligns with genetic data

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

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

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 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.

The case status reflects the current stage of case processing, either by the Emedgene platform or a genomic analyst.

You can view the current status of a case in the following locations:

• Status column in the Cases table – for a quick overview across multiple cases

• Top bar of the individual case page – for immediate visibility while reviewing a case

• Case details panel, under Case-related activities – to track status updates and history

There are both provided by the platform and the option to create to suit your workflow.

The DRAGEN sample-level quality control (QC) report 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 accessed from the Sample quality section of the Lab tab.

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

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

Cases table lists key details of all genomic sequencing cases submitted by the organization.

You can the table by hiding, showing, rearranging fields, or adjusting column widths, except for Case ID, which is fixed as the first column and always visible.

Cases table fields

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

The Case details panel is organized into three tabs:

• Case info—displays technical, operational, and clinical information about the case

• Family tree—shows a graphical pedigree and sample details for each family member

• Activity—provides a timeline of all actions taken within the case for audit and collaboration

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

1. Case search bar

2. tab

3. tab

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:

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

In the tab under Settings, you can tailor how case statuses appear and function to better align with your workflow:

• Create custom case statuses to reflect your team's specific processes

• Remove unused statuses to keep the list clean and relevant. Only statuses that are not currently assigned to any case can be removed.

• Rearrange the order of statuses by drag-and-drop to match your preferred case interpretation flow. The updated order is reflected in:

> 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

To select variants with a particular tag, use the Filter candidates dropdown menu in the top right corner. You can select from Most Likely, Candidate, Incidental, Carrier, Not Reviewed, or any custom tags used in your organization.

For each variant on the Candidates tab, you can explore the suggested diagnosis, gene symbol, main variant details, and variant tag.

When a variant is found in a gene with no known association with a disease, the possible diagnosis cannot be indicated. Such variants are displayed under the Gene of Unknown Significance title.

All the relevant fitting a сompound heterozygous mode of inheritance are presented together. This refers to both confirmed and assumed compound heterozygosity (cases with at least one parent and singleton cases, respectively).

If you want to inspect the complete variant information, click on the variant bar to continue to the . You can visualize evidence in text or graphical format (Click on the interactive text in the top left corner: Show evidence as text or Show evidence graph to toggle between the two).

You can implement different combinations of 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 flow while or a case.

The Top bar in the Individual case page indicates the Case ID and current .

Options available through the Top bar:

• Change the

When available, a 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.

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).

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

NGS case

Option 1: FASTQ case

The table view is customizable:

• can be reordered by drag-and-drop

• Any column can be shown or hidden by selecting the columns in the Show/Hide columns menu in the top right corner of the page

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

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

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.

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

ACMG SF v3.2 gene list

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.

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.

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

How to link your storage to Emedgene:

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.

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

• —reports sequencing run technicalities

• —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

Prerequisites

• Download and install node js platform via https://nodejs.org/en/download Minimum version required: 16 Upgrade existing installation: nvm install --lts

Batch upload via CLI (Command Line Interface)

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

1. Download the CSV template file.

1. Edit the downloaded batchCases.csv file. See for more details.

2. 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.

1. 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.

2. -l will create a log file in the same location.

More information can be found by running

Add new case page > 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:

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 () or in a batch ().

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).

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.

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

Variant tagging by the AI Shortlist

Variants are automatically tagged as:

• Most Likely Candidates and Candidates

  Variants prioritized by the AI Shortlist

• Secondary findings

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

• Carrier variants

  Variants identified by the carrier analysis pipeline (if enabled)

Assigning variant tags during review

During review in the Candidates tab, additional tags can be applied to a variant alongside the original automatic tag.

The Candidates tab presents:

A set of the most promising variants based on scores calculated by the AI Shortlist. These variants are initially tagged by the system.

Variant types assessed:

• SNVs and indels

• CNVs

• SVs

• mtDNA variants

(Secondary)*

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:

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

ACMG SF v3.2 gene list

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.

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.

In Report and other custom

Variants that were manually selected to be reported.

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).

The formatting of variant table rows provides visual cues about the variant status for the current user within a specific case.

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.

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, and ACCOUNT_URL

Google Cloud Storage Credentials update procedure

How to get the client credentials?

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:

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.

The Genome view tab is a powerful feature designed to give users a clear, visual overview of the genome and chromosomes in their cases. This feature is especially useful for analyzing large Copy Number Variation (CNV) events and regions of homozygosity/loss of heterozygosity (ROH/LOH) across the genome, providing intuitive filtering and interactive insights for researchers and clinicians.

What is the Genome view tab?

The Genome View tab is a dedicated section within the Case Page for Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), and Array data. This tab offers a graphical visualization of genomic data, focusing on CNV and LOH/ROH analysis for proband cases. Users can access this tab directly from the Case Page.

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.

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.

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

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.

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.

This guide provides a step-by-step process for creating a new case via the user interface. Detailed instructions for each step are available in the corresponding pages of the section.

Step 1: Start a new case

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, VCF, or Array).

3. Click Next to proceed.

Step 2: Build the family tree and add patient information

The page is divided into two panels: Create family tree (left) and Add patient information (right).

(left)

1. Use the visual tool to build the pedigree.

2. Add Clinical Notes (optional) in free text, or upload a clinical presentation file (.pdf, .xls, .txt, .doc, .jpeg, .jpg).

   1. HPO terms for phenotypes and diseases will be extracted and can be linked to the proband.

Add patient information (right)

For each family member:

1. (use a unique file path unless reusing samples).

1. Fill in a sample name (for VCF input, this must match the header in the file).

2. Complete the required patient details: for a and for .

Click Next to proceed to the Case info screen.

Step 3: Case info screen

Here you define how the analysis will run:

1. : Choose Array, Custom Panel, Exome, Whole Genome, or Other.

   • For Exome cases, variants outside exons ±50 bp are automatically filtered.

2. Carrier Analysis: Optional checkbox. Requires a targeted gene list.

3. :

Additional case info (optional):

• Indication for testing (free text).

• (choose from predefined organization labels; these cannot be changed later).

At the Summary stage, confirm case type, gene list, and other selections.

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.

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.

Project VCF

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

Accepted file types:

• .pvcf

• .vcf

• .pvcf.gz

• .vcf.gz

VCF

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

Accepted file types:

• .vcf

• .vcf.gz

• .targeted.json

• .gt_sample_summary.json

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 ).

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.

1. Navigate to local directory containing metrics files for a specific sample.

2. Define sample name as a variable samplename="NA12878".

3. Combine the find

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.

Add new case page > Family tree screen > Add patient information panel > Patient info section

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. ,

2. ,

3. , or

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

Selection mode

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 patient information below.

Batch mode

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

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. 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.

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.

10. Patient Ethnicities

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

2. ​Click on Complete once all the information is added.

Scope

Bring Your Own Key (BYOK) is a security feature that allows organizations to use their own encryption keys to protect their data. This ensures that they maintain control over their encryption keys and, consequently, their data.

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.

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

Add a client secret

Create a new key

Find key details

AWS Key Management Service (KMS) Setup

Description is coming soon.

Architecture

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.

• The salt is unique and will not be used anywhere else in the platform.

Illustration of data flow when searching in Emedgene platform:

Illustration of data flow when creating a case with searchable field in Emedgene platform:

Appendix

By using the multi-selection mode, you can speed up review by applying actions to multiple variants at once. This makes it easier to apply tags, assign pathogenicity, and review status without handling each variant individually.

Multi-selection of variants

1. Hover over a variant to reveal a checkbox at the start of the line;

2. Selecting a checkbox activates the Multi-select actions bar, replacing the Search bar.

3. Checkboxes appear for all variants in the current view.

4. Select variants individually or use the Select all checkbox.

   Important: Select all applies only to variants currently displayed on the page, not all variants matching your filters.

Bulk actions:

1. View/ Un-view variants

Change the review status for multiple variants at once:

1. Select the variants of interest.

2. In the Multi-select actions bar, click the Viewed icon.

3. Choose Viewed or Un-viewed.