The Management page can be accessed by clicking on the corresponding tab under . Note: The Management page is accessible only for users having a Manager role.

The Organization Settings page is accessible via the dropdown menu under Settings. Note: The Organization Settings page is accessible only for users having a Manager role.

In Emedgene, the smallest unit of access control is an Emedgene role in legacy Emedgene environments and an IAM scope in Illumina environments. Each Emedgene role corresponds directly to an IAM scope, both defining the same set of access permissions.

In Illumina environments, user access is managed externally through the AWS Identity and Access Management (IAM) service.

In legacy Emedgene environments, user access is managed internally via the User Management tab in Settings.

• Set the Array quality thresholds for your organization. If a sample’s quality values meet the criteria below, it will be classified as ‘High’; otherwise, it will be classified as ‘Low’.

• Default thresholds are Call Rate >= 0.99 and LogR Dev <= 0.2

• Values between 0 to 1 are accepted.

In the Case statuses card, 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:

  • The Status field dropdown in the Cases table

  • The top bar of the individual case page

Stale Days

Define the number of days that must pass without the case being finalized for it to be classified as "stale" and subsequently factored into the Stale Cases statistics on the Dashboard.

Create and delete user groups as needed, aligning them with your case review workflow. Each user group is represented by its dedicated column in the Cases table.

Associating URLs with your workgroup on ILMN Cloud

On ILMN Cloud, customers can easily associate URLs with their current workgroup by selecting from a list of predefined URL patterns. These URLs play a crucial role in organizing and accessing workgroup-specific resources.

Key Points to Remember:

The My settings page can be accessed by clicking on the corresponding tab under .

The My settings page features the following cards:

Creating an internal database VCF file

The internal database VCF file is produced by our support team.

Please refer to the linked for recommendations on sample selection as well as the technical details and limitations involved in constructing internal historic and noise databases.

You can set a Preset group as default. The case is assigned to the default Preset group if no Preset group is selected or the default value is selected in the Preset group selection step.

1. Click on the dropdown arrow;

2. Select the Preset group;

(an access key ID and a secret access key) used to connect to the default AWS S3 storage bucket: create, deactivate, activate or delete.

The PON file (combined counts file) is not stored within Emedgene but within the customer storage location, which means that the file is retrieved each time a new case using the associated test kit is processed.

NOTE: If the combined counts file is removed from the storage location, then new cases created with the associated test kit, or reanalysis that includes secondary analysis, will have an error and go to status of "Issue Reported".

If PON file is deleted from storage, but customer is now using a different DRAGEN version or Ref Genome build version that has another PON, then that new file will be used without case failure.

• Set the Gene list threshold for your organization. Case validations will not be applied for cases with gene list containing fewer than the gene list threshold.

• Default value for the Gene list threshold is 50.

• Values above 0 and whole numbers are accepted.

The AI shortlist prioritizes variants, helping you focus on the most relevant candidates.

In the AI Shortlist module card, you can select an AI shortlist mode for rare disease analysis and one for carrier analysis to match your workflow. Once a mode is changed, it will apply to all new case analyses, including reanalysis.

Rare disease analysis

Emedgene offers two analysis modes for rare disease interpretation.

• Focused Mode: The list includes only variants found in genes known to be associated with disease. Variants that are highly ranked but located in genes of unknown significance (GUSs) are labeled as Most likely GUS and Candidate GUS.

• Discovery Mode: The list is not restricted to variants in known disease-associated genes. Variants in both known and unknown genes are evaluated and ranked together in the same list.

Carrier analysis

The AI shortlist will prioritize variants reported as pathogenic or likely pathogenic in any known variant databases or variants with high severity.

• Known Pathogenic: The AI shortlist will prioritize variants reported as pathogenic or likely pathogenic in any known variant databases.

• High Severity: The AI shortlist will prioritize variants with high severity.

• Both: The AI shortlist will prioritize variants reported as pathogenic or likely pathogenic in any known variant databases or variants with high severity.

Use the Organization DB Management card to manage organization databases—custom variant datasets that support variant interpretation. Here you can:

• Review database details in a table:

  • Database name and type, file, genome reference, variant type, date last edited

  • Active (On/Off): Indicates whether annotation from the database is applied to variants in new and reanalyzed cases

  • AI Shortlist (On/Off): Indicates whether the AI Shortlist factors in allele frequencies from the database. When enabled, variants with an allele frequency above 10% are excluded from the list

  • For CNV databases: Candidate (C) and annotation (A) used for comparing CNV variants in cases vs. the database

• Search: Use the search bar above the table to look up a database by its name

• Download: Click the Download icon on the right to export a database as a VCF file

• databases (requires special )

• database settings (requires special )

The Network feature allows you to share case data with collaborators according to the network's sharing policy. By subscribing to a network, you agree to share you cases' data with all network collaborators based on these policies.

You can view the level of sharing for each network in the Network table. For any questions or requests regarding a network, please contact the network admin.

The Network page is accessible via the dropdown menu under Settings.

The dedicated network manager can:

• networks;

• data sharing policies for each network;

• or networks.

Network table

Network details and controls:

1. Network name: Displays the name of the network you are subscribed to.

2. Restricted: Shows which data fields are shared under restricted sharing mode, according to , typically basic case metadata such as date, tags, and case type.

3. Extended: Displays which data fields are shared under extended sharing mode according to

Collaborator details

When you click on a Network's entry in the table, details for each collaborator are displayed:

1. Collaborator: Displays the organization name

2. Person of contact

3. Region: Presents the AWS region and cloud infrastructure (v100.39.0+)

Connect a new storage, edit its details, check connection, or delete a storage. Please check out a dedicated Managing data storage section for instructions.

The default page upon entering a case can be customized. You can choose between:

1. Candidates - default option;

2. Analysis/filters

3. Analysis/presets

Click on user avatar at the rightmost corner of the top navigation panel to open the Settings dropdown menu. From there, you can enter:

My Settings

Add or edit your professional credentials, profile picture, contact information and organization's details, and review roles and permissions granted to you.

, custom , , create, edit and delete , set how long until a case becomes "stale", and assign user groups.

Add, activate and inactivate users and manage .

networks, for each network, and networks.

for your organization, , and (the latter is only for ILMN cloud users).

Next to the user's initials or profile picture is a question mark Help icon. Clicking on this icon expands a dropdown menu with the following options:

• Help Center: Access the user manual and learn how to leverage powerful features to serve your patients or advance your research

• What's New: check out the new features, enhancements, or any other updates related to the platform

• Coming soon: Walkthroughs & Feature Requests

Enable case access for our support team via a slider button to provide billing, administrative, and support services. You can also disable it afterward to ensure maximum security.

By default, Emedgene displays the Case ID in the "EMGXXXXXXXXX" format. However, you have the choice to use the Proband ID instead.

Set up webhooks. You might need assistance from your IT team.

You can customize the default order of Variant table columns. Just rearrange the columns by dragging and dropping them to your preferred sequence.

Emedgene supports data encryption with customer-managed keys through . This gives organizations full control over their encryption and helps meet compliance requirements for data protection regulations such as HIPAA and GDPR.

Encryption is managed through a Key Management Service (KMS)—a secure system that creates and controls cryptographic keys. Currently, is supported, and will be available soon.

Starting in v100.39.0, users with appropriate can configure encryption for their workgroup directly in the platform using a key from Azure Key Vault KMS.

In Illumina environments, user access is managed through the AWS Identity and Access Management (IAM) service.

The smallest unit of access control in in Illumina environments is an . An IAM role is a specific set of IAM scopes that determine the user’s access level.

Adding a new user

1. Information

Displays organization details such as Cloud region, Organization name, Organization URL, Organization ID and the Platform main version.

The Emedgene SV annotation pipeline integrates multiple structural variant databases, including allele frequency sources (e.g., gnomAD SV) and variant/region pathogenicity resources (e.g., ClinVar, ClinGen). Annotation is performed based on defined overlap thresholds tailored to the clinical significance of each database category

Present under Workbench & Pipeline in Organization Settings, this module enables configuration of annotation overlapping thresholds for structural variants with external and internal databases.

After a database VCF file is created by Illumina Support or provided by the customer, it is stored in a dedicated customer bucket. To activate the database, users with appropriate need to register the database.

Quality parameters section allows users to configure quality thresholds for the organization. Users with 'Manage Quality Parameters' role can edit the thresholds.

For legacy Emedgene environments, user access is managed within the platform via the User Management tab in Settings. The smallest unit of access control in in legacy Emedgene environments is an .

The User management tab enables organization administrators to in legacy Emedgene environments.

Administrators can:

• Add new users and assign specific Emedgene roles.

Add /version to the URL if you'd like to check the current version of your environment. For example, for demo.emedgene.com, use demo.emedgene.com/version.

If you want to learn more about the different versions available, check .

Panel of Normals (PON) is a a set of matched normal samples to determine a baseline coverage pattern and account for recurrent technical artifacts that are specific to your workflow. Depth of coverage per each sequenced region is averaged across PON samples; if a significant increase or decrease from this baseline is detected in a test sample, a CNV is called.

A PON linked to a coverage BED kit is used by DRAGEN to increase accuracy of copy number variation (CNV) calling in targeted panel and exome cases.

A PON file is typically a text file listing absolute paths to 'target counts' files of individual matched normal samples. However, a PON can also be in the form of a combined counts file, which is a column-wise concatenation of individual target counts files (either GC-corrected or not).

The Illumina BaseSpace Baseline Builder App can generate this combined counts file (see "Creating a PON for Emedgene using BaseSpace Baseline Builder").

Recommendations for creating a PON to call CNVs from exome data:

1. Samples for a PON should be derived from healthy individuals.

2. In our experience, a PON of at least 40-50 samples yields the best results. A smaller PON is better than nothing, but keep in mind that you may encounter more false positives.

3. You should aim at preparing samples for a PON in a unified manner to avoid the batch effect. Please log differences in library preparation (if any).

Here you can review and manage filter Presets for your organization. The presets table includes:

• Preset name

• Type

• Last update (v100.39.0+): Displays the date of the most recent change

The table is sortable by any column (available from version 100.39.0 onwards).

Create Preset

Upon clicking on Add new, you will be redirected to Analysis tools page where you can .

Review logic behind the Preset

Click on an downward arrow icon left to the Preset's name.

Duplicate Preset (v100.39.0+)

1. Locate the preset you would like to duplicate and click on the new Duplicate icon;

2. When duplicating a preset, a new preset name must be entered. The naming and creation process follows the same limitations as creating a new preset .

Note: the duplicated preset will inherit the original preset's configuration. Note: this function is available to users with the existing role permissions.

Edit Preset

1. Click on the ✏️Edit icon;

2. Make the necessary changes. Editing the Preset requires basic understanding of JSON data format;

3. Click Save. The software will perform schema validation on the edited Preset. Note: you can modify the Preset's content but not its name. Note: the Preset can't be edited if it's .

Lock/unlock Preset

Locking the Preset prevents any user from changing it.

1. Click on the Lock/Unlock icon;

2. Click on Lock/Unlock in the popup window.

Delete Preset

1. Click on the 🗑️Delete icon;

2. Confirm your decision by clicking Delete in the popup window.

Note: Only unused Presets can be deleted.

As part of the management settings, users can upload BED files to be associated with their kits.

The format of the BED file (.bed) is following the description from UCSC Genome Browser with some modifications:

• The file has to be a tab-delimited text file;

• The file should not contain headers;

• The number of fields per line must be consistent throughout any single set of data.

• Zero-based index: Start and end positions are identified using a zero-based index.

There are three required fields:

1. Chromosome - The name of the chromosome has to be sorted in alpha-numeric order. example: chr1, chr2, ..., chr12, ..., chr22, chrX, chrY, chrM.

2. Chromosome Start - The starting position of the feature in the chromosome. The start position has to be smaller than the end position. The data has to be sorted in numeric order.

3. Chromosome End - The ending position of the feature in the chromosome. The end position has to be greater than the start position. The data has to be sorted in numeric order.

To add a region name to a BED file, include it as an additional column. This name will be displayed in place of the actual region location for insufficient regions, making it easier to review these areas on the Lab page.

Example:

Test Settings

1. Case Labels Create, edit, and remove Case labels that haven't been in use. Once you're done, don't forget to click Save at the bottom of the Test Settings section.

2. Gene lists Create gene lists from scratch or from existing ones, edit, remove, hide, unhide, and download. Adding and removing genes to the list is only available for gene lists that haven't been in use; editing the list name is not restricted.

Organization databases (DBs) are custom variant datasets that by adding population-specific frequencies, detecting technical artifacts, and referencing curated variants.

Organization database types

Manage BED files in the BED files card.

These files define your kits and determine which genomic regions are analyzed during case processing. BED files should follow the format as described .

Kit BED file applications

Select a platform version for your organization from the options available in your region. Changes may take 1–15 minutes to become active.

Users must link a PON to an existing Coverage BED by specifying the kit ID. Once the PON is created, to trigger CNV calling, you must provide the corresponding Coverage BED during case creation.

A PON Management section is available in the Organization Settings page under Kit management.

This section includes a table displaying:

• Kit Name

• Kit ID

• GC Corrected Status

• Human Reference

• DRAGEN Version

• Maximum Interval Size

Additionally, there is an ‘Add PON’ button to initiate the PON addition process.

Prerequisites

Before adding a PON, ensure the following requirements are met:

• The PON file must be a combined counts file.

• The file must be stored in a supported cloud storage (AWS S3, ICA, or BaseSpace Storage). Direct uploads are not allowed.

• Users must have the “Manage PON” role.

Compatibility

To use the PON for CNV calling, the sample pipeline version must be set to 37.0 or higher. Otherwise, CNV calling will not use the newly added PON.

Existing PONs

Previously existing PONs will not appear in the PON table. However, a notification will indicate the presence of existing PONs within the workgroup.

PON Migration

Previously existing PONs will continue to function, and CNV calling will remain unaffected. If migration to the new PON table is required, please contact [email protected] or your bioinformatics support team.

Adding a new PON

1. Click ‘Add PON’ to open a pop-up window.

2. Select values for the required fields:

   1. Coverage kit: Lists all unique kits within the organization (excluding common kits). To add a PON for a common Kit BED, create a separate Kit with the same Kit BED (refer to Kit Management for details).

1. On next window select a combined counts file from a supported cloud storage service (AWS S3, ICA, or BaseSpace Storage). Ensure the file is available in storage before proceeding.

2. Only one file with the extension “.combined.counts.txt.gz” can be selected.

3. Click ‘Next’ to validate the file.

Automated Validations

Before adding a PON, the system performs the following checks:

1. Maximum Interval Size Validation

• The system inspects the first 1000 rows of the combined counts file to ensure that no target exceeds the threshold interval size.

• Using a combined counts file with the expected maximum interval size is strongly recommended.

2. GC Correction Validation

• The system determines GC correction status by checking the cnv-enable-gcbias-correction field in the file headers:

  • 0: Non-GC corrected

  • 1: GC corrected

Viewing Added PONs

Once a PON is successfully added, it appears in the PON table with details based on the selected inputs. Value for Maximum Interval Size and GC Corrected Status are inferred from validation results.

Deleting PON from table

If user wants to delete a PON for a combination listed in table for any reasons, then user with role “Manage Pon” shall be able to delete it. This deletion will be a soft deletion i.e. linkage between combined counts file and Kit BED will be removed and there will be no impact on combined counts file itself.

Emedgene users can supply their own panel of normal (PON) to enable CNV calling on gene panels and WES samples. This guide details the process using BaseSpace.

The DRAGEN Baseline Builder application in BaseSpace can be used to build PONs that are compatible with Emedgene:

Users without BaseSpace can receive a free trial BaseSpace account along with compute (250 iCredits) and storage (1 TB) by registering here (https://basespace.illumina.com/). The compute is allocated for 30 days upon trial commencement and will be sufficient to generate a PON.

Alternatively, Illumina Connected Analytics (ICA), DRAGEN servers and DRAGEN in cloud are also capable of generating Emedgene-compatible PONs.

Requirements

• ~ 50 normal samples 50/50 male:female split, originating from the same library prep protocol, ideally from the same sequencer.

• The sample fastq files need to be in one or more Projects in your Basespace account.

• iCredits

Uploading a BED file:

The BED file used must match that uploaded to Emedgene, any discrepancy may cause a failure during case processing.

Uploading a BED file to a project in BaseSpace can be done within the project:

Note that hg19/grch37 BED files are not directly compatible with hs37d5 (the reference used within Emedgene) and will require their region contigs being renamed from "chr1", "chr2", "chr3"... etc. to "1", "2", "3" etc.

Running the app

1) Select the application "DRAGEN Baseline Builder" with the latest version that matches your Emedgene secondary analysis pipeline (4.3 in example) and click launch:

2) Select output project and Baseline Mode "CNV":

3) Select input FASTQ Biosamples to use.

50 samples is a rough guideline as the degree of correlation between normals and case sample is more important than quantity.

4) Select correct reference genome, matching that used in Emedgene.

Chose the multigenome version of the genome build.

For GRCh38/hg38:

For GRCh37/hg19 choose the hs37d5 build (remember to rename contigs in BED file if using hs37d5):

5) Select BED file you uploaded to your Basespace project in Step 2.1 above:

6) Configure Emedgene-specific settings:

Emedgene CNV calling has DRAGEN settings that must also be used during PON creation. In the app, this can be done in the advanced settings section at the bottom of the page:

Tick "Ignore Duplicate Reads in CNV Baseline Files" and change "Generated Combined Counts file for CNV" to "GC Corrected" 7) Tick the BaseSpace Labs App Acknowledgement box.

8) Launch the app.

Downloading Results

Once the analysis is complete, open the output files and look for the *.combined.counts.txt.gz file. It may be within a folder called "pon".

It can be downloaded from analysis results manually, by clicking on the file and selecting "Download", via the Basespace CLI (https://developer.basespace.illumina.com/docs/content/documentation/cli/cli-overview), or if the output project is connected to Emedgene - loaded directly into the platform.

Users with a 'Manage AI ACMG' role have the ability to exclude PP5 and BP6 tags from ACMG classification. These tags rely on external assertions without independent evidence and may introduce bias (Biesecker et al. 2018).

This option is enabled by default.

• When enabled: PP5 and BP6 tags will not be used in ACMG classification. If either tag is positive (by AI or manual input), a warning message will be displayed in the ACMG section of the variant page.

• When disabled: The tags will be included, and no warning sign will be shown.

Prerequisites

Format: Database file must follow VCF 4.2 specifications.

Tools:

• Required:

  • awk

  • bgzip

• Optional:

  • vcftools Useful for population frequency calculations.

How to create a noise or historic database file

Example historic DB VCF header and variant line

How to create a curated database file

Example curated DB VCF variant lines

Small variant

Copy number variant

Next steps

Upon request, Illumina Support builds internal historic or noise database VCF files from the customer’s cases based on supplied requirements.

What data is tracked for each variant?

For each variant, internal historic database records:

• Allele frequency (AF): The ratio of the number of variant alleles to the total number of alleles in the dataset

• Allele count (AC): The number of observed variant alleles

• Allele number (AN): The total number of alleles assessed, derived from the number of individuals

• Genotype counts: heterozygous (HET), homozygous (HOM), and hemizygous (HEMI)

• Sample list (TEN): A short list of samples where the variant was found

How are variants merged across cases?

Historic database

Noise database

Important notes and limitations

Variant quality

No quality filters are applied; all variants are retained in the database regardless of confidence or quality.

Duplication events

CNV callers often report copy number (CN) without specifying zygosity. In Emedgene, the following assumptions are applied:

• CN = 3 → Interpreted as heterozygous duplication

• CN > 3 → Interpreted as homozygous duplication

When samples contain different CN values for the same region, they are merged into a single DUP entry. The counts for this entry are then calculated based on the inferred zygosity rather than the exact copy number.

Sex chromosomes

Chromosome X

Allele counts depend on the sample’s recorded sex:

• Females: Diploid — contribute one allele to the allele count (AC) if heterozygous, or two alleles if homozygous

• Males: Haploid — contribute one allele to AC

• Mosaic chrX variants in males: Treated as heterozygous, contributing one allele to AC

Chromosome Y

Haploid; only samples recorded as male contribute to allele counting.

Pseudo‑autosomal regions (PAR)

No special handling is implemented yet.

Mitochondrial DNA variants

Heteroplasmy levels are not taken into account; mitochondrial DNA variants are treated as homoplasmic and therefore counted as haploid homozygous in the dataset.

No data regions

The issue

The merging algorithm does not account for sequencing coverage and incorrectly interprets positions with no data as homozygous reference calls. As a result, the dataset becomes artificially enriched in reference alleles, leading to an underestimation of allele frequency (AF) for any variant located in regions with absent coverage.

This applies to any region with absent coverage in any dataset (exomes only, genomes only, or mixed). However, it becomes particularly problematic in mixed exome–genome datasets, where exome samples systematically lack data outside the capture regions (see below).

Special case: Non-coding variant allele frequency in mixed exome–genome datasets

When exome and genome samples are combined in a single dataset, AF values for non‑coding variants become systematically lower than their true population frequency. This occurs because exome samples have no coverage in non‑coding regions, and the merging algorithm incorrectly interprets these missing data points as homozygous reference. As a result, variants located outside the exome capture regions—such as common intronic or intergenic variants—are underrepresented in the aggregated dataset.

The downward AF bias becomes especially pronounced when exome samples greatly outnumber genome samples:

• Exomes outnumber genomes by ~20× Common intronic variants may appear at an artificially low frequency (<0.05), despite being frequent in the population.

• Exomes outnumber genomes by ~100× or more The bias becomes so severe that common non‑coding variants may appear at <0.01 AF, leading to potential misclassification as rare variants.

Here you can review and manage Preset groups.

Preset groups can be created by combining different Presets. Alternatively, you can upload a JSON file that defines the Presets in a Preset group. The file name and schema will be validated upon upload.

The section has two tabs:

• V2 (new)

Here you can create (from Presets/from a JSON file, view, edit, hide/unhide and download your organization's Preset groups.

• V1 (legacy)

Here you can view and download legacy Preset groups.

Migrating V1 Preset groups to the improved V2 methodology

Legacy Preset groups can be migrated to the new methodology via two simple steps: downloading the Preset group JSON file in the V1 (legacy) tab, then uploading it on the V2 (new) tab.

Create Preset group from existing Presets:

1. Click on Add new;

2. From the dropdown, select New;

3. Enter a name for the Preset group. Note: The Preset group can't be renamed later!

Create Preset group from JSON file:

This is the second step in the migration of the V1 (legacy) Preset group, after you have downloaded the Preset group JSON file.

1. Click on Add new;

2. From the dropdown, select From file;

3. From the file browser, select a JSON file that defines a Preset group;

4. The system will thoroughly validate the file;

Review contents of the Preset group (V2 and V1)

Click on an downward arrow icon left to the Preset group's name.

Edit Preset group

1. Click on the ✏️Edit icon; 2a. Add Presets to the group as needed. Select Presets to include in the Preset group from the dropdown. Click Add after selecting each Preset; 2b. Remove Presets from the group as needed. Click on the Remove icon to the right of the Preset name. 2c. Drag and drop Presets to change the order;

2. Click Save.

Hide/unhide Preset group

When you hide a Preset group, it will no longer appear in the Preset groups list offered at case creation (Select preset group step).

Click on the 👁️Hide/Unhide icon.

Note: A default Preset group cannot be hidden.

Download Preset group file (V2 and V1)

This is the first step in the migration of the V1 (legacy) Preset group to V2 methodology. A Preset group file contains preset names and the filters used to define each Preset in JSON format.

Click on the ⬇️Download icon.

Revert Preset group (V1)

If a V1 Preset group that has undergone migration is set to revert, the corresponding V2 Preset group will be deleted.

Click on the ↩️Revert icon to undo the migration.

Note: If a migrated Preset group has been assigned as default, it cannot be reverted.

Users with appropriate can tune database settings within their organization.

The Pipeline version card, located in the Workbench & Pipeline section of Organization Settings, allows you to set the applied versions for the sample pipeline, case pipeline, DRAGEN, and human reference. You can also configure whether to include reference homozygous genotype calls in cases.

Sample

Set the organization sample pipeline configurations. These configurations are only applicable for cases starting from FASTQ files.

• Human Ref- Set the used human reference genome build to apply in the pipeline, used for alignment and calling. possible options: GRCh37 and GRCh38.

• Secondary Analysis Pipeline- Set the used secondary analysis pipeline for your organization. This pipeline shall be applicable for all FASTQ samples that are part of the case.

• DRAGEN Version- Set the used DRAGEN version utilized in the Secondary analysis pipeline.

Variant caller mapping

Select the variant callers enabled for your secondary analysis pipeline.

Each caller is annotated with its sequencing compatibility (WGS, WES), methodology (e.g., CNV read-depth, small variant, SV split-end), and compatibility requirements across sample, DRAGEN, and case pipeline versions. By default, SNV and CNV callers are always enabled. Additional callers, such as SV, SMN, and STR can now be selectively activated to match evolving analysis needs. Changes made through this interface will apply to newly processed cases after changing selected callers.

Enabling variant caller that is not compatible with the selected pipelines is not supported.

Some variant callers may require an assigned PON file to apply properly for WES sequencing type.

Sample pipeline arguments

View your Sample pipeline arguments for mapping and calling. In order to modify these arguments, you will need to contact technical support.

If the arguments are not set, they will be displayed as ‘N/A’.

Case

Set the organization case pipeline configuration. This configuration is applicable for all cases in your organization.

Changing the case pipeline will affect the used annotation sources and version for your cases and may impact AI shortlist outcome for your case.

Include Reference Homozygosity (v36.0+)

The Include Reference Homozygous & No Coverage calls option allows you to control whether reference homozygous and no coverage genotype calls are included for the proband in newly processed cases. When enabled, cases will include these calls; when disabled (default), they will not.

The Variant tags card allows organizations to add and delete custom variant tags.

Add a variant tag

Delete a variant tag

In Emedgene, the smallest unit of access control is an Emedgene role in legacy Emedgene environments and an IAM scope in Illumina environments. Each IAM scope corresponds directly to an Emedgene role, both defining the same set of access permissions.

The table below lists IAM scopes / Emedgene roles and maps each of them to the predefined v38.0 IAM roles:

• Director (+v38)

• Full Access User (+v38)

• Analyst (+v38)

• IT Team (+v38)