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.

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 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.

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 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.

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

Case statuses help teams:

• Monitor case progress

• Track ownership

• Maintain workflow consistency across the organization

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

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

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

• panel, under Case-related activities – to track status log

There are out-of-the-box statuses provided by the platform and the option to create custom statuses to suit your case review workflow. Go to Settings > Management > to create, remove, or reorder case statuses for your organization.

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

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

Options available through the Top bar:

• Change the Case status

• Reanalyze the case

• and write interpretation notes

• Preview the

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 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

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

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

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

Sample-level for all samples in a case can be downloaded by clicking the download icon next to the Sample quality section title.

For NGS cases, the report includes coverage and mapping statistics.

For array cases, metrics include array QC values such as call rate, autosomal call rate, and Log R dev.

The Emedgene Workbench offers a wide array of dynamic filters to help reveal or limit variants that are the most relevant to your clinical case. Each filter contains multiple options to customize the case review process according to your organization's best practices.

The Filters/Presets panel includes:

• : Quickly locate specific variants within a case by applying customized filters. Located on top of the Filters/Presets panel.

The Analysis Tools tab includes features to support case review:

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

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

The Zygosity Filters enable manual selection of for each sequenced sample in the pedigree.

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 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 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.

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.

You can expand to a broader genetic testing option if the results of more targeted testing are inconclusive. You may reflex from Custom Panel to Exome or Genome, or from Exome to Genome.

To do this, you should change the Case type in Edit case info flow, thereby rerunning using the broader analysis.

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

The Individual case page includes:

1. Top bar—displays a Case ID and Case status 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

3. —illustrates quality metrics for the sequenced samples

4. —provides an interactive overview of genomic structure, ideal for analyzing CNV and ROH/LOH events

5. —provides numerous customizable filters to help you explore the total list of genetic variants in compliance with your organization's standard case review process. You can export shortlisted variants in .xlsx format

6. —documents versions of all the resources used during case analysis

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

Contamination is detected using Peddy 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.

Contamination check results:

• N/A No data is available (older cases or when idr_baf = 0.000).

• No No contamination detected (idr_baf < 0.200).

• Unlikely Possible contamination, but evidence is weak (0.200 ≤ idr_baf

The table view is customizable:

• Columns 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

• You can choose between comfort and compact view by clicking the corresponding button

All modifications are automatically saved for each individual user and retained until new changes are made.

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

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.

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.

New mode (v100.39.0+)

New mode introduces a dual-view interface that combines the variant table with an IGV-based visualization component.

Users can enable the New Analysis tools viewing mode using the toggle in the upper-right corner of the screen. When the New mode is activated, a new window appears on top of the variant table in the lower half of the screen. The window includes the Variant visualization tool, same as available in the Visualization tab of the Variant page.

Visualization window features

• The window can be resized by dragging its upper border

• Use the Show visualizations and Hide visualizations on top of the variant table to toggle the visualization component on or off.

Table and visualization window interaction

• Single-click on a variant row zooms the visualization to that region (Note: In Legacy mode, single-click opens variant page)

• Double-click or click the Open link (visible in the leftmost column on hover) to open variant page

• Once a variant is open in visualization window, use the up/down arrow keys to move through the variant table

Legacy mode

Legacy mode does not include access to the IGV-based variant viewer.

Modes and options

Display No Coverage slider

The Gene Filters allow filtering variants by specific genes or regions based on clinical relevance, functional significance, or targeted sequencing design. These filters help prioritize variants in genes associated with disease, dosage sensitivity, or coding regions.

Filter options

Default values

When are reset to default, Gene Filters remain disabled, except for cases launched in Virtual Panel mode. In such cases, the Candidate Genes filter is activated by default.

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:

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 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.

The Analysis Tools tab includes features to support case review:

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

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.

NGS case

Option 1: FASTQ case

Each phenotype observed in the proband is compared to the phenotypic profile of the selected disease. Based on how closely each proband phenotype matches the disease phenotype profile, a similarity level is assigned.

On the and in the Variant page , each HPO term listed for the proband is marked with an icon that shows how similar it is to the phenotypes reported for the disease.

Phenotypic similarity levels

Exact match

The Emedgene Workbench offers a wide array of dynamic filters to help reveal or limit variants that are the most relevant to your clinical case. Each filter contains multiple options to customize the case review process according to your organization's best practices.

The Filters/Presets panel includes:

• : Quickly locate specific variants within a case by applying customized filters. Located on top of the Filters/Presets panel.

Proprietary phenotypic match models assess how closely a gene’s associated disease phenotype matches the patient’s phenotype profile. A degree of similarity is resembled by the phenotypic match score. The phenotypic match score indicates the degree of similarity between the patient’s phenotype set and the phenotype set of the best-matching gene-related disease.

If a gene is linked to multiple diseases, the model compares the patient’s phenotypes with each disease profile individually. The highest score determines the best match, and this disease is automatically displayed in the Evidence graph. The Phenomatch and Phenomeld scores shown in the variant table correspond to this best-matching disease, even if the associated disease is manually updated.

The Versions tab provides a snapshot of all tools, data sources, and knowledge bases used during the analysis of a case. This feature supports traceability and reproducibility by documenting the exact versions of each resource involved in variant interpretation.

Reported categories

Annotation tools

This section lists automated tools used for variant-level annotation. Using computational models and predictive algorithms, they generate scores or classifications that help assess the potential impact of each variant.

Emedgene resources

This section includes Emedgene proprietary models and components that support variant prioritization and interpretation:

• ACMG tags algorithm

• AI Shortlist model "Tier v0" indicates focused mode, and "Tier v2" indicates discovery mode of the AI Shortlist model for rare diseases.

• Emedgene knowledge base

• Phenocompare model

Input data

This section documents the input files used in the tertiary analysis pipeline. These include:

• Quality BED file—Defines regions used to evaluate sample quality

• Region of interest BED file—Specifies genomic intervals included in tertiary analysis

• Sample VCF files—Contain the variant calls generated during upstream analysis; associated individual

Knowledge base sources

This section documents the curated databases used to provide gene-level and disease-related information.

Organization local databases

This section includes organization-specific datasets integrated into the analysis.

Population databases

This section documents the variant frequency databases used to assess variant rarity and support pathogenicity classification.

Reference data

This section documents the reference genome assembly name and reference genome GenBank assembly accession number.

Variant databases

This section includes databases that support classification by providing information on previously observed variants.

The Polymorphism Filters enable filtering variants by alternative allele frequencies and genotype counts in public and internal databases.

The Polymorphism Filters can operate in a or mode.

Exporting annotated variant data facilitates collaboration and record-keeping. To start the download, click the Export icon at the top of the Analysis Tools tab.

The XLSX export file contains only the variants visible after applying filters. If your filtered list has more than 1,500 variants, the export file will include only the first 1,500.

Export file includes the following data:

The Evidence & Tags filters focus variant review by surfacing variants with relevant supporting evidence, annotations, tags, and review status, making decision‑making faster and more consistent.

The Evidence & Tags filters can operate in a or mode.

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.

The Presets feature in the Analysis Tools tab lets you save and reuse filter combinations that match your lab’s workflows and Standard Operational Procedures (SOPs). Instead of setting filters from scratch each time, you can apply a Preset to instantly narrow down variants according to predefined rules. Presets can be created from active filters or gene lists, and managed at the organization level for consistency across all users.

Creating Presets from active filters

To save Presets from active filters:

In Emedgene, case status indicates the current stage of a case—from data upload through analysis, review, and results finalization. Statuses are assigned either automatically by the system or by authorized users, depending on the workflow stage and user permissions.

Different statuses require different IAM scopes/Emedgene roles for assignment and reassignment.

Case statuses are grouped into three categories based on control type:

• System-controlled: Assigned automatically by the system; cannot be reassigned by users.

• User-controlled: Assigned and reassigned by authorized users.

• System-assigned, user-reassignable: Assigned automatically by the system but can be reassigned by authorized users.

Case statuses by origin:

• Out-of-the-box: Default options provided by the platform.

• Custom: to align with specific workflows.

Case lifecycle

Each status represents a distinct stage in the case lifecycle. Figure 1 shows the possible transitions between statuses and the control type for each assignment, indicated by solid and dashed arrows. Table 1 provides an overview of case statuses.

Table 1. Case status reference

In some cases, you may need to record variants that were not included in the uploaded VCF or not called from FASTQ data. This is particularly useful when:

• You are complementing NGS results with findings from other genetic tests (e.g., long-read sequencing, optical mapping, CGH, SNP array, karyotyping/FISH, repeat-primed PCR, MLPA, Southern blot, etc.)

• You wish to report on adjacent CNV calls as a single CNV event.

• You wish to report a set of adjacent variants together as a single multi-nucleotide variant (MNV).

Manually adding variants ensures that all relevant findings are visible in the case review and can be considered during interpretation.

Currently supported variant types: SNV, CNV, UPD, ROH, and STR.

To manually add a variant:

1. Open the Analysis Tools tab.

2. Click the plus (+) button in the top-right corner.

1. In the Manually Add Variant window, select the variant type: SNV, CNV, UPD, ROH, or STR.

2. Fill in the details based on the selected type:

1. Click on Create Variant to add it to the case.

Viewing manually added variants

• Manually added variants have a blue frame and are clearly marked with the label “Manually added variant” on the Variant page.

• These variants differ from pipeline-called variants:

  • Quality and Visualization sections are not available.

Sorting and formatting notes

• STR variants that are added manually do not fully align with the pipeline-called STR format. For example, variant length is not displayed.

• Manually added variants cannot be sorted by columns that do not apply, such as AI Rank.

Filtering manually added variants

To view only the variants you have added manually:

• In the Evidence & Tags Filters section, select Manually added variants.

This helps you focus specifically on variants that were entered outside of the automated pipeline.

Use the Filters tab in the Filters/Presets panel to narrow numerous variants down to those most relevant to your case.

Filter categories

Filtering modes

, , , , and can operate in either a simple or advanced mode.

Use simple mode for quick, high-level filtering. For more detailed filtering with expanded options, choose advanced mode to gain deeper control over filter parameters.

Filters actions menu

When the Filters tab of the Filters/Presets panel is selected, click the vertical ellipsis (⋮) in the top-left corner to access the following actions:

• Clear: Clears all filters and displays all variants in the case

• Reset to default: Clears all filters and applies the :

  • Quality: Moderate/High

Use the Filters tab in the Filters/Presets panel to narrow numerous variants down to those most relevant to your case.

Filter categories

If you need to update your case details, adjust phenotypes, change the gene list, or customize disease information for reporting, Emedgene allows you to edit an existing case directly from the interface. Depending on what you modify, some edits may prompt reanalysis, while others will simply be saved without triggering a new run.

The Genes coverage section helps you quickly identify parts of genes that may not have been adequately sequenced in your case. This insight is particularly important when assessing sequencing quality, interpreting uncertain findings, or deciding if further validation is needed.

While variant callers provide base-by-base coverage, Emedgene simplifies the view by showing average coverage per region. This makes it easier for you to spot undercovered genes at a glance, even when individual positions may appear sufficiently covered. By smoothing out local fluctuations, average coverage helps you prioritize regions that might require further review and complements DRAGEN's fine-grained metrics with a broader, more interpretable view.

How coverage is calculated

Coverage metrics are generated differently depending on the type of input data used for your case:

1. FASTQ / BAM or gVCF-based cases

• If your case was started from FastQ/BAM or gVCF, coverage is inferred from gVCF reference blocks (also called GVCFBlocks).

• These blocks are segments of the genome where genotype quality (GQ) is consistent.

• A new block is created whenever there's a significant change in GQ, which results in a highly segmented and detailed representation of local sequencing quality.

2. VCF + BAM or VCF + BED-based Cases

• If your case includes VCF and BAM or VCF and BED, coverage is calculated directly from the aligned reads or from predefined BED intervals.

• Coverage is calculated as the true base-by-base average across the entire region.

• This method avoids the variability of gVCF segmentation and gives a precise coverage profile for each region.

Regions evaluated for coverage

Coverage is compared against expected regions defined in:

• Emedgene's reference BED file, or

• Your test’s custom KIT BED file

Each region is defined by:

• Chromosome

• Start & end positions

• Name and strand (Optional)

Coverage assessment

Emedgene uses the tool bedtools intersect to compare each expected region from the regions used for coverage assessment against actual read coverage. The system captures:

• How much of the region overlaps with sequenced data

• Depth of coverage per segment

Coverage statistics

Each region includes these metrics:

How to use the coverage tool

You can interactively explore gene-level coverage details using the Genes with Insufficient Coverage tab. This tool is currently available only for FASTQ-based cases.

Here’s what you can do:

• Search for a specific gene or a list of genes.

• Filter results based on coverage thresholds:

  • ≤0x

To check coverage for a gene:

1. Enter the gene symbol in the search box and select it.

2. Choose your desired coverage filter from the dropdown.

3. Review the results in the table or download the data.

To look up the coverage for multiple genes that are saved as a :

Click the Add Gene List button and select any of your pre-loaded gene lists.

To further filter regions:

• By maximum depth of coverage

  Select Coverage, then choose the highest allowable coverage value from the dropdown list,

• By percentage of bases covered >20×

  Select % of Bases Gt20, then choose the highest allowable percentage from the dropdown list.

Visual review in allows manual variant confirmation by inspecting aligned reads at specific genomic regions.

To inspect poorly covered regions of a gene in the desktop IGV browser:

1. Click on More details in the row corresponding to the gene of interest. This opens a pop-up with coverage details for the selected gene.

2. In the pop-up, select View on IGV to open the region in the IGV desktop application.

To download data

Click the Download button to export the full list of low-coverage regions as a *_insufficient_regions.tsv file. Each row includes region coordinates and all metrics.

Each row corresponds to one region and includes:

• Region coordinates

• Calculated coverage metrics

• Region length

Use this file to:

• Compare multiple cases

• Track sequencing gaps

• Plan confirmatory testing

• Share results with collaborators

Use Quality filters to refine variant reviews by setting quality thresholds and filtering based on calling methodology. This prioritizes variants that meet specific confidence metrics for interpretation.

The Quality filters can operate in a Simple or Advanced mode.

Simple mode

Advanced mode

Provides fine-grained control over multiple quality metrics depending on the variant type.

Quality

VCF FILTER

Calling methodology

Quality metrics by variant type: SNV, indel, MNV (v100.39.0+), and STR

Mapping quality (MQ)

Depth (DP)

Alternate read count

Allele bias

Quality metrics by variant type: SV, CNV, and LOH/ROH

Length

Bin Count

Default values

When are reset to default, The Quality filters are set to:

• Quality: Moderate and High

• Mapping Quality: ≥45

• Depth: ≥10

Finalizing a case locks it to preserve interpretation decisions and ensures reporting consistency. Once finalized, no changes can be made to variant tags, pathogenicity, interpretation notes, or case-level classifications unless the case status is reverted.

How to finalize a case

1. Open the Case Interpretation widget

   • On the Individual Case page, click on the Case Interpretation button in the top bar.

2. Indicate the case result

   In the Case Interpretation widget, select the final result of the analysis:

   • Confidently Solved (Positive)

   • Likely Solved (Positive)

1. Select variants to include in the Clinical Report

   • Use the tag filter dropdown to choose which tagged variants to include.

   • You can reorder variants by drag-and-drop. The order is preserved in the Clinical Report within each tag group and variant type.

Changes from v38.0+:

4. Add notes and interpretations

Within the Case Interpretation widget, you may add:

• Interpretation notes

• Gene interpretation (import gene annotation from Curate)

• Recommendations

These free-text fields are saved per case and will automatically populate in the Clinical Report if your lab uses Emedgene’s reporting solution.

5. Save your progress

Click Save at any time to ensure all changes are recorded.

6. Preview the report (optional)

• Click the eye icon in the top bar of the Individual Case page.

• Select a template and click Preview.

• Reports can be downloaded in .pdf or .odt format.

7. Finalize the case

• Change the Case Status to Finalized.

• Once finalized:

  • The case is locked from further edits.

8. Generate the final report

• Click the printer icon in the top bar of the Individual Case page.

• Choose Create New or select a previously generated report.

• Select a template and click Generate.

Viewing finalized cases

• For finalized cases, you can review the Case Result, Interpretation Notes, and Finalized Variants in the Finalize tab (right-hand panel).

• You can also see finalized variants by selecting Finalized in the dropdown menu on the Candidates tab.

With reporting solution provided by Emedgene, creating comprehensive Clinical Reports is a piece of cake.✨ All the relevant case- and variant-level information is automatically populated to the corresponding sections of the report.

Exemplary Clinical Report layout and information sources

Case Information

Includes (numbers indicate ):

1. Patient details: Patient's name [1], date of birth [2], sex [2] and MRN [1];

2. Technical sample details: Specimen's type [1] and quality [3], dates collected [1] and received [1];

3. Provider details: Lab number [1], ordering physician's name [1];

Results summary

Results summary gives a general overview of the test result:

1. Test result summary [4];

2. Secondary ACMG findings summary [4];

3. Interpretation summary [4];

4. Recommendations [4].

Detailed results

Detailed results highlight the genetic testing findings:

1. Basic sequence variant details:

   1. Gene [3],

   2. Genomic location [3],

Test details

Test details:

1. Test methodology [5];

2. Test limitations [5].

References

The References [9] section lists all the PubMed citations mentioned in the report. References will be auto-formatted if the PMID is supplied in the report.

Signatures

The Signatures section documents who and when generated the report [3].

Generating a Clinical Report

Data sources

[1] - API;

[2] - filled in while ; displayed in ; for non-finalized cases;

[3] - automatically inferred by Emedgene,

[4] - filled in in the Case Interpretation widget while ,

[5] - fixed text,

[6] - manually assigned in the Pathogenicity box of the of the Variant Page,

[7] - depends on the evidence generated on the ,

[8] - automatically or manually filled in in the Variant Interpretation notes of the of the Variant Page,

[9] - in any of the free text fields you can add PMIDs in one of the following formats: PMID1234, PMID 1234, PMID:1234.

The Evidence page presents the most relevant evidence supporting the automatic variant classification generated by the AI Shortlist. This includes ACMG classification tags, variant details, gene-disease relationships, phenotype matches, and supporting literature. You can also manually review and edit evidence to tailor it to your case.

The Evidence page is available for:

• Variants in the AI Shortlist (automatically)

• Any manually tagged variant in the analysis

Accessing the Evidence Page

You can view teh Evidence Page from:

Click on the variant bar to open its dedicated Evidence page.

In the Evidence section, click the See evidence button under the Evidence box.

View modes

You can switch between two display modes:

• Graph view: Offers an interactive visual of gene-disease-phenotype relationships, inheritance modes, and ACMG tags.

• Text view: Displays structured evidence and citations for use in clinical reports or further notes.

AI Shortlist collects data from credible studies and public databases in an internal knowledge base that maps complex connections between variants, genes, mechanisms, diseases, and phenotypes.

What's included in the Evidence Page?

Variant information

Displays the main effect, HGVS nomenclature, zygosity (HET, HOM, HEMI, or REF), and de novo status if applicable.

Gene information

Gene symbol, if the gene is tolerant to variation, and assumed inheritance mode in the case under review. This is suggested based on the observed level of genotype-phenotype co-segregation and inheritance mode of the genetic condition (reported or suspected).

In addition to the conventional inheritance modes (Autosomal Dominant, Autosomal Recessive, Compound Heterozygote Autosomal Recessive, X-Linked Dominant, X-Linked Recessive), the platform also employs Autosomal Dominant Partial Penetrance and Partial Autosomal Recessive designations.

Autosomal Dominant Partial Penetrance is used when the gene-associated condition is AD, and the variant is Het in the test subject and at least one of their parents. This suggests that the phenotypes may be due to incomplete penetrance of the genetic condition.

Partial Autosomal Recessive is used when the gene-associated condition is AR and the variant in the test subject is Het. This helps to account for the possibility that another causative variant is undetected - in the same (compound heterozygosity) or another (digenic inheritance) gene.

Disease information

Name of the condition as sourced from OMIM, literature, or other databases. When multiple diseases are associated with a gene, users can manually select the one most relevant to the case or choose to add a custom disease.

Patient phenotypes

Proband's phenotypes that match phenotypes reported for the suspected disease. Exact, indirect, and matches by ascendance are considered.

Unconfirmed disease phenotypes

Phenotypes reported for the suspected disease but not observed in the proband.

Unmatched patient phenotypes

Phenotypes observed in the proband but not known to be manifested as part of the suspected disease.

Links

Follow the links to the primary sources to explore the evidence further. The links are in the References section (text view) and are accessible by hovering over the arrows (graph view).

Edit mode

Click the pencil icon in the top-left corner of the graph to:

• Edit disease associations

• Add or remove evidence boxes

• Tailor interpretation to case-specific insights

Evidence Graph

This visual module links genes, diseases, and inheritance modes, providing an at-a-glance overview of your variant’s clinical context.

• Easily switch the associated disease for reporting or interpretation.

• The graph influences ACMG tags like PP4, PM5, and PVS1, depending on gene-disease pairings.

• Connects with literature via LitVar2 integration.

Evidence Notes

Add your own insights or justifications related to the variant, interpretation, or evidence decisions.

• Notes are tag-specific and can support audit trails and future reviews.

• Note changes are not logged in the Activity Panel—consider logging important insights as Comments instead.

Activity tracking for Evidence

All meaningful updates—including ACMG tag edits, classification changes, and disease graph modifications—are logged in the Activity Panel, ensuring full transparency for collaborative curation and audits.

How to use the Evidence Page

• Use Phenomeld scores (available as a filter and sortable column in Analysis Tools) to prioritize variants with high phenotype concordance.

• When editing any ACMG tag, always click Save before editing notes to ensure no data is lost (especially for finalized cases).

• Double-check that inheritance mode matches disease after editing the Evidence Graph.

The Evidence Page brings together automation, clinical reasoning, and interpretative flexibility, enabling users to make transparent, reproducible decisions in line with the latest guidelines—while giving you full control to adjust when needed.