VarSage is a bioinformatics decision-support platform for post-calling interpretation of small germline sequence variants. It is intended for exome or genome VCF/table outputs that have already passed through an upstream alignment, variant calling, and primary quality-control workflow. VarSage does not call variants from reads; it integrates existing variant calls with phenotype, inheritance, population, transcript, clinical-assertion, and computational evidence to produce an auditable prioritization dossier.
The application is organized around three analytical contexts: phenotype-driven rare-disease prioritization, couple-level carrier screening, and family-aware trio analysis. Across all contexts, VarSage treats the variant as a structured allele defined by chromosome, 1-based position, reference allele, alternate allele, selected sample genotype, and annotation fields. This deterministic coordinate model is deliberately kept separate from AI-generated summaries.
Single-proband analysis that ranks candidate alleles by phenotype fit, molecular consequence, rarity, inheritance compatibility, quality, and provisional evidence.
Independent partner review followed by same-gene comparison for recessive reproductive-risk signals and scope-specific carrier findings.
Proband-first prioritization followed by parental allele-state comparison for possible de novo, inherited, and compound-heterozygous models.
VarSage follows a layered interpretation architecture. First, submitted phenotypes and variants are normalized into controlled representations. Next, transcript and coordinate annotations are attached while preserving original source fields. The resulting candidate set is filtered for assay scope, quality, benign assertions, and population frequency. Finally, retained variants are ranked using phenotype similarity, inheritance compatibility, molecular consequence, rarity, clinical assertions, computational predictors, and provisional ACMG/AMP-oriented evidence.
The final report is not just a score table. It is an audit trail: which sample was analyzed, which file type was detected, which variants were removed, which databases matched, which HPO terms were used, which inheritance model was plausible, and which evidence remains incomplete. This is essential in clinical bioinformatics because the same numeric rank can arise from very different biological and evidentiary situations.
Use when an affected proband has WES/WGS variants and the primary question is phenotypic plausibility of candidate alleles.
Use when two partners are assessed for heterozygous variants in the same recessive disease gene or in configured carrier scopes.
Use when maternal and/or paternal variant files can inform de novo, inherited, or biallelic hypotheses in the proband.
The rare-disease workflow is a single-proband prioritization pipeline for suspected Mendelian disease. Its purpose is to reduce a large variant set to a biologically interpretable shortlist while preserving the evidentiary chain behind each retained candidate. The workflow explicitly distinguishes review priority from pathogenicity classification and separates variant-level evidence from case-level causality.
After ranking and optional synthesis, VarSage adds a cautious case-level decision spectrum. This summarizes whether the retained candidate set contains no positive candidate, an inconclusive candidate, a positive candidate requiring confirmation, or an externally confirmed cause. The decision is derived from structured evidence and report-review signals; it is not a molecular diagnosis.
Assigned when no retained candidate is available, or when the retained candidates do not contain pathogenic/likely-pathogenic or primary report-review evidence. This is not a statement that a genetic diagnosis is excluded.
Assigned when candidates remain important for review, but the current evidence is not strong enough for a cautious positive decision. The result should remain inconclusive or candidate-only until the uncertainty is resolved.
Assigned when a prioritized candidate has a pathogenic/likely-pathogenic provisional classification, a matching ClinVar-like assertion, or a primary report-review signal. Expert review, orthogonal confirmation, and relevant segregation or phase checks are still required.
This is a reporting endpoint for human interpretation, not an automatic VarSage decision. The tool never assigns it; it requires qualified expert review and laboratory confirmation where appropriate.
The main table orders variants by a composite score that combines phenotype fit, inheritance support, consequence, rarity, and quality.
Each row can expand to show annotation, filtering, phenotype overlap, literature links, and provisional ACMG evidence.
The workflow exports HTML, TSV, and Microsoft word results so the shortlist can be reviewed and shared outside the app.
The carrier-screening workflow is built for reproductive-risk review in a couple. It analyzes each partner independently, applies carrier-scope filtering, and then performs a gene-level comparison between retained heterozygous findings. This design prevents partner-specific evidence from being conflated while still making shared-gene signals immediately visible.
Each partner gets its own retained-variant summary, including panel genes, secondary findings, and X-chromosome review where relevant.
The couple-level table highlights genes where both partners still have retained heterozygous variants after filtering.
The workflow writes downloadable TSV, HTML, and Word outputs with separate findings for each partner and the couple summary.
The trio workflow extends proband prioritization with parental allele-state evidence. It is designed for family-aware interpretation: the proband is ranked first, and parental calls are then used to evaluate inheritance hypotheses. Missing parent files, low-quality parental calls, or absent coverage are treated as missing evidence rather than definitive evidence of absence.
The proband still gets a full rare-disease ranking table with phenotype, inheritance, and evidence context.
The trio summary separates possible de novo, compound-heterozygous, and causative candidate views from the parental comparisons.
Mother and father each receive their own structured summary, and the final exports include the trio-specific interpretation sections.
Every gene-bearing result view in the web application uses the same on-demand GTEx panel. Rare disease ranked candidates, Carrier screening same-gene warnings and partner tables, and Trio proband, inheritance-finding, compound-heterozygous, causative, parent-review, and literature-context tables all expose the same Expression control.
Opening the control retrieves the gene's GTEx Portal profile and shows median TPM by tissue, tissue ranking, expression breadth, tissue specificity, a distribution summary, phenotype-linked tissue context when a broad local mapping is available, a locally saved reviewer note, and a link to the GTEx gene page. Expression data are presented as biological plausibility context rather than as variant-classification evidence.
VarSage uses GTEx Portal API v2, with gtex_v8 and GENCODE v26 as the defaults. Expression is loaded only when a signed-in user opens a gene panel, and unavailable remote data does not interrupt the analysis.
The authenticated VarSage endpoint receives the selected gene and phenotype labels. VarSage sends the gene lookup to GTEx and performs phenotype-to-tissue matching locally; patient files, patient IDs, and clinical narrative are not sent to GTEx by this feature.
Expression is biological context, not an ACMG/AMP criterion and not a pathogenicity measure. It does not change filtering, ranking, classification, or reportability. Low bulk-tissue expression does not exclude a gene because cell type, developmental stage, transcript, and disease state matter.
VarSage uses the Human Phenotype Ontology (HPO) as the controlled vocabulary for clinical features. HPO terms are used for rare-disease ranking, phenotype-concern scope in carrier screening, trio proband interpretation, and gene-disease plausibility review. The system preserves the difference between observed findings, explicitly absent findings, and inferred ontology matching context.
The matcher recognizes HPO IDs, canonical names, synonyms, selected clinically equivalent aliases, punctuation-normalized terms, and common plural forms. HPO hierarchy is used when mapping phenotypes to genes: for example, Migraine (HP:0002076) is a child of Headache (HP:0002315), so a gene annotated with Headache can remain in scope for a patient with migraine. The extractor may add direct, clinically useful parent terms such as Headache to the editable suggestion list; users can remove any suggestion before submitting the case. Co-symptoms such as photophobia, nausea, visual impairment, or aura are not inferred unless the source text states them or the user confirms them. AI suggestions still require a valid ontology ID and supporting text, and explicit negation takes precedence.
Type HPO terms directly into the Known HPO Terms field. The autocomplete searches by term name and synonym. Add terms one at a time; they appear as removable chips below the input.
Use Extract HPO terms with AI below each pipeline's clinical description. The model suggests present and explicitly absent terms, including strongly supported ontology parents, while deterministic validation requires valid identifiers and evidence from the submitted text. Suggestions appear in removable chips and remain editable before the analysis starts.
Terms entered in the Absent HPO Terms field are recorded as excluded findings. They help refine the phenotype profile but do not contribute positively to the ranking score.
The deterministic matcher normalizes text, handles common plurals (seizures matches Seizure), and searches HPO term names and synonyms. It prioritizes more specific (longer) terms when the 25-term cap is reached. Gene-scope matching additionally recognizes a patient term's HPO ancestors, but it never treats unrelated associated symptoms as observed findings.
VarSage loads a gene-phenotype mapping file configured through GENE_PHENOTYPES_FILE. HGNC aliases and HPOA disease annotations can enrich this layer by connecting previous gene symbols, OMIM/ORPHA disease identifiers, disease-level phenotypes, and known inheritance terms. Phenotype overlap is therefore a structured plausibility measure, not a proof of causality.
Standard Variant Call Format. VarSage reads genotype, depth, genotype quality, filter status, INFO annotations, FORMAT fields, and transcript annotations. Multi-sample VCFs are supported when the analyzed sample ID is selected.
Tabular files with separate coordinate columns or a single parseable variant column. Common aliases for chromosome, position, reference allele, alternate allele, gene, consequence, and frequency are recognized.
Plain text files are treated as delimited tables when a consistent separator is present. They are most appropriate for small, already-curated variant lists.
VarSage can infer the parser from filename and content. For non-standard exports, optional AI schema detection can identify columns, after which coordinate parsing remains deterministic.
BED files define genomic intervals of interest, usually assay targets or capture regions. When a BED target set is selected, variants outside those intervals are removed before Jannovar annotation and external database merging. This improves runtime and keeps the report aligned with the technical scope of the assay.
BED coordinates use the standard 0-based half-open convention, while VCF and most variant tables use 1-based variant positions. VarSage handles the comparison internally, but the BED file must correspond to the same genome build as the uploaded variants.
The VarSage SuperDB layer is a local, build-specific coordinate annotation resource. It is intended to provide high-value fields needed for rapid interpretation: population rarity, clinical assertion context, prediction scores, splice scores, and stable variant identifiers. Large SuperDB files can be BGZIP-compressed and Tabix-indexed so only positions present in the current analysis are queried.
maxFreq summarizes population rarity by retaining the maximum observed allele frequency across configured population resources for the same allele. It provides a conservative rarity screen, but disease prevalence, penetrance, ancestry, and inheritance mode still determine the appropriate clinical threshold.
REVEL, CADD, SpliceAI, InterVar-like fields, ClinVar-like assertions, and custom laboratory columns can be attached when present in local annotation tables. These fields support review but do not replace primary evidence assessment.
VarSage offers optional AI-assisted features, but the platform remains functional without any model call. AI can help translate clinical text into reviewable HPO candidates, identify unusual table schemas, summarize evidence for top-ranked variants, and draft cautious report narrative from structured data. It is not the authority for coordinate extraction, allele identity, ACMG evidence, or clinical classification.
AI reads the free-text clinical description and suggests present or explicitly absent HPO terms. Suggestions must resolve to valid ontology identifiers and should be interpreted as phenotype curation assistance.
When enabled for a case, AI reviews a bounded top-ranked candidate set plus selected rescue candidates and provides a narrative summary of phenotype fit, inheritance support, evidence quality, and missing confirmation steps.
For AI-reviewed candidates, the model may suggest ACMG/AMP criteria that appear compatible with the supplied structured evidence. Deterministic validation checks the code, strength, and prerequisites before accepting any provisional refinement. Expert verification remains mandatory.
For unusual TSV/CSV headers, AI can identify which columns appear to encode chromosome, position, reference, alternate, gene, consequence, quality, and annotation fields. VarSage then parses coordinates deterministically.
When enabled, AI drafts a case-level synthesis from structured candidate data. The synthesis separates review priority, provisional classification, case-level causality, reportability, and missing evidence.
VarSage provides conservative ACMG/AMP-oriented evidence summaries for germline Mendelian sequence variants. These summaries are provisional decision-support aids, not clinical laboratory classifications. Evidence is deliberately labeled by state so reviewers can distinguish explicit auditable data from hypotheses and contextual information.
Derived from HPO overlap between the patient profile and gene-disease associations. High overlap supports phenotype-gene plausibility, but it is not sufficient by itself for variant pathogenicity.
Based on zygosity, sex, expected inheritance mode, known gene inheritance, and family clues. Trio and phase evidence remain provisional until sample identity, parentage, callability, and segregation are reviewed.
Derived from maxFreq and other frequency fields. Rarity supports review, but maximum credible allele frequency depends on prevalence, penetrance, inheritance mode, and disease-specific guidance.
REVEL, CADD, SpliceAI, and similar predictors support computational-impact review only when their thresholds and applicability are appropriate. Correlated predictors should not be counted independently.
Depth, genotype quality, and filter status are used to reduce technical noise. Quality metrics support confidence in a call, not pathogenicity.
| State | Meaning | Example |
|---|---|---|
| Applied | The prerequisite fact is explicitly present in the source data and can be audited. | A supplied validated functional assay strength, or de novo evidence with parentage confirmation. |
| Candidate | The data suggest a criterion, but disease-specific or laboratory prerequisites are incomplete. | Rarity compatible with PM2_Supporting, or possible compound heterozygosity without confirmed trans phase. |
| Informational | The field is useful context but is not counted as ACMG/AMP evidence. | ClinVar label, GTEx expression, LitVar context, or AI narrative without primary supporting evidence. |
PVS1 is not assigned from consequence alone; loss-of-function disease mechanism, transcript relevance, NMD expectation, exon rescue, and the ClinGen PVS1 decision tree require review. PM2 is treated conservatively at Supporting strength. PP3 and BP4 require calibrated predictors. PP5 and BP6 are not generated from ClinVar labels. De novo, trans-phase, co-segregation, and functional evidence require the corresponding laboratory and disease-specific prerequisites.
Back to topVarSage is an independent project. With thanks to adbioinformatics.com for supporting VarSage and for writing about the platform in their blog article on AI exome variant prioritization.
We also thank Ahmad Golestani Far and Amir Shahbazi for the time they devoted to testing the tool.
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