How Folklore Processes a Genomic Case
Folklore checks the VCF, annotates each variant, applies the deterministic classification rules, matches the phenotype, retrieves literature, and prepares the evidence for clinical review. Model-generated text remains outside the class calculation.
Under 15 minutes
Full genome processing and clinical interpretation
The Analysis Pipeline
The core path covers quality control, annotation, classification, phenotype matching, literature retrieval, screening, and report preparation. Optional modules add family, cohort, and mitochondrial analysis.
VCF Processing & Quality Control
Standard VCF parsing accepts files from any sequencing platform, whole genome, whole exome, or targeted panels. Quality metrics are assessed per variant, applying configurable filters for read depth, genotype quality, and allelic balance.
Variants with documented clinical significance in ClinVar are preserved regardless of quality score. The quality filter therefore does not remove them from clinical review.
Output: Quality-filtered variant set with clinically significant variants preserved
Variant Annotation
Each variant is annotated through Ensembl VEP (Variant Effect Predictor) for consequence prediction, protein impact, and functional domain mapping. Annotation runs in parallel across the variant set.
Multi-source database enrichment adds population frequencies from gnomAD (global and population-specific allele frequencies), clinical significance from ClinVar, functional impact predictions from 12+ computational tools including SIFT, PolyPhen-2, CADD, REVEL, AlphaMissense, DANN, MetaSVM, GERP++, PhyloP, and PhastCons, gene constraint metrics (pLI, LOEUF, o/e loss-of-function), and gene-disease associations from ClinGen.
Output: Fully annotated variants with population, functional, conservation, and clinical data
ACMG/AMP Classification
Variant classification follows the 2015 ACMG/AMP guidelines (Richards et al., Genetics in Medicine), the international standard for clinical variant interpretation. All 28 evidence criteria are systematically evaluated: PVS1, PS1–4, PM1–6, PP1–5, BA1, BS1–4, and BP1–7.
Classification is strictly rule-based. No AI model determines variant pathogenicity. Each variant receives one of five standard classifications, Pathogenic, Likely Pathogenic, Variant of Uncertain Significance (VUS), Likely Benign, or Benign, with an explicit listing of every criterion applied.
Output: Classified variants with the applied ACMG criteria and audit record
Phenotype-Genotype Correlation
Patient phenotype, described using Human Phenotype Ontology (HPO) terms, is compared with the known phenotypic profiles of genes carrying candidate variants. Semantic similarity uses the HPO hierarchy, term specificity, and information content in addition to exact matches.
Each gene receives a normalized relevance score (0–100) with tiered clinical classification. A Pathogenic BRCA1 variant is not flagged as clinically relevant when the patient was referred for epilepsy. Phenotype matching connects technical classification to clinical relevance for the specific patient.
Output: Ranked gene list prioritized by phenotype match strength for this patient
Literature Evidence
A locally maintained, genetics-filtered database supports sub-second queries across millions of PubMed publications. Gene mentions, variant mentions, and phenotype associations are extracted during ingestion and used to retrieve evidence for a case.
Multi-component relevance scoring ranks publications for the case. Each citation includes its PubMed identifier (PMID), DOI, and extracted evidence context so the source publication can be checked.
Output: Ranked literature evidence with traceable citations per gene and variant
Clinical Screening & Prioritization
After classification, annotation, phenotype matching, and literature review, a multi-dimensional prioritization algorithm ranks variants by overall clinical relevance. Scoring adapts to the clinical context, patient age, sex, family history, and indication for genetic testing.
The system supports multiple screening strategies including neonatal intensive care, pediatric genetics, adult diagnostic workup, proactive screening, and carrier testing. The output is a tiered shortlist: Tier 1 (actionable findings requiring immediate clinical attention), Tier 2 (potentially actionable, warranting further review), with incidental findings identified and flagged separately.
Output: Focused shortlist of clinically actionable variants from hundreds of candidates
Family and Trio Analysis (Optional)
When family members are sequenced alongside the proband, Folklore adds inheritance-aware evidence on top of the upstream classification. Three algorithms run sequentially on pre-classified data: de novo detection with confidence tiers, compound heterozygous phasing with parental origin determination, and segregation scoring per the ClinGen SVI 2021 framework. Sample QC via PLINK identity-by-descent runs first to detect sample-swap, consanguinity, and duplicate-sample issues before any inheritance call.
No variant re-calling. The service consumes the existing classified DuckDB files and joins them on chromosome, position, and allele. A typical WGS trio completes the full inheritance analysis in approximately 30 to 90 seconds, with explicit feasibility flags recording which phases were planned as feasible and why.
Output: Inheritance-annotated variants with de novo, compound het, and segregation evidence
Cohort Analytics (Optional)
For population-level research, Folklore aggregates classified samples into a cohort matrix with a deduplicated variant catalog and sparse genotype storage. Six statistical analyses share the matrix: gene-level burden testing with Fisher, CMC, and SKAT-O methods plus FDR correction; pathway enrichment with background correction; pLoF analysis; cohort versus gnomAD frequency analysis; GWAS signal replication; and polygenic risk scoring with PGS Catalog weight files.
A weighted candidate gene nomination engine integrates evidence across all six analyses and produces a ranked list with per-component breakdowns and human-readable evidence summaries. Power analysis is reported per gene so non-significant results in underpowered genes are interpretable.
Output: Ranked candidate genes with statistical evidence and per-component scoring
Mitochondrial DNA Analysis
Mitochondrial DNA biology differs fundamentally from the nuclear genome. Maternal inheritance, heteroplasmy with tissue-specific threshold effects, lack of recombination, and haplogroup structure all change how variants should be classified. Folklore routes mitochondrial variants through a dedicated classifier following the McCormick 2020 specifications produced by the ClinGen Mitochondrial Disease Variant Curation Expert Panel.
Twenty ACMG criteria are applied with mtDNA-specific thresholds and tools including APOGEE2 for protein-coding genes, MitoTIP and HmtVAR for tRNA, MITOMAP and HmtDB for population frequency. Seven criteria are explicitly excluded with verbatim biological rationale. Haplogroup-aware BA1 and NUMT pseudogene detection prevent the most common false-positive patterns.
Output: mtDNA variants classified per the ClinGen Mitochondrial Expert Panel framework
Model-Assisted Clinical Interpretation
An AI model summarizes upstream classifications, phenotype correlations, literature findings, and screening results in a structured narrative. Step 3 remains the rule-based class calculation; the model prepares its output for clinical review.
The report can include the classification alone or combine classification, phenotype, literature, and screening data when available. PDF and DOCX outputs identify the supporting evidence. AI inference runs on dedicated EU infrastructure, and no data is sent to an external AI service.
Output: Downloadable clinical interpretation report with structured evidence and recommendations
Clinical Control Principles
The architecture separates rule-based classification, evidence synthesis, and the geneticist's clinical decision.
Rule-Based Classification
ACMG/AMP criteria applied through deterministic rules determine the class. AI supports evidence gathering and presentation outside that calculation.
Evidence Record
Classifications link to the applied ACMG criteria, literature references to their PMID, and phenotype scores to their HPO terms.
Reproducible Results
The pipeline records the applied criteria and evidence sources for review.
Geneticist Authority
Folklore gathers evidence, applies classification rules, and presents findings. A qualified geneticist reviews the evidence and makes the clinical decision.
See the Pipeline in Action
Follow a case from VCF upload to a report prepared for clinical review.