nanotatoR

nanotatoR annotates and classifies structural variants from whole genome sequencing (WGS) and Optical Genome Mapping (OGM) to support variant interpretation and prioritization.

Key Features:

• Frequency estimation: Estimates structural variant frequencies using external databases DGV, DECIPHER, Bionano Genomics BNDB and user-defined internal databases.
• Reference-based annotation: Annotates SVs with GRCh37/38-based BED files to identify overlapping, upstream, and downstream genes.
• Gene overlap analysis: Calculates overlap percentages and nearest-gene distances and extracts a phenotype-tailored primary gene list from public databases for prioritization.
• Transcriptomic integration: Integrates expression data such as RNA-Seq for overlapping or nearby genes when available to assess transcriptomic impact.
• Quality-control filtering: Applies customizable quality-control filtration parameters to filter SVs based on user-specified criteria.
• Output organization: Produces Excel outputs organized into multiple sheets by SV type (e.g., INDELs, inversions, translocations) and inheritance patterns (e.g., de novo).
• Data source support: Supports analysis of SVs derived from WGS and OGM platforms.

Scientific Applications:

• Clinical diagnosis: Provides detailed annotations to aid determination of variant pathogenicity for clinical interpretation of OGM and WGS-derived SVs.
• Genetic disease research: Facilitates filtering and prioritization of known and candidate pathogenic variants in cohorts, including applications demonstrated in Duchenne Muscular Dystrophy.

Methodology:

Uses external databases (DGV, DECIPHER, Bionano Genomics BNDB) and user-defined internal databases for frequency estimation; annotates SVs with GRCh37/38 BED files to identify overlapping/upstream/downstream genes; computes overlap percentages and nearest-gene distances; extracts phenotype-specific primary gene lists from public databases; integrates RNA-Seq expression data when available; applies customizable QC filters; outputs Excel sheets by SV type (INDELs, inversions, translocations) and inheritance patterns (de novo); validated using reference datasets including NA12878 mapped with two enzyme labelings and the NA24143 trio and assessed against Bioconductor quality and runtime criteria (April 2019 release).