DeNovoGear

DeNovoGear detects de novo single-nucleotide polymorphisms (SNPs) and insertions/deletions (INDELs) from familial and somatic sequencing data to identify spontaneous germ-line and somatic mutations and support parental-origin inference in trio analyses.

Key Features:

• De novo mutation detection: Identifies SNPs and INDELs in trio-based datasets and in somatic or paired-sample comparisons.
• Likelihood-based error modeling: Applies likelihood-based error models to reduce false positives during mutation discovery.
• Fragment-level parental-origin inference: Incorporates fragment information to ascertain the parental origin of germ-line mutations.
• Phasing of de novo mutations: Phases de novo mutations using genotype data from neighboring SNP sites.
• Pairwise sample comparison: Detects SNPs and INDELs present in one sample but absent in another for comparative analyses.
• Sequencing data compatibility: Operates on exome and whole-genome sequencing datasets.

Scientific Applications:

• Germ-line de novo discovery: Detection of de novo SNPs and INDELs in offspring relative to parents in trio studies.
• Parental-origin and inheritance studies: Tracing lineage and inheritance patterns by inferring parental origin of mutations.
• Somatic variant analysis: Identification of somatic mutations across tissues or paired samples.
• Comparative genomics: Identification of sample-specific variants for comparative and case–control analyses.
• Validation on human WGS: Demonstrated a 95% validation rate for predicted de novo insertion and/or deletion mutations on human whole-genome sequencing data.

Methodology:

Uses likelihood-based error modeling, incorporates fragment information to infer parental origin, phases de novo mutations using genotype data from neighboring SNP sites, and performs pairwise comparisons to identify sample-specific SNPs and INDELs.