Lancet

Lancet calls somatic single nucleotide variants (SNVs) and insertions/deletions (indels) from short-read tumor/normal paired sequencing data using localized micro-assembly with colored de Bruijn graphs and linked-reads barcode-aware haplotype integration.

Key Features:

• Somatic variant calling: Detects SNVs and indels with high sensitivity and accuracy in tumor/normal paired samples.
• Localized micro-assembly: Performs localized micro-assembly for candidate variant regions.
• Colored de Bruijn graph: Uses a colored de Bruijn graph assembly paradigm for joint analysis of tumor and normal reads.
• Joint tumor/normal analysis: Analyzes tumor and normal reads within a unified framework to support somatic variant detection.
• Linked-reads support: Integrates linked-reads sequencing data by incorporating barcodes and haplotype read assignments into the local assembly.
• Barcode-aware coverage: Computes barcode-aware coverage and identifies variants that conflict with local haplotype structure.
• Artifact discrimination: Improves discrimination of true somatic mutations from sequencing artifacts, including in low-complexity regions.
• Repeat analysis: Performs on-the-fly repeat composition analysis to inform assembly and variant calling.
• Self-tuning k-mer strategy: Adapts k-mer sizes via a self-tuning strategy to enhance specificity and accuracy.
• Alignment requirement: Requires raw reads to be aligned using BWA (Burrows-Wheeler Aligner).
• Implementation: Implemented in C++.

Scientific Applications:

• Cancer genomics: Detection of somatic mutations in cancer genomes from tumor/normal paired sequencing data.
• Linked-reads analysis: Resolution of variants using linked-reads barcodes and haplotype information.
• Low-complexity and repetitive regions: Accurate variant calling in genomic regions with low complexity or repeats.
• Artifact filtering: Distinguishing true somatic mutations from sequencing artifacts via barcode- and haplotype-aware analysis.

Methodology:

Performs localized micro-assembly using a colored de Bruijn graph, integrates linked-reads barcodes and haplotype read assignments into the local assembly to compute barcode-aware coverage and identify haplotype-conflicting variants, performs on-the-fly repeat composition analysis and a self-tuning k-mer strategy, expects raw reads aligned with BWA, and is implemented in C++.