Monovar

Monovar detects and genotypes single-nucleotide variants (SNVs) from single-cell DNA sequencing data to account for allelic dropout, false-positive errors, and coverage nonuniformity.

Key Features:

• Algorithmic Design: Engineered for single-cell DNA sequencing to handle low-coverage and error-prone data typical of single-cell contexts.
• Statistical Approach: Employs a statistical model/framework that mitigates allelic dropout and false-positive calls in single-cell sequencing.
• Output Format: Processes BAM (Sequence Alignment/Map) input files and outputs results in VCF (Variant Call Format) containing detected SNVs.

Scientific Applications:

• Oncology research: Identifying driver mutations and delineating clonal substructures within tumor datasets.
• Tumor heterogeneity: Resolving intratumoral heterogeneity and the evolutionary dynamics of tumors.

Methodology:

Uses a statistical model that addresses uneven coverage, allelic dropout, and false-positive errors in single-cell sequencing and processes BAM inputs to produce VCFs with genotyped SNVs.