Abstract
Current variant callers are not suitable for single-cell DNA sequencing (SCS) as they do not account for allelic dropout, false-positive errors, and coverage non-uniformity. We developed Monovar, a novel statistical method for detecting and genotyping single nucleotide variants in SCS data. Evaluation based on an isogenic fibroblast cell line and three different human tumor datasets showed substantial improvement of Monovar over standard algorithms for identifying driver mutations and delineating clonal substructure.
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