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Updated: Mar 22, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
Monovar: single-nucleotide variant detection in single cells
Hamim Zafar1,2, Yong Wang3, Luay Nakhleh1
1Department of Computer Science, Rice University, Houston, Texas, USA.
Abstract:
Current variant callers are not suitable for single-cell DNA sequencing, as they do not account for allelic dropout, false-positive errors and coverage nonuniformity. We developed Monovar (https://bitbucket.org/hamimzafar/monovar), a statistical method for detecting and genotyping single-nucleotide variants in single-cell data. Monovar exhibited superior performance over standard algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in three different human tumor data sets.
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