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Monovar: single-nucleotide variant detection in single cells

Nature Methods volume 13, pages 505507 (2016) | Download Citation

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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Acknowledgements

This work was supported by grants to N.N. from the Lefkofsky Foundation, NCI (RO1CA169244-01), NIH (R21CA174397), Agilent University Relations, and MD Anderson Knowledge Gap and Center for Genetics & Genomics. N.N. is a Damon Runyon-Rachleff Innovator (DRR-25-13), ACS Research Scholar, T.C. Hsu Endowed Scholar and Sabin Fellow. K.C. is a Sabin Fellow and was supported by an NCI grant (RO1CA172652). The study was supported by the Bosarge, Chapman and Dell Foundations and NCI (CA016672). The authors thank W. Zhou.

Author information

Author notes

    • Hamim Zafar
    •  & Yong Wang

    These authors contributed equally to this work.

Affiliations

  1. Department of Computer Science, Rice University, Houston, Texas, USA.

    • Hamim Zafar
    •  & Luay Nakhleh
  2. Department of Bioinformatics and Computational Biology, the University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.

    • Hamim Zafar
    • , Nicholas Navin
    •  & Ken Chen
  3. Department of Genetics, the University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.

    • Yong Wang
    •  & Nicholas Navin

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Contributions

H.Z. was involved in all aspects. Y.W. analyzed the data. L.N. developed the algorithm. N.N. analyzed the data and wrote the manuscript. K.C. analyzed data and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Nicholas Navin or Ken Chen.

Integrated supplementary information

Supplementary information

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    Supplementary Text and Figures

    Supplementary Figures 1–3, Supplementary Tables 1–6 and Supplementary Note 1

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    Supplementary Software

    Monovar code and accessory files.

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DOI

https://doi.org/10.1038/nmeth.3835

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