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Monovar: single-nucleotide variant detection in single cells

Nature Methods volume 13, pages 505–507 (2016)Cite this article

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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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Figure 1: Monovar algorithm and performance in a normal cell line.
Figure 2: Application of Monovar to human tumor samples.

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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

  1. Hamim Zafar and Yong Wang: These authors contributed equally to this work.

Authors and 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

Authors
  1. Hamim Zafar
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  2. Yong Wang
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  3. Luay Nakhleh
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  4. Nicholas Navin
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  5. Ken Chen
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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.

Corresponding authors

Correspondence to Nicholas Navin or Ken Chen.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Comparison of Monovar to Standard Variant Calling Methods using Isogenic Cell Line Data

Monovar, GATK UnifiedGenotyper, GATK HaplotypeCaller and Samtools were compared using single cell exome sequencing data generated from a normal isogenic fibroblast cell line in terms of SNV detection (a) Precision versus Detection Efficiency (Recall) and (b) SNV transition and transversion spectrum for FP errors.

Supplementary Figure 2 Performance of Monovar using in Silico Down-Sampled Coverage Depth Data

Monovar and GATK HaplotypeCaller were compared in terms of (a) Precision and (b) Detection Efficiency (Recall), respectively at 40×, 30×, 20× and 10× sequencing depths, acquired via down-sampling the SKN2 SCS data.

Supplementary Figure 3 Performance of Monovar for Detecting Sub-clonal SNVs in Admixture Samples

The SNV detection (a) Precision and (b) Detection Efficiency of Monovar were measured by comparing SNVs detected from a set of datasets, created by in silico intermixing of variable numbers of SKN2 and 12 TNBC cells, with SNVs detected from SKN2 bulk sequencing data.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3, Supplementary Tables 1–6 and Supplementary Note 1 (PDF 2042 kb)

Supplementary Software

Monovar code and accessory files. (ZIP 28383 kb)

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Zafar, H., Wang, Y., Nakhleh, L. et al. Monovar: single-nucleotide variant detection in single cells. Nat Methods 13, 505–507 (2016). https://doi.org/10.1038/nmeth.3835

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