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Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing

Nature Biotechnology volume 30, pages 61–68 (2012)Cite this article

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Abstract

Distinguishing single-nucleotide variants (SNVs) from errors in whole-genome sequences remains challenging. Here we describe a set of filters, together with a freely accessible software tool, that selectively reduce error rates and thereby facilitate variant detection in data from two short-read sequencing technologies, Complete Genomics and Illumina. By sequencing the nearly identical genomes from monozygotic twins and considering shared SNVs as 'true variants' and discordant SNVs as 'errors', we optimized thresholds for 12 individual filters and assessed which of the 1,048 filter combinations were effective in terms of sensitivity and specificity. Cumulative application of all effective filters reduced the error rate by 290-fold, facilitating the identification of genetic differences between monozygotic twins. We also applied an adapted, less stringent set of filters to reliably identify somatic mutations in a highly rearranged tumor and to identify variants in the NA19240 HapMap genome relative to a reference set of SNVs.

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Figure 1: Development of individual filters on monozygotic twin genomes.
Figure 2: Efficacy of the individual filters with respect to the number of shared and discordant SNVs in monozygotic twins (CG filters) and NA19240 genomes (Illumina filters).
Figure 3: ROC curves of all filter combinations.

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Acknowledgements

We appreciate the assistance of M. Veugelers and S. Plaisance (VIB Technology Watch). We acknowledge G. Peuteman, T. Van Brussel, S. Cammaerts, M. Strazisar and the Genetic Service Facility (http://www.vibgeneticservicefacility.be/) for technical assistance. We highly appreciate the helpful comments from the reviewers. The research was supported by the Fund for Scientific Research Flanders (FWO-F) to J.R. and P.V.L., the Agency for Innovation by Science and Technology (IWT) to M.V.D.B., the Stichting tegen Kanker, FWO-F and the KULeuven (KULPFV/10/016-SymBioSysII)) to D.L.

Author information

Author notes

  1. Joke Reumers, Peter De Rijk, Diether Lambrechts and Jurgen Del-Favero: These author contributed equally to this work.

Authors and Affiliations

  1. Vesalius Research Center, Vlaams Instituut voor Biotechnologie (VIB), Leuven, Belgium

    Joke Reumers, Hui Zhao, Dominiek Smeets & Diether Lambrechts

  2. Vesalius Research Center, University of Leuven, Leuven, Belgium

    Joke Reumers, Hui Zhao, Dominiek Smeets & Diether Lambrechts

  3. Department of Molecular Genetics, Applied Molecular Genomics Group, VIB, Antwerp, Belgium

    Peter De Rijk, Anthony Liekens, Maarten Van Den Bossche & Jurgen Del-Favero

  4. Applied Molecular Genomics Group, University of Antwerp, Antwerp, Belgium

    Peter De Rijk, Anthony Liekens, Maarten Van Den Bossche & Jurgen Del-Favero

  5. Real Time Genomics, San Francisco, California, USA

    John Cleary & Brian Hilbush

  6. Department of Molecular and Developmental Genetics, VIB, Leuven, Belgium

    Peter Van Loo

  7. Department of Human Genetics, University of Leuven, Leuven, Belgium

    Peter Van Loo

  8. Collaborative Antwerp Psychiatric Research Institute (CAPRI), Faculty of Medicine, University of Antwerp, Antwerp, Belgium

    Maarten Van Den Bossche & Bernard Sabbe

  9. PC Sint-Norbertushuis, Duffel, Belgium

    Maarten Van Den Bossche & Bernard Sabbe

  10. ZNA Psychiatric Hospital Stuivenberg, Antwerp, Belgium

    Kirsten Catthoor

  11. Division of Gynaecologic Oncology, Department of Obstetrics and Gynaecology, University Hospital Gasthuisberg, Leuven, Belgium

    Evelyn Despierre & Ignace Vergote

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  1. Joke Reumers
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Contributions

D.L. and J.D.-F. conceptualized this work. J.R. and P.D.R. wrote algorithms and analyzed data. H.Z. analyzed the Yoruban genome, A.L. assisted with the twin analysis. J.C. and B.H. performed RTG-related analyses. P.V.L. provided the ASCAT algorithm, D.S. performed SNP array experiments. K.C., M.V.D.B., B.S., E.D. and I.V. selected and characterized patient samples. All authors approved the manuscript.

Corresponding authors

Correspondence to Diether Lambrechts or Jurgen Del-Favero.

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Competing interests

B.H. and J.C. are employees of Real Time Genomics and have financial interests in Real Time Genomics.

Supplementary information

Supplementary Text and Figures

Supplementary Notes 1–14 (PDF 5224 kb)

Supplementary Table S1

Validation experiments for the twin and tumor-normal genomes using Sanger sequencing and Sequenom MassARRAY genotyping (XLSX 390 kb)

Supplementary Table S2

Metrics and error rates calculated for each filter combination performed on the twin genome comparison using coverage depth cutoffs of 10 and 20 (XLSX 908 kb)

Supplementary Table S3

Overlap analysis of somatic variants in Tumor 1 and its replicate using three filter settings (XLSX 106 kb)

Supplementary Table S4

Sequenom validation of somatic missense SNVs in the ovarian clear cell tumor genome using three filter settings (XLSX 67 kb)

Supplementary Table S5

Prediction of the effect of the validated somatic mutations and somatic non-coding SNVs in the ovarian serous carcinoma (XLSX 18 kb)

Supplementary Table S6

Effect of filters cumulatively applied to the NA19240 genome, using stringent CG filters versus unfiltered CG data (XLSX 500 kb)

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Reumers, J., De Rijk, P., Zhao, H. et al. Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing. Nat Biotechnol 30, 61–68 (2012). https://doi.org/10.1038/nbt.2053

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