Toward better understanding of artifacts in variant calling from high-coverage samples

Motivation: Whole-genome high-coverage sequencing has been widely used for personal and cancer genomics as well as in various research areas. However, in the lack of an unbiased whole-genome truth set, the global error rate of variant calls and the leading causal artifacts still remain unclear even...

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Bibliographic Details
Published inBioinformatics (Oxford, England) Vol. 30; no. 20; pp. 2843 - 2851
Main Author Li, Heng
Format Journal Article
LanguageEnglish
Published England Oxford University Press 15.10.2014
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Summary:Motivation: Whole-genome high-coverage sequencing has been widely used for personal and cancer genomics as well as in various research areas. However, in the lack of an unbiased whole-genome truth set, the global error rate of variant calls and the leading causal artifacts still remain unclear even given the great efforts in the evaluation of variant calling methods. Results: We made 10 single nucleotide polymorphism and INDEL call sets with two read mappers and five variant callers, both on a haploid human genome and a diploid genome at a similar coverage. By investigating false heterozygous calls in the haploid genome, we identified the erroneous realignment in low-complexity regions and the incomplete reference genome with respect to the sample as the two major sources of errors, which press for continued improvements in these two areas. We estimated that the error rate of raw genotype calls is as high as 1 in 10–15 kb, but the error rate of post-filtered calls is reduced to 1 in 100–200 kb without significant compromise on the sensitivity. Availability and implementation: BWA-MEM alignment and raw variant calls are available at http://bit.ly/1g8XqRt scripts and miscellaneous data at https://github.com/lh3/varcmp . Contact:  hengli@broadinstitute.org Supplementary information:  Supplementary data are available at Bioinformatics online.
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Associate Editor: Jonathan Wren
ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btu356