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. 2011 Sep 15;27(18):2601-2.
doi: 10.1093/bioinformatics/btr446. Epub 2011 Jul 29.

ContEst: estimating cross-contamination of human samples in next-generation sequencing data

Kristian Cibulskis  1 , Aaron McKenna, Tim Fennell, Eric Banks, Mark DePristo, Gad Getz
Affiliations

ContEst: estimating cross-contamination of human samples in next-generation sequencing data

Kristian Cibulskis et al. Bioinformatics. .

Abstract

Summary: Here, we present ContEst, a tool for estimating the level of cross-individual contamination in next-generation sequencing data. We demonstrate the accuracy of ContEst across a range of contamination levels, sources and read depths using sequencing data mixed in silico at known concentrations. We applied our tool to published cancer sequencing datasets and report their estimated contamination levels.

Availability and implementation: ContEst is a GATK module, and distributed under a BSD style license at http://www.broadinstitute.org/cancer/cga/contest

Contact: kcibul@broadinstitute.org; gadgetz@broadinstitute.org

Supplementary information: Supplementary data is available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
(A) False positive somatic mutations detected per megabase on in silico contaminated data; most cancers have ~1 true event per megabase (B) accuracy with single contaminating sample (C) accuracy with multiple contaminating samples (D) accuracy with respect to read depth; shaded areas indicate 95% confidence interval (E) contamination estimates of TCGA Ovarian dataset.
See this image and copyright information in PMC

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