Feature-based classifiers for somatic mutation detection in tumour–normal paired sequencing data
Open Access
- 13 November 2011
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 28 (2), 167-175
- https://doi.org/10.1093/bioinformatics/btr629
Abstract
Motivation: The study of cancer genomes now routinely involves using next-generation sequencing technology (NGS) to profile tumours for single nucleotide variant (SNV) somatic mutations. However, surprisingly few published bioinformatics methods exist for the specific purpose of identifying somatic mutations from NGS data and existing tools are often inaccurate, yielding intolerably high false prediction rates. As such, the computational problem of accurately inferring somatic mutations from paired tumour/normal NGS data remains an unsolved challenge. Results: We present the comparison of four standard supervised machine learning algorithms for the purpose of somatic SNV prediction in tumour/normal NGS experiments. To evaluate these approaches (random forest, Bayesian additive regression tree, support vector machine and logistic regression), we constructed 106 features representing 3369 candidate somatic SNVs from 48 breast cancer genomes, originally predicted with naive methods and subsequently revalidated to establish ground truth labels. We trained the classifiers on this data (consisting of 1015 true somatic mutations and 2354 non-somatic mutation positions) and conducted a rigorous evaluation of these methods using a cross-validation framework and hold-out test NGS data from both exome capture and whole genome shotgun platforms. All learning algorithms employing predictive discriminative approaches with feature selection improved the predictive accuracy over standard approaches by statistically significant margins. In addition, using unsupervised clustering of the ground truth ‘false positive’ predictions, we noted several distinct classes and present evidence suggesting non-overlapping sources of technical artefacts illuminating important directions for future study. Availability: Software called MutationSeq and datasets are available from http://compbio.bccrc.ca. Contact:saparicio@bccrc.ca Supplementary information: Supplementary data are available at Bioinformatics online.Keywords
This publication has 24 references indexed in Scilit:
- vipR: variant identification in pooled DNA using RBioinformatics, 2011
- BamTools: a C++ API and toolkit for analyzing and managing BAM filesBioinformatics, 2011
- Initial genome sequencing and analysis of multiple myelomaNature, 2011
- Genome remodelling in a basal-like breast cancer metastasis and xenograftNature, 2010
- BART: Bayesian additive regression treesThe Annals of Applied Statistics, 2010
- SNVMix: predicting single nucleotide variants from next-generation sequencing of tumorsBioinformatics, 2010
- Robust biomarker identification for cancer diagnosis with ensemble feature selection methodsBioinformatics, 2009
- VarScan: variant detection in massively parallel sequencing of individual and pooled samplesBioinformatics, 2009
- The Elements of Statistical LearningPublished by Springer Science and Business Media LLC ,2009
- Algorithm AS 217: Computation of the Dip Statistic to Test for UnimodalityJournal of the Royal Statistical Society Series C: Applied Statistics, 1985