title: Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  Software called MutationSeq and datasets are available from http://compbio.bccrc.ca.
authors.affiliation: Department of Molecular Oncology, BC Cancer Agency, Vancouver, BC, Canada.
authors.forename: Jiarui
authors.initials: J
authors.lastname: Ding
authors.forename: Ali
authors.initials: A
authors.lastname: Bashashati
authors.forename: Andrew
authors.initials: A
authors.lastname: Roth
authors.forename: Arusha
authors.initials: A
authors.lastname: Oloumi
authors.forename: Kane
authors.initials: K
authors.lastname: Tse
authors.forename: Thomas
authors.initials: T
authors.lastname: Zeng
authors.forename: Gholamreza
authors.initials: G
authors.lastname: Haffari
authors.forename: Martin
authors.initials: M
authors.lastname: Hirst
authors.forename: Marco A
authors.initials: MA
authors.lastname: Marra
authors.forename: Anne
authors.initials: A
authors.lastname: Condon
authors.forename: Samuel
authors.initials: S
authors.lastname: Aparicio
authors.forename: Sohrab P
authors.initials: SP
authors.lastname: Shah
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics*; D016000:Cluster Analysis; D059472:Exome; D005260:Female; D016678:Genome; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D009369:Neoplasms; D020641:Polymorphism, Single Nucleotide; D012984:Software; D060388:Support Vector Machine
references.citation: N Engl J Med. 2010 Oct 14;363(16):1532-43
references.pmid: 20942669
references.citation: Genome Res. 2010 Sep;20(9):1297-303
references.pmid: 20644199
references.citation: Nature. 2010 Apr 15;464(7291):999-1005
references.pmid: 20393555
references.citation: Bioinformatics. 2010 Mar 15;26(6):730-6
references.pmid: 20130035
references.citation: Bioinformatics. 2010 Feb 1;26(3):392-8
references.pmid: 19942583
references.citation: Bioinformatics. 2009 Sep 1;25(17):2283-5
references.pmid: 19542151
references.citation: J Pathol. 2011 Apr;223(5):567-73
references.pmid: 21381030
references.citation: Bioinformatics. 2009 Aug 15;25(16):2078-9
references.pmid: 19505943
references.citation: Nature. 2009 Oct 8;461(7265):809-13
references.pmid: 19812674
references.citation: Nature. 2011 Jan 27;469(7331):539-42
references.pmid: 21248752
references.citation: Nature. 2011 Jul 27;476(7360):298-303
references.pmid: 21796119
references.citation: Bioinformatics. 2011 Jul 1;27(13):i77-84
references.pmid: 21685105
references.citation: Nature. 2011 Jun 05;475(7354):101-5
references.pmid: 21642962
references.citation: BMC Bioinformatics. 2011 Nov 21;12:451
references.pmid: 22099972
references.citation: Bioinformatics. 2011 Jun 15;27(12):1691-2
references.pmid: 21493652
references.citation: Nature. 2011 Mar 24;471(7339):467-72
references.pmid: 21430775
references.citation: Genome Res. 2009 Jun;19(6):1124-32
references.pmid: 19420381
references.citation: N Engl J Med. 2009 Jun 25;360(26):2719-29
references.pmid: 19516027
references.citation: N Engl J Med. 2009 Feb 19;360(8):765-73
references.pmid: 19228619
references.citation: Nat Genet. 2010 Feb;42(2):181-5
references.pmid: 20081860