A comparative analysis of algorithms for somatic SNV detection in cancer

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dc.contributor.authorRoberts, N.
dc.contributor.authorKortschak, R.
dc.contributor.authorParker, W.
dc.contributor.authorSchreiber, A.
dc.contributor.authorBranford, S.
dc.contributor.authorScott, H.
dc.contributor.authorGlonek, G.
dc.contributor.authorAdelson, D.
dc.date.issued2013
dc.descriptionData source: Supplementary data, https://doi.org/10.1093/bioinformatics/btt375
dc.description.abstractMotivation: With the advent of relatively affordable high-throughput technologies, DNA sequencing of cancers is now common practice in cancer research projects and will be increasingly used in clinical practice to inform diagnosis and treatment. Somatic (cancer-only) single nucleotide variants (SNVs) are the simplest class of mutation, yet their identification in DNA sequencing data is confounded by germline polymorphisms, tumour heterogeneity and sequencing and analysis errors. Four recently published algorithms for the detection of somatic SNV sites in matched cancer–normal sequencing datasets are VarScan, SomaticSniper, JointSNVMix and Strelka. In this analysis, we apply these four SNV calling algorithms to cancer–normal Illumina exome sequencing of a chronic myeloid leukaemia (CML) patient. The candidate SNV sites returned by each algorithm are filtered to remove likely false positives, then characterized and compared to investigate the strengths and weaknesses of each SNV calling algorithm. Results: Comparing the candidate SNV sets returned by VarScan, SomaticSniper, JointSNVMix2 and Strelka revealed substantial differences with respect to the number and character of sites returned; the somatic probability scores assigned to the same sites; their susceptibility to various sources of noise; and their sensitivities to low-allelic-fraction candidates.
dc.description.statementofresponsibilityNicola D. Roberts, R. Daniel Kortschak, Wendy T. Parker, Andreas W. Schreiber, Susan Branford, Hamish S. Scott, Garique Glonek and David L. Adelson
dc.identifier.citationBioinformatics, 2013; 29(18):2223-2230
dc.identifier.doi10.1093/bioinformatics/btt375
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.orcidKortschak, R. [0000-0001-8295-2301]
dc.identifier.orcidSchreiber, A. [0000-0002-9081-3405]
dc.identifier.orcidBranford, S. [0000-0002-1964-3626] [0000-0002-5095-7981]
dc.identifier.orcidScott, H. [0000-0002-5813-631X]
dc.identifier.orcidAdelson, D. [0000-0003-2404-5636]
dc.identifier.urihttp://hdl.handle.net/2440/79134
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1023059
dc.rights© The Author 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.source.urihttps://doi.org/10.1093/bioinformatics/btt375
dc.subjectHumans
dc.subjectNeoplasms
dc.subjectSequence Analysis, DNA
dc.subjectMutation
dc.subjectAlgorithms
dc.subjectSoftware
dc.subjectGenotyping Techniques
dc.subjectExome
dc.titleA comparative analysis of algorithms for somatic SNV detection in cancer
dc.typeJournal article
pubs.publication-statusPublished

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