Dataset Information

A comparative analysis of algorithms for somatic SNV detection in cancer.

ABSTRACT:

Motivation

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.

Availability

Data accession number SRA081939, code at http://code.google.com/p/snv-caller-review/

Contact

david.adelson@adelaide.edu.au

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Roberts ND

PROVIDER: S-EPMC3753564 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Publications

A comparative analysis of algorithms for somatic SNV detection in cancer.

Roberts Nicola D ND Kortschak R Daniel RD Parker Wendy T WT Schreiber Andreas W AW Branford Susan S Scott Hamish S HS Glonek Garique G Adelson David L DL

Bioinformatics (Oxford, England) 20130709 18

<h4>Motivation</h4>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 rece ...[more]

PMID: 23842810

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Dataset Information

A comparative analysis of algorithms for somatic SNV detection in cancer.

Motivation

Results

Availability

Contact

Supplementary information

Publications

A comparative analysis of algorithms for somatic SNV detection in cancer.

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