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

Roberts ND, Kortschak RD, Parker WT, Schreiber AW, Branford S, Scott HS, Glonek G, Adelson DL - Bioinformatics (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Molecular and Biomedical Science and School of Mathematical Sciences, University of Adelaide, South Australia, Australia.

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.

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Related in: MedlinePlus

The proportion of total depth contributed by the most common variant base in the cancer (smooth lines) and normal (jagged lines) for somatic sites uniquely returned by VarScan (red), SomaticSniper (green), JSM2 (orange) and Strelka (blue). The horizontal axis is the scaled index of each site after sorting by variant proportion in the cancer (scaled index chosen for comparisons across different sample sizes)
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btt375-F6: The proportion of total depth contributed by the most common variant base in the cancer (smooth lines) and normal (jagged lines) for somatic sites uniquely returned by VarScan (red), SomaticSniper (green), JSM2 (orange) and Strelka (blue). The horizontal axis is the scaled index of each site after sorting by variant proportion in the cancer (scaled index chosen for comparisons across different sample sizes)

Mentions: The characteristics of somatic candidates uniquely returned by each caller are illustrated by Figure 6. Each set of sites returned solely by one caller was sorted by variant proportion in the cancer sample. Then the variant proportion in the cancer (smooth lines) and normal sample (jagged lines) were plotted against the scaled index of each site.Fig. 6.


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

Roberts ND, Kortschak RD, Parker WT, Schreiber AW, Branford S, Scott HS, Glonek G, Adelson DL - Bioinformatics (2013)

The proportion of total depth contributed by the most common variant base in the cancer (smooth lines) and normal (jagged lines) for somatic sites uniquely returned by VarScan (red), SomaticSniper (green), JSM2 (orange) and Strelka (blue). The horizontal axis is the scaled index of each site after sorting by variant proportion in the cancer (scaled index chosen for comparisons across different sample sizes)
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3753564&req=5

btt375-F6: The proportion of total depth contributed by the most common variant base in the cancer (smooth lines) and normal (jagged lines) for somatic sites uniquely returned by VarScan (red), SomaticSniper (green), JSM2 (orange) and Strelka (blue). The horizontal axis is the scaled index of each site after sorting by variant proportion in the cancer (scaled index chosen for comparisons across different sample sizes)
Mentions: The characteristics of somatic candidates uniquely returned by each caller are illustrated by Figure 6. Each set of sites returned solely by one caller was sorted by variant proportion in the cancer sample. Then the variant proportion in the cancer (smooth lines) and normal sample (jagged lines) were plotted against the scaled index of each site.Fig. 6.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Molecular and Biomedical Science and School of Mathematical Sciences, University of Adelaide, South Australia, Australia.

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.

Show MeSH
Related in: MedlinePlus