A comparative analysis of algorithms for somatic SNV detection in cancer

Roberts, Nicola D.; Kortschak, R. Daniel; Parker, Wendy T; Schreiber, Andreas W.; Branford, Susan; Scott, Hamish S.; Glonek, Garique; Adelson, David L.

doi:10.1093/bioinformatics/btt375

Cited by 89 publications

(80 citation statements)

References 19 publications

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“…Candidates present in dbSNP are regarded as putative false positives caused by germline polymorphisms or sequencing errors1930. Although it is not adequate to assign a single site to putative false positives solely by its appearance in dbSNP, the proportion of dbSNP entries in a candidate set implies its unreliability.…”

Section: Resultsmentioning

confidence: 99%

“…Although each of the new tools has different updated versions after years of improvement, they still have poor consensus in realistic scenarios1819. Moreover, due to incomplete experimental evaluation and lack of an established gold-standard method for massive somatic SNV calling featuring with highest sensitivity and specificity, their relative merits in real applications are largely unknown.…”

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confidence: 99%

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In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data

Cai

Yuan

Zhang

et al. 2016

Sci Rep

103

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Four popular somatic single nucleotide variant (SNV) calling methods (Varscan, SomaticSniper, Strelka and MuTect2) were carefully evaluated on the real whole exome sequencing (WES, depth of ~50X) and ultra-deep targeted sequencing (UDT-Seq, depth of ~370X) data. The four tools returned poor consensus on candidates (only 20% of calls were with multiple hits by the callers). For both WES and UDT-Seq, MuTect2 and Strelka obtained the largest proportion of COSMIC entries as well as the lowest rate of dbSNP presence and high-alternative-alleles-in-control calls, demonstrating their superior sensitivity and accuracy. Combining different callers does increase reliability of candidates, but narrows the list down to very limited range of tumor read depth and variant allele frequency. Calling SNV on UDT-Seq data, which were of much higher read-depth, discovered additional true-positive variations, despite an even more tremendous growth in false positive predictions. Our findings not only provide valuable benchmark for state-of-the-art SNV calling methods, but also shed light on the access to more accurate SNV identification in the future.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data

Cai

Yuan

Zhang

et al. 2016

Sci Rep

103

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“…As another DNA-only control, a leading (45) DNA-WES mutation caller from Illumina, Strelka (17), was run on the same DNA-WES. Strelka exhibited inferior performance overall, smaller true positive rates at fixed false positive rates, and never achieved the sensitivity of UNCeqR META or UNCeqR DNA (Figure 2).…”

Section: Resultsmentioning

confidence: 99%

Integrated RNA and DNA sequencing improves mutation detection in low purity tumors

Wilkerson¹,

Cabanski²,

Sun³

et al. 2014

Nucleic Acids Res

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Identifying somatic mutations is critical for cancer genome characterization and for prioritizing patient treatment. DNA whole exome sequencing (DNA-WES) is currently the most popular technology; however, this yields low sensitivity in low purity tumors. RNA sequencing (RNA-seq) covers the expressed exome with depth proportional to expression. We hypothesized that integrating DNA-WES and RNA-seq would enable superior mutation detection versus DNA-WES alone. We developed a first-of-its-kind method, called UNCeqR, that detects somatic mutations by integrating patient-matched RNA-seq and DNA-WES. In simulation, the integrated DNA and RNA model outperformed the DNA-WES only model. Validation by patient-matched whole genome sequencing demonstrated superior performance of the integrated model over DNA-WES only models, including a published method and published mutation profiles. Genome-wide mutational analysis of breast and lung cancer cohorts (n = 871) revealed remarkable tumor genomics properties. Low purity tumors experienced the largest gains in mutation detection by integrating RNA-seq and DNA-WES. RNA provided greater mutation signal than DNA in expressed mutations. Compared to earlier studies on this cohort, UNCeqR increased mutation rates of driver and therapeutically targeted genes (e.g. PIK3CA, ERBB2 and FGFR2). In summary, integrating RNA-seq with DNA-WES increases mutation detection performance, especially for low purity tumors.

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“…A comparative analysis of algorithms for somatic SNV detection in cancer samples rated SomaticSniper as one of the best performers. 8 Its sensitivity was evaluated on WGS data from a cell line (coverage of 30 times) and reported to be ;99.8% (calling 496 of the 497 previously validated sites 6 ) This method, however, does not consider possible variations in sensitivity due to changes in the allelic fraction of the mutations (ie, low-frequency mutations) because cell lines have presumably lower biological heterogeneity than primary tumor samples. On the contrary, the more recently published MuTect 7 describes benchmarking approaches to evaluate its sensitivity and specificity as a function of the allelic fraction, and it is designed to recognize low allele fraction events ($0.03).…”

Section: Introductionmentioning

confidence: 99%

The hidden genomic landscape of acute myeloid leukemia: subclonal structure revealed by undetected mutations

Bodini

Ronchini

Giacò

et al. 2015

Blood

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The analyses carried out using 2 different bioinformatics pipelines (SomaticSniper and MuTect) on the same set of genomic data from 133 acute myeloid leukemia (AML) patients, sequenced inside the Cancer Genome Atlas project, gave discrepant results. We subsequently tested these 2 variant-calling pipelines on 20 leukemia samples from our series (19 primary AMLs and 1 secondary AML). By validating many of the predicted somatic variants (variant allele frequencies ranging from 100% to 5%), we observed significantly different calling efficiencies. In particular, despite relatively high specificity, sensitivity was poor in both pipelines resulting in a high rate of false negatives. Our findings raise the possibility that landscapes of AML genomes might be more complex than previously reported and characterized by the presence of hundreds of genes mutated at low variant allele frequency, suggesting that the application of genome sequencing to the clinic requires a careful and critical evaluation. We think that improvements in technology and workflow standardization, through the generation of clear experimental and bioinformatics guidelines, are fundamental to translate the use of next-generation sequencing from research to the clinic and to transform genomic information into better diagnosis and outcomes for the patient.

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A comparative analysis of algorithms for somatic SNV detection in cancer

Cited by 89 publications

References 19 publications

In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data

In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data

Integrated RNA and DNA sequencing improves mutation detection in low purity tumors

The hidden genomic landscape of acute myeloid leukemia: subclonal structure revealed by undetected mutations

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