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

Cai, Lei; Yuan, Wei; Zhang, Zhou; He, Lin; Chou, Kuo‐Chen

doi:10.1038/srep36540

Cited by 106 publications

(94 citation statements)

References 45 publications

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“…The first crucial step in analyzing cancer genomic data is the identification of genetic variants, particularly those of somatic origin. In that sense, the research community has made great efforts to assess the performance of the many different somatic variant callers available (6)(7)(8)(9)(10)(11). However, so far, there has been no agreement on which variant caller nor which strategy to combine them is the most suitable.…”

Section: Introductionmentioning

confidence: 99%

The consequences of variant calling decisions in secondary analyses of cancer sequencing data

García-Prieto

Valencia

et al. 2020

Preprint

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The analysis of tumor genomes is a key step to understand many aspects of cancer biology, ranging from its aetiology to its treatment or the oncogenic processes driving cell transformation. The first crucial step in the analysis of any tumor genome is the identification of somatic genetic variants that cancer cells have acquired during their evolution. For that purpose, a wide range of somatic variant callers have been created in recent years. However, it is still unclear which variant caller, or combination of them, is best suited to analyze tumor sequencing data.Here we present a study to elucidate if different variant callers (MuSE, MuTect2, SomaticSniper, VarScan2) and strategies to combine them (Consensus and Union) lead to different downstream results in these three important aspects of cancer genomics: driver genes, mutational signatures and clinically actionable targets identification. To this end, we assess their performance in five different projects from The Cancer Genome Atlas (TCGA). Our results show that variant calling decisions have a significant impact on these downstream analyses, rendering important differences in driver genes prediction and mutational status among variant call sets, as well as in the identification of clinically actionable targets. More importantly, it seems that there is not a one-size-fits-all variant calling strategy, as the optimal decision seems to depend on both, the cancer type and the goal of the analysis.Contact: eduard.porta@bsc.es; alfonso.valencia@bsc.es Fig. 6. Patients with at least one clinically actionable target identified by Cancer Genome Interpreter (CGI) classified per clinical evidence level.

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Section: Introductionmentioning

confidence: 99%

The consequences of variant calling decisions in secondary analyses of cancer sequencing data

García-Prieto

Valencia

et al. 2020

Preprint

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“…A Chi-square test and Cochran-Armitage trend test were used to detect the difference of the genotypic and allelic distribution between PCOS patients and healthy women, and one way analysis of variance (ANOVA) and Kruskal-Wallis tests were performed to detect the differences among three groups (Cai et al 2016b;Huang et al 2016). The Power and Sample Size Program was used to calculate the power (Dupont and Plummer 1990).…”

Section: Discussionmentioning

confidence: 99%

A Missense Variant rs4645843 in <i>TNF-</i><i>α</i> Gene Is a Risk Factor of Polycystic Ovary Syndrome in the Uygur Population

Zhao

Wan

2017

Tohoku J. Exp. Med.

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Polycystic ovary syndrome (PCOS) is a complex endocrine syndrome, resulting from the interaction of gene variants and environmental factors. PCOS is viewed as a proinflammatory state and is characterized by hyperandrogenism, hyperinsulinemia, and over-weight. In China, the incidence of PCOS is higher in the Uygur population than that in the Chinese Han population. The association of the tumor necrosis factor α (TNF-α ) gene with PCOS remains to be clarified. Here, we investigated the association of TNF-α polymorphisms with PCOS in the Uygur population (393 patients with PCOS and 381 healthy subjects). Two single-nucleotide polymorphisms in TNF-α were analyzed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method: rs1800629 (-308G/A polymorphism), a commonly tested variant and rs4645843 (6213C/T polymorphism) that causes a Pro-to-Leu substitution at position 84, the most damaging variant of TNF-α based on in silico analysis. We thus found that both the genotypic and allelic distributions of rs4645843 were significantly different between PCOS and control groups (p = 0.03 and 0.024, respectively), whereas those of rs1800629 were similar between the groups. Furthermore, rs4645843 was significantly associated with serum testosterone levels and the score of Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), but no such association was found with rs1800629. Importantly, both rs4645843 and rs1800629 were significantly associated with higher body mass index (p < 0.05). This is the first study that shows the association of TNF-α gene with PCOS in the Uygur population. The TNF-α gene may influence the pathogenesis of PCOS through regulating testosterone level, obesity and HOMA-IR.

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“…Single nucleotide variant calling: We evaluated five publicly available software packages capable of single nucleotide variant (SNV) calling: SAMtools v1.9 [22], VarScan2 v2.3.9 [32], Mutect2 v4.beta.3-SNAPSHOT [24], VarDict [25], and Pisces v5.2.0.1 [26]. All versions were the most recent available at the time of our study.…”

Section: Methodsmentioning

confidence: 99%

“…We investigated the performance of five variant callers in separating the gold-standard mutations from the non-mutated sites: SAMtools [22], VarScan2 [23], MuTect2 [24], VarDict [25] and Pisces [26]. See Methods for exact version numbers and other details.…”

Section: Variant Callers Disagree Greatly Under Default Configurationsmentioning

confidence: 99%

Accuracy and Reproducibility of Somatic Point Mutation Calling in Clinical-Type Targeted Sequencing Data

Karimnezhad

Palidwor

Thavorn

et al. 2019

Preprint

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Background: Treating cancer depends in part on identifying the mutations driving each patient's disease. Many clinical laboratories are adopting high-throughput sequencing for assaying patients' tumours, applying targeted panels to formalin-fixed paraffin-embedded tumour tissues to detect clinically-relevant mutations. While there have been some benchmarking and best practices studies of this scenario, much variantcalling work focuses on whole-genome or whole-exome studies, with fresh or fresh-frozen tissue. Thus, definitive guidance on best choices for sequencing platforms, sequencing strategies, and variant calling for clinical variant detection is still being developed.Results: Because ground truth for clinical specimens is rarely known, we used the well-characterized Coriell cell lines GM12878 and GM12877 to generate data. We prepared samples to mimic as closely as possible clinical biopsies, including formalin fixation and paraffin embedding. We evaluated two well-known targeted sequencing panels, Illumina's TruSight 170 panel and the Oncomine Focus panel. Sequencing was performed on an Illumina NextSeq500 and an Ion Torrent PGM respectively. We performed multiple biological replicates of each assay, to test reproducibility. Finally, we applied five different public and freely-available somatic single-nucleotide variant (SNV) callers to the data, MuTect2, SAMtools, VarScan2, Pisces and VarDict. Although the TruSight 170 and Oncomine Focus panels cover different amounts of the genome, we did not observe major differences in variant calling success within the regions that each covers. We observed substantial discrepancies between the five variant callers. All had high sensitivity, detecting known SNVs, but highly varying and non-overlapping false positive detections. Harmonizing variant caller parameters or intersecting the results of multiple variant callers reduced disagreements. However, intersecting results from biological replicates was even better at eliminating false positives.Conclusions: Reproducibility and accuracy of targeted clinical sequencing results depends less on sequencing platform and panel than on downstream bioinformatics and biological variability. Differences in variant callers' default parameters are a greater influence on algorithm disagreement than other differences between the algorithms. Contrary to typical clinical practice, we recommend analyzing replicate samples, as this greatly decreases false positive calls.

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

Cited by 106 publications

References 45 publications

The consequences of variant calling decisions in secondary analyses of cancer sequencing data

The consequences of variant calling decisions in secondary analyses of cancer sequencing data

A Missense Variant rs4645843 in <i>TNF-</i><i>α</i> Gene Is a Risk Factor of Polycystic Ovary Syndrome in the Uygur Population

Accuracy and Reproducibility of Somatic Point Mutation Calling in Clinical-Type Targeted Sequencing Data

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