ArticlePloS one2017
Comprehensive benchmarking of SNV callers for highly admixed tumor data.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- An alignment-free strategy for circulating tumor DNA detection and tumor fraction estimation from whole-genome sequencing data.PLoS computational biology · 2026Article
- A Modular and Programmable Cas13d Platform for RNA Single Nucleotide Variant Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Benchmarking within-sample minority variant detection with short-read sequencing inbioRxiv : the preprint server for biology · 2026Article
- Comprehensive benchmarking of methods for mutation calling in circulating tumor DNA.Nature communications · 2025Article
- Personalizing DNA Cancer Vaccines.Journal of personalized medicine · 2025Review
- Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens.Journal of biomedical science · 2024Review
- Consistency of a clinical decision support system with molecular tumour board recommendations for tumour sequencing-guided treatment of pancreatic cancer.ESMO gastrointestinal oncology · 2024Article
- Whole patient knowledge modeling of COVID-19 symptomatology reveals common molecular mechanisms.Frontiers in molecular medicine · 2022Article
- The COVID-19 explorer-An integrated, whole patient knowledge model of COVID-19 disease.Frontiers in molecular medicine · 2022Article
- Detecting Genetic Variation of ColonizingFrontiers in bioinformatics · 2022Article
- FiNGS: high quality somatic mutations using filters for next generation sequencing.BMC bioinformatics · 2021Article
- Detailed evaluation of cancer sequencing pipelines in different microenvironments and heterogeneity levels.Turkish journal of biology = Turk biyoloji dergisi · 2021Article
- Best practices for bioinformatic characterization of neoantigens for clinical utility.Genome medicine · 2019Review
- NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer.BMC medical genomics · 2019Article
- Calling Variants in the Clinic: Informed Variant Calling Decisions Based on Biological, Clinical, and Laboratory Variables.Computational and structural biotechnology journal · 2019Review
Corrections and comments
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3 authors.
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Abstract
Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.
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