Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection

By A. Ewing, Kathleen E. Houlahan, Yin Hu, K. Ellrott, Cristian Caloian, Takafumi N. Yamaguchi, J. Christopher Bare, Christine P'ng, Daryl Waggott, Veronica Y. Sabelnykova and 8 more. Nature Methods, 2015

314 citations recorded by the source corpus.

The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f8886c96365a8c2ff9cb95290affebe42a8459bf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Kristian Cibulskis
2. Gad Getz

In Genetics

1. Chip Stewart
2. Eytan Ruppin
3. Gad Getz
4. Andrea Soltoggio

In Molecular Biology

1. Chip Stewart
2. Eytan Ruppin
3. Gad Getz
4. Bernhard O. Palsson

Citations in the corpus

• Calling Somatic SNVs and Indels with Mutect2 (2019, methodology): “These bams are from real data, with “somatic variants” spiked in in silico using BamSurgeon [3] and hence have known truth data.”
• VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research (2016, methodology): “A number of regularly cited variant callers, such as GATK (3), FreeBayes (http: //arxiv.org/abs/1207.3907) and VarScan (4) are designed to call SNV and small InDels separately, but not complex combinations of these events.”
• Precision Oncology: The Road Ahead (2017, background): “Although the practical utility of RNA-seq in the clinic has been challenging, technological advances allowing the application of RNA-seq to clinically-relevant specimens (including formalin-fixed, paraffin-embedded tissues), along with efforts to benchmark data analysis pipelines (ICGC-TCGA DREAM So”
• Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines (2018, unknown): “278 Cell Systems 6, 271–281, March 28, 2018 was informed by theDREAMchallenge (Ewing et al., 2015), many of the methods selected were based on best practices of the original TCGA AWGs.”