The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection
Open accessAbstract
The first report of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge introduces the BAMSurgeon tool for accurate tumor simulation and reports the performance of 248 submissions in calling single-nucleotide variants from three pairs of synthetic tumor–normal genome benchmarks. 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/ .
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Cites
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Cited by
Best practices for benchmarking germline small-variant calls in human genomes
the Global Alliance for Genomics and Health Benchmarking Team, Krusche, P., Trigg, L., Boutros, P. C., Mason, C. E., De La Vega, F. M., Moore, B. L., Gonzalez-Porta, M., Eberle, M. A., Tezak, Z., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0054-x · Cited by 429
Self-citationOlson, N. D., Wagner, J., McDaniel, J., Stephens, S. H., Westreich, S. T., Prasanna, A. G., Johanson, E., Boja, E., Maier, E. J., Serang, O., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100129 · Cited by 202
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Transposable element detection from whole genome sequence data
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Self-citationSpearheading future omics analyses using dyngen, a multi-modal simulator of single cells
Cannoodt, R., Saelens, W., Deconinck, L., & Saeys, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-24152-2 · Cited by 124
Metadata
- Authors
- participants, I.-T. D. S. M. C. C., Ewing, A. D., Houlahan, K. E., Hu, Y., Ellrott, K., Caloian, C., Yamaguchi, T. N., Bare, J. C., P'ng, C., Waggott, D., Sabelnykova, V. Y., Kellen, M. R., Norman, T. C., Haussler, D., Friend, S. H., Stolovitzky, G., Margolin, A. A., Stuart, J. M., & Boutros, P. C.
- Venue
- Nature Methods
- Published
- 2015-05-18
- Volume
- 12
- Issue
- 7
- Pages
- 623-630
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W1565378970
- Cited by
- 332
- References
- 40
- FWCI
- 10.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- participants, I.-T. D. S. M. C. C., Ewing, A. D., Houlahan, K. E., Hu, Y., Ellrott, K., Caloian, C., Yamaguchi, T. N., Bare, J. C., P'ng, C., Waggott, D., Sabelnykova, V. Y., Kellen, M. R., Norman, T. C., Haussler, D., Friend, S. H., Stolovitzky, G., Margolin, A. A., Stuart, J. M., & Boutros, P. C. (2015). Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nature Methods. https://doi.org/10.1038/nmeth.3407
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
