The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Single molecule molecular inversion probes for targeted, high-accuracy detection of low-frequency variation
Open accessAbstract
The detection and quantification of genetic heterogeneity in populations of cells is fundamentally important to diverse fields, ranging from microbial evolution to human cancer genetics. However, despite the cost and throughput advances associated with massively parallel sequencing, it remains challenging to reliably detect mutations that are present at a low relative abundance in a given DNA sample. Here we describe smMIP, an assay that combines single molecule tagging with multiplex targeted capture to enable practical and highly sensitive detection of low-frequency or subclonal variation. To demonstrate the potential of the method, we simultaneously resequenced 33 clinically informative cancer genes in eight cell line and 45 clinical cancer samples. Single molecule tagging facilitated extremely accurate consensus calling, with an estimated per-base error rate of 8.4 × 10−6 in cell lines and 2.6 × 10−5 in clinical specimens. False-positive mutations in the single molecule consensus base-calls exhibited patterns predominantly consistent with DNA damage, including 8-oxo-guanine and spontaneous deamination of cytosine. Based on mixing experiments with cell line samples, sensitivity for mutations above 1% frequency was 83% with no false positives. At clinically informative sites, we identified seven low-frequency point mutations (0.2%–4.7%), including BRAF p.V600E (melanoma, 0.2% alternate allele frequency), KRAS p.G12V (lung, 0.6%), JAK2 p.V617F (melanoma, colon, two lung, 0.3%–1.4%), and NRAS p.Q61R (colon, 4.7%). We anticipate that smMIP will be broadly adoptable as a practical and effective method for accurately detecting low-frequency mutations in both research and clinical settings.
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Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Fast and accurate long-read alignment with Burrows-Wheeler transform.
Li, H., & Durbin, R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp698 · Cited by 12,063
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774
Mapping and analysis of chromatin state dynamics in nine human cell types
Ernst, J., Kheradpour, P., Mikkelsen, T. S., Shoresh, N., Ward, L. D., Epstein, C. B., Zhang, X., Wang, L., Issner, R., Coyne, M., et al. (2011) · Nature · doi:10.1038/nature09906 · Cited by 2,898
Circulating mutant DNA to assess tumor dynamics
Diehl, F., Schmidt, K., Choti, M. A., Romans, K., Goodman, S., Li, M., Thornton, K., Agrawal, N., Sokoll, L., Szabo, S. A., et al. (2008) · Nature Medicine · doi:10.1038/nm.1789 · Cited by 2,734
Tumour evolution inferred by single-cell sequencing
Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., et al. (2011) · Nature · doi:10.1038/nature09807 · Cited by 2,598
COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer
Forbes, S. A., Bindal, N., Bamford, S., Cole, C., Kok, C. Y., Beare, D., Jia, M., Shepherd, R., Leung, K., Menzies, A., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq929 · Cited by 2,278
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Schuurhuis, G. J., Heuser, M., Freeman, S., Béné, M.-C., Buccisano, F., Cloos, J., Grimwade, D., Haferlach, T., Hills, R. K., Hourigan, C. S., et al. (2018) · Blood · doi:10.1182/blood-2017-09-801498 · Cited by 1,041
Guidelines for Validation of Next-Generation Sequencing–Based Oncology Panels
Jennings, L. J., Arcila, M. E., Corless, C., Kamel-Reid, S., Lubin, I. M., Pfeifer, J., Temple-Smolkin, R. L., Voelkerding, K. V., & Nikiforova, M. N. (2017) · The Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2017.01.011 · Cited by 743
Inherited causes of clonal haematopoiesis in 97,691 whole genomes
Bick, A. G., Weinstock, J. S., Nandakumar, S. K., Fulco, C. P., Bao, E. L., Zekavat, S. M., Szeto, M. D., Liao, X., Leventhal, M. J., Nasser, J., et al. (2020) · Nature · doi:10.1038/s41586-020-2819-2 · Cited by 733
Stessman, H. A. F., Xiong, B., Coe, B. P., Wang, T., Hoekzema, K., Fenckova, M., Kvarnung, M., Gerdts, J., Trinh, S., Cosemans, N., et al. (2017) · Nature Genetics · doi:10.1038/ng.3792 · Cited by 559
Metadata
- Authors
- Hiatt, J. B., Pritchard, C. C., Salipante, S. J., O'Roak, B. J., & Shendure, J.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2013-02-04
- Volume
- 23
- Issue
- 5
- Pages
- 843-854
- ISSN
- 1088-9051 (ISSN-L)
- OpenAlex
- W1999790790
- Cited by
- 327
- References
- 56
- Type
- posted content
- Language
- en
- Open access
- bronze
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- Hiatt, J. B., Pritchard, C. C., Salipante, S. J., O'Roak, B. J., & Shendure, J. (2013). Single molecule molecular inversion probes for targeted, high-accuracy detection of low-frequency variation. Genome Research. https://doi.org/10.1101/gr.147686.112
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