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Cited by 4,981

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples

Open access
Cibulskis, Kristian, Lawrence, Michael S, Carter, Scott L, Sivachenko, Andrey, Jaffe, David, Sougnez, Carrie, Gabriel, Stacey, Meyerson, Matthew, Lander, Eric S, Getz, Gad·2013·Nature Biotechnology
DOI 10.1038/nbt.2514
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Abstract

Detection of somatic point substitutions is a key step in characterizing the cancer genome. However, existing methods typically miss low-allelic-fraction mutations that occur in only a subset of the sequenced cells owing to either tumor heterogeneity or contamination by normal cells. Here we present MuTect, a method that applies a Bayesian classifier to detect somatic mutations with very low allele fractions, requiring only a few supporting reads, followed by carefully tuned filters that ensure high specificity. We also describe benchmarking approaches that use real, rather than simulated, sequencing data to evaluate the sensitivity and specificity as a function of sequencing depth, base quality and allelic fraction. Compared with other methods, MuTect has higher sensitivity with similar specificity, especially for mutations with allelic fractions as low as 0.1 and below, making MuTect particularly useful for studying cancer subclones and their evolution in standard exome and genome sequencing data.

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Show all 17 references

Research works printed but not linked: 29

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Cited by

Items 101–200 of 236

Metadata

Authors
Cibulskis, K., Lawrence, M. S., Carter, S. L., Sivachenko, A., Jaffe, D., Sougnez, C., Gabriel, S., Meyerson, M., Lander, E. S., & Getz, G.
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2013-02-10
Volume
31
Issue
3
Pages
213-219
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
DOI
10.1038/nbt.2514
OpenAlex
W1527347967
Cited by
4,981
References
17
FWCI
71.81
Type
journal article
Language
en
Open access
bronze
Licence
www.springer.com/tdm (tdm)

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