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A simple consensus approach improves somatic mutation prediction accuracy.


ABSTRACT: Differentiating true somatic mutations from artifacts in massively parallel sequencing data is an immense challenge. To develop methods for optimal somatic mutation detection and to identify factors influencing somatic mutation prediction accuracy, we validated predictions from three somatic mutation detection algorithms, MuTect, JointSNVMix2 and SomaticSniper, by Sanger sequencing. Full consensus predictions had a validation rate of >98%, but some partial consensus predictions validated too. In cases of partial consensus, read depth and mapping quality data, along with additional prediction methods, aided in removing inaccurate predictions. Our consensus approach is fast, flexible and provides a high-confidence list of putative somatic mutations.

SUBMITTER: Goode DL 

PROVIDER: S-EPMC3978449 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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A simple consensus approach improves somatic mutation prediction accuracy.

Goode David L DL   Hunter Sally M SM   Doyle Maria A MA   Ma Tao T   Rowley Simone M SM   Choong David D   Ryland Georgina L GL   Campbell Ian G IG  

Genome medicine 20130930 9


Differentiating true somatic mutations from artifacts in massively parallel sequencing data is an immense challenge. To develop methods for optimal somatic mutation detection and to identify factors influencing somatic mutation prediction accuracy, we validated predictions from three somatic mutation detection algorithms, MuTect, JointSNVMix2 and SomaticSniper, by Sanger sequencing. Full consensus predictions had a validation rate of >98%, but some partial consensus predictions validated too. In  ...[more]

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