A simple consensus approach improves somatic mutation prediction accuracy

David Goode, Sally M Hunter, Maria A Doyle, Tao Ma, Simone M Rowley, David Choong, Georgina L Ryland, Ian G Campbell

Research output: Contribution to journalArticleResearchpeer-review

31 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1 - 14
Number of pages14
JournalGenome Medicine
Issue number9 (Article # 90)
Publication statusPublished - 2013

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