OutLyzer: software for extracting low-allele-frequency tumor mutations from sequencing background noise in clinical practice
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Etienne Muller1,2, Nicolas Goardon1, Baptiste Brault1, Antoine Rousselin1, Germain Paimparay1, Angelina Legros1, Robin Fouillet1, Olivia Bruet1, Aurore Tranchant1, Florian Domin1, Chankannira San1, Céline Quesnelle1, Thierry Frebourg2,3,4, Agathe Ricou1, Sophie Krieger1,2,5, Dominique Vaur1,2, Laurent Castera1,2
1Department of Cancer Biology and Genetics, CCC François Baclesse, Genomic and Personalized Medicine in Cancer and Neurological Disorders Unit, Caen, France
2Inserm U1079, Genomic and Personalized Medicine in Cancer and Neurological Disorders Unit, Rouen, France
3Genetic Department, Rouen University Hospital, Genomic and Personalized Medicine in Cancer and Neurological Disorders Unit, Rouen, France
4Rouen University, France
5Caen University, France
Laurent Castéra, email: email@example.com
Keywords: variant-caller, somatic mutation, bioinformatics, oncology, precision medicine
Received: May 26, 2016 Accepted: October 11, 2016 Published: November 04, 2016
Highlighting tumoral mutations is a key step in oncology for personalizing care. Considering the genetic heterogeneity in a tumor, software used for detecting mutations should clearly distinguish real tumor events of interest that could be predictive markers for personalized medicine from false positives. OutLyzer is a new variant-caller designed for the specific and sensitive detection of mutations for research and diagnostic purposes. It is based on statistic and local evaluation of sequencing background noise to highlight potential true positive variants. 130 previously genotyped patients were sequenced after enrichment by capturing the exons of 22 genes. Sequencing data were analyzed by HaplotypeCaller, LofreqStar, Varscan2 and OutLyzer. OutLyzer had the best sensitivity and specificity with a fixed limit of detection for all tools of 1% for SNVs and 2% for Indels. OutLyzer is a useful tool for detecting mutations of interest in tumors including low allele-frequency mutations, and could be adopted in standard practice for delivering targeted therapies in cancer treatment.
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