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TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning Filtering

View ORCID ProfileDonald Freed, Renke Pan, Rafael Aldana
doi: https://doi.org/10.1101/250647
Donald Freed
Sentieon Inc., Mountain View, CA.
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  • For correspondence: don.freed@sentieon.com
Renke Pan
Sentieon Inc., Mountain View, CA.
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Rafael Aldana
Sentieon Inc., Mountain View, CA.
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Abstract

Detection of somatic mutations in tumor samples is important in the clinic, where treatment decisions are increasingly based upon molecular diagnostics. However, accurate detection of these mutations is difficult, due in part to intra-tumor heterogeneity, contamination of the tumor sample with normal tissue and pervasive structural variation. Here, we describe Sentieon TNscope, a haplotype-based somatic variant caller with increased accuracy relative to existing methods. An early engineering version of TNscope was used in our submission to the most recent ICGC-DREAM Somatic Mutation calling challenge. In that challenge, TNscope is the leader in accuracy for SNVs, indels and SVs. To further improve variant calling accuracy, we combined the improvements in the variant caller with machine learning. We benchmarked TNscope using in-silico mixtures of well-characterized Genome in a Bottle (GIAB) samples. TNscope displays higher accuracy than the other benchmarked tools and the accuracy is substantially improved by the machine learning model.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 19, 2018.
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TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning Filtering
Donald Freed, Renke Pan, Rafael Aldana
bioRxiv 250647; doi: https://doi.org/10.1101/250647
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TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning Filtering
Donald Freed, Renke Pan, Rafael Aldana
bioRxiv 250647; doi: https://doi.org/10.1101/250647

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