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Identification of Mutated Cancer Driver Genes in Unpaired RNA-Seq Samples

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Cancer Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1878))

Abstract

The identification of cancer driver genes through the analysis of mutations detected with high-throughput sequencing is a useful tool and a key challenge in cancer genomics. The workflow presented here relies on unpaired RNA-seq tumoral samples, thus leveraging already available RNA-seq data and providing the intrinsical benefits of directly targeting the transcriptome. Based on well-established methods for variant detection, this workflow also involves thorough data cleaning and extensive annotation, which enable the selection for somatic mutations with functional impact and the prioritization of genes relevant to the carcinogenic processes in the input samples.

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Author information

Authors and Affiliations

  1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    David Mosen-Ansorena

  2. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    David Mosen-Ansorena

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  1. David Mosen-Ansorena
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Correspondence to David Mosen-Ansorena .

Editor information

Editors and Affiliations

  1. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Alexander Krasnitz

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Mosen-Ansorena, D. (2019). Identification of Mutated Cancer Driver Genes in Unpaired RNA-Seq Samples. In: Krasnitz, A. (eds) Cancer Bioinformatics. Methods in Molecular Biology, vol 1878. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8868-6_5

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  • DOI: https://doi.org/10.1007/978-1-4939-8868-6_5

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8866-2

  • Online ISBN: 978-1-4939-8868-6

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