Abstract
Whole exome sequencing (WES) is a powerful approach for discovering sequence variants in cancer cells but its time effectiveness is limited by the complexity and issues of WES data analysis. Here we present iWhale, a customizable pipeline based on Docker and SCons, reliably detecting somatic variants by three complementary callers (MuTect2, Strelka2 and VarScan2). The results are combined to obtain a single variant call format file for each sample and variants are annotated by integrating a wide range of information extracted from several reference databases, ultimately allowing variant and gene prioritization according to different criteria. iWhale allows users to conduct a complex series of WES analyses with a powerful yet customizable and easy-to-use tool, running on most operating systems (macOs, GNU/Linux and Windows).iWhale code is freely available at https://github.com/alexcoppe/iWhale and the docker image is downloadable from https://hub.docker.com/r/alexcoppe/iwhale.
Highlights
Malignant transformation of cells is driven by many factors, including the development of somatic mutations that may affect signalling pathways which govern cell behavior and the expression of cancer hallmarks [1]
Leveraging our experience in the field of Whole exome sequencing (WES) applied to cancer research [8, 20,21,22], we present iWhale, an automated, easy-to-use and customizable software pipeline
Tumor samples were obtained by spiking variants in HG00246 and NA20505 WES samples obtained from the International Genome Sample Resource [31], which were used as controls (Table 1)
Summary
Malignant transformation of cells is driven by many factors, including the development of somatic mutations that may affect signalling pathways which govern cell behavior and the expression of cancer hallmarks [1] Both molecular and bioinformatics advancements are helping early diagnosis of the disease as well as the identification of better therapeutic strategies facilitating personalized medicine [2]. Silvia Bresolin is assistant professor at the Department of Women’s and Children’s Health of the University of Padova Her field of interest is generation sequencing and transcriptoma data generation and analysis in hematological pediatric malignancies. She leads a computational genomics group committed to cancer research and teaches biology and bioinformatics. Submitted: 27 December 2019; Received (in revised form): 27 March 2020
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