iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data

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.

Keywords: Bioinformatics; Cancer; Docker; Pipeline; Whole exome sequencing.

Figure 1

Flowchart diagram of the iWhale pipeline. The software runs under Docker and all the steps are managed by SCons.

Figure 2

(A) Venn diagram displaying the comparison of the detected variants found by the three variant callers used by iWhale. (B) Performance evaluation of iWhale, using simulated paired tumor and control WES data.