A portable and scalable workflow for detecting structural variants in whole-genome sequencing data

摘要

I. Introduction Cancer affects millions of people worldwide. With the advent of novel DNA sequencing technologies, whole genome sequencing (WGS) is becoming an integral part of cancer diagnostics that can potentially enable tailored treatments of individual patients. Despite the advances in large-scale cancer genomics projects (such as TCGA and ICGC's PCAWG) systematic and comprehensive analysis of massive genomic data, in particular the detection and interpretation of structural variations (SVs) in the genomes, remains challenging due to computational and algorithmic limitations [1-2]. A range of methods is available to detect SVs in short-read sequencing data, each producing different results. Therefore, comprehensive SV detection requires the use of multiple methods or tools (callers). In fact, most SV callers implement more than one approach including evidence from split read information, discordantly aligned read pairs, read depth and short-read assembly to improve sensitivity and/or specificity [3-6]. Alternatively, multiple tools, often written in different languages, can be readily combined using a workflow management system. However, a workflow developed on one computing system is not necessarily portable to or reusable on another system due to the complexity of software environments involved, system usage policies or the use of different batch schedulers by HPC clusters (e.g. Grid Engine, Slurm or Torque).