A survey of tools for variant analysis of next-generation genome sequencing data

Abstract

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.

Keywords: Mendelian disorders; bioinformatics tools; cancer; next-generation sequencing; variants.

Figure 1:

Basic workflow for whole-exome and whole-genome sequencing projects. After library preparation, samples are sequenced on a certain platform. The next steps are quality assessment and read alignment against a reference genome, followed by variant identification. Detected mutations are then annotated to infer the biological relevance and results can be displayed using dedicated tools. The found mutations can further be prioritized and filtered, followed by validation of the generated results in the lab.

Figure 2:

Venn diagrams showing the number of identified variants for tested germline (A), somatic (B), CNV (C) and exome CNV (D) tools. The depicted numbers in (A) and (B) report identified SNPs and INDELs. Venn diagram (C) shows the overlap between known (cnv_sim) and predicted CNVs. Figure (D) illustrates the overlap between CONTRA and ExomeCNV. The intersection numbers were adjusted to reflect that 10 CNVs detected by CONTRA are located within 3 CNVs reported by ExomeCNV.