A three-caller pipeline for variant analysis of cancer whole-exome sequencing data

Liu,Ze‑Kun; Shang,Yu‑Kui; Chen,Zhi‑Nan; Bian,Huijie

doi:10.3892/mmr.2017.6336

A three-caller pipeline for variant analysis of cancer whole-exome sequencing data

Authors:
- Ze‑Kun Liu
- Yu‑Kui Shang
- Zhi‑Nan Chen
- Huijie Bian
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Affiliations: Department of Cell Biology and National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, Shaanxi 710032, P.R. China
Published online on: March 16, 2017 https://doi.org/10.3892/mmr.2017.6336
Pages: 2489-2494
Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Rapid advancements in next generation sequencing (NGS) technologies, coupled with the dramatic decrease in cost, have made NGS one of the leading approaches applied in cancer research. In addition, it is increasingly used in clinical practice for cancer diagnosis and treatment. Somatic (cancer‑only) single nucleotide variants and small insertions and deletions (indels) are the simplest classes of mutation, however, their identification in whole exome sequencing data is complicated by germline polymorphisms, tumor heterogeneity and errors in sequencing and analysis. An increasing number of software and methodological guidelines are being published for the analysis of sequencing data. Usually, the algorithms of MuTect, VarScan and Genome Analysis Toolkit are applied to identify the variants. However, one of these algorithms alone results in incomplete genomic information. To address this issue, the present study developed a systematic pipeline for analyzing the whole exome sequencing data of hepatocellular carcinoma (HCC) using a combination of the three algorithms, named the three‑caller pipeline. Application of the three‑caller pipeline to the whole exome data of HCC, improved the detection of true positive mutations and a total of 75 tumor‑specific somatic variants were identified. Functional enrichment analysis revealed the mutations in the genes encoding cell adhesion and regulation of Ras GTPase activity. This pipeline provides an effective approach to identify variants from NGS data for subsequent functional analyses.

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