Construction of a combinatorial pipeline using two somatic variant  calling  methods  for whole exome sequence data of gastric cancer

High-throughput next-generation sequencing is a powerful tool to identify the genotypic landscapes of somatic variants and therapeutic targets in various cancers including gastric cancer, forming the basis for personalized medicine in the clinical setting. Although the advent of many computational a...

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Bibliographic Details
Published inThe Journal of Medical Investigation Vol. 64; no. 3.4; pp. 233 - 240
Main Authors Kohmoto, Tomohiro, Masuda, Kiyoshi, Naruto, Takuya, Tange, Shoichiro, Shoda, Katsutoshi, Hamada, Junichi, Saito, Masako, Ichikawa, Daisuke, Tajima, Atsushi, Otsuji, Eigo, Imoto, Issei
Format Journal Article
LanguageEnglish
Published Japan The University of Tokushima Faculty of Medicine 2017
Subjects
Aged
Aged, 80 and over
Algorithms
Exome
Female
Gastric cancer
High-Throughput Nucleotide Sequencing
Humans
Male
Middle Aged
Mutation
somatic mutations
Stomach Neoplasms - genetics
variant calling algorithms
somatic mutations
exome
Gastric cancer
variant calling algorithms
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Summary:High-throughput next-generation sequencing is a powerful tool to identify the genotypic landscapes of somatic variants and therapeutic targets in various cancers including gastric cancer, forming the basis for personalized medicine in the clinical setting. Although the advent of many computational algorithms leads to higher accuracy in somatic variant calling, no standard method exists due to the limitations of each method. Here, we constructed a new pipeline. We combined two different somatic variant callers with different algorithms, Strelka and VarScan 2, and evaluated performance using whole exome sequencing data obtained from 19 Japanese cases with gastric cancer (GC); then, we characterized these tumors based on identified driver molecular alterations. More single nucleotide variants (SNVs) and small insertions/deletions were detected by Strelka and VarScan 2, respectively. SNVs detected by both tools showed higher accuracy for estimating somatic variants compared with those detected by only one of the two tools and accurately showed the mutation signature and mutations of driver genes reported for GC. Our combinatorial pipeline may have an advantage in detection of somatic mutations in GC and may be useful for further genomic characterization of Japanese patients with GC to improve the efficacy of GC treatments. J. Med. Invest. 64: 233-240, August, 2017
ISSN:1343-1420
1349-6867
DOI:10.2152/jmi.64.233