Evaluation of SNP calling methods for closely related bacterial isolates and a novel high-accuracy pipeline: BactSNP

Dai Yoshimura, Rei Kajitani, Yasuhiro Gotoh, Katsuyuki Katahira, Miki Okuno, Yoshitoshi Ogura, Tetsuya Hayashi, Takehiko Itoh

研究成果: Contribution to journalArticle査読

22 被引用数 (Scopus)


Bacteria are highly diverse, even within a species; thus, there have been many studies which classify a single species into multiple types and analyze the genetic differences between them. Recently, the use of whole-genome sequencing (WGS) has been popular for these analyses, and the identification of single-nucleotide polymorphisms (SNPs) between isolates is the most basic analysis performed following WGS. The performance of SNP-calling methods therefore has a significant effect on the accuracy of downstream analyses, such as phylogenetic tree inference. In particular, when closely related isolates are analyzed, e.g. in outbreak investigations, some SNP callers tend to detect a high number of false-positive SNPs compared with the limited number of true SNPs among isolates. However, the performances of various SNP callers in such a situation have not been validated sufficiently. Here, we show the results of realistic benchmarks of commonly used SNP callers, revealing that some of them exhibit markedly low accuracy when target isolates are closely related. As an alternative, we developed a novel pipeline BactSNP, which utilizes both assembly and mapping information and is capable of highly accurate and sensitive SNP calling in a single step. BactSNP is also able to call SNPs among isolates when the reference genome is a draft one or even when the user does not input the reference genome. BactSNP is available at https://github.com/IEkAdN/BactSNP.

ジャーナルMicrobial Genomics
出版ステータス出版済み - 5 1 2019

All Science Journal Classification (ASJC) codes

  • 疫学
  • 微生物学
  • 分子生物学
  • 遺伝学


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