천종식 (서울대학교) | 상위피인용논문 > 한빛사

상위피인용논문

조회49

서울대학교

CV File 455 kb

J. Microbiol., 2021 May;59(5):476-480 | https://doi.org/10.1007/s12275-021-1154-0 Introducing EzAAI: a pipeline for high throughput calculations of prokaryotic average amino acid identity

Dongwook Kim ^{1 †}, Sein Park ^{1 †}, Jongsik Chun ^{1 *}

¹Interdisciplinary Program in Bioinformatics, Institute of Molecular Biology & Genetics, School of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea.

^†These authors contributed equally
^*Corresponding author: correspondence to Jongsik Chun

Abstract

The average amino acid identity (AAI) is an index of pairwise genomic relatedness, and multiple studies have proposed its application in prokaryotic taxonomy and related disciplines. AAI demonstrates better resolution in elucidating taxonomic structure beyond the species rank when compared with average nucleotide identity (ANI), which is a standard criterion in species delineation. However, an efficient and easy-to-use computational tool for AAI calculation in large-scale taxonomic studies is not yet available. Here, we introduce a bioinformatic pipeline, named EzAAI, which allows for rapid and accurate AAI calculation in prokaryote sequences. The EzAAI tool is based on the MMSeqs2 program and computes AAI values almost identical to those generated by the standard BLAST algorithm with significant improvements in the speed of these evaluations. Our pipeline also provides a function for hierarchical clustering to create dendrograms, which is an essential part of any taxonomic study. EzAAI is available for download as a standalone JAVA program at http://leb.snu.ac.kr/ezaai.

논문정보

형식Research article
게재일2021년 05월 (BRIC 등록일 2025-01-08)
연구진국내 연구진
분야 생명과학 > 분류/생태/환경생물학
피인용횟수120회 이상 인용된 논문

댓글 작성 로그인

CV 열람하기

연락처 보기