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ANItools web: a web tool for fast genome comparison within multiple bacterial strains.

Na Han1, Yujun Qiang1, Wen Zhang2

  • 1State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China.

Database : the Journal of Biological Databases and Curation
|June 9, 2016
PubMed
Summary

ANItools web provides a user-friendly platform for calculating average nucleotide identity (ANI), a key metric for bacterial species classification. This tool aids in defining relationships between bacterial strains using whole-genome data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Microbial Taxonomy

Background:

  • Prokaryote classification historically relied on phenotypic traits.
  • Advances in genetic sequencing have shifted focus to genomic data for accurate classification.
  • Average Nucleotide Identity (ANI) is a proposed metric for bacterial species definition based on whole-genome comparisons.

Purpose of the Study:

  • To develop a web-based tool, ANItools, for calculating Average Nucleotide Identity (ANI).
  • To facilitate bacterial classification and identification using genomic data through an accessible online platform.

Main Methods:

  • Development of a web application (ANItools) for calculating ANI values.
  • Inclusion of a comprehensive database of ANI values for various bacterial genera and species.
  • Implementation of automated genome comparison and graphical report generation.

Main Results:

  • The ANItools web server allows direct retrieval of ANI values from online sources.
  • The integrated database contains ANI data for 2773 strains across 668 genera.
  • Automated comparison of user-input genomes with database entries provides graphical ANI calculation results.

Conclusions:

  • ANItools web serves as a valuable resource for understanding relationships between bacterial strains.
  • The tool enhances bacterial species classification and identification by leveraging whole-genome information.