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TaxonKit: A practical and efficient NCBI taxonomy toolkit.

Wei Shen1, Hong Ren1

  • 1Institute for Viral Hepatitis, Department of Infectious Diseases, Key Laboratory of Molecular Biology for Infectious Diseases, Ministry of Education, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China.

Journal of Genetics and Genomics = Yi Chuan Xue Bao
|May 18, 2021
PubMed
Summary

TaxonKit is a new command-line toolkit that efficiently manipulates National Center for Biotechnology Information (NCBI) Taxonomy data. It offers comprehensive functions for querying, filtering, and reformatting taxonomic information, improving data analysis in biology.

Keywords:
LineageNCBI TaxonomyTaxIdTaxId changelogTaxonKit

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The National Center for Biotechnology Information (NCBI) Taxonomy is crucial for biomedical and ecological research.
  • Existing tools for manipulating NCBI Taxonomy data often lack comprehensive functionality or efficient performance.
  • Common tasks include querying taxonomy identifiers (TaxIds) by name, retrieving lineages, and listing descendants.

Purpose of the Study:

  • To introduce TaxonKit, a command-line toolkit designed for efficient and comprehensive manipulation of NCBI Taxonomy data.
  • To provide a tool that addresses the limitations of existing NCBI Taxonomy data manipulation software.
  • To facilitate various taxonomy data operations, including querying, filtering, lineage retrieval, and change tracking.

Main Methods:

  • Development of TaxonKit, a command-line toolkit with seven core subcommands.
  • Implementation of functions for TaxId querying, listing, filtering, lineage retrieval and reformatting, lowest common ancestor computation, and TaxId change tracking.
  • Focus on competitive processing performance, scalability, and accessibility.

Main Results:

  • TaxonKit offers a comprehensive suite of functions for manipulating NCBI Taxonomy data.
  • The toolkit demonstrates competitive processing performance and scalability across different dataset sizes.
  • Practical functions and good accessibility are provided to aid taxonomy data manipulations.

Conclusions:

  • TaxonKit provides an efficient and versatile solution for handling NCBI Taxonomy data.
  • The toolkit's capabilities can significantly facilitate research in biomedical and ecological fields.
  • TaxonKit is freely accessible under the MIT license with comprehensive documentation available.