Efficient computation of contributional diversity metrics from microbiome data with FuncDiv

Gavin M. Douglas, Sunu Kim, Morgan G.I. Langille, B. Jesse Shapiro

Résultat de recherche: Articleexamen par les pairs

5 Citations (Scopus)

Résumé

MOTIVATION: Microbiome datasets with taxa linked to the functions (e.g. genes) they encode are becoming more common as metagenomics sequencing approaches improve. However, these data are challenging to analyze due to their complexity. Summary metrics, such as the alpha and beta diversity of taxa contributing to each function (i.e. contributional diversity), represent one approach to investigate these data, but currently there are no straightforward methods for doing so. RESULTS: We addressed this gap by developing FuncDiv, which efficiently performs these computations. Contributional diversity metrics can provide novel insights that would be impossible to identify without jointly considering taxa and functions. AVAILABILITY AND IMPLEMENTATION: FuncDiv is distributed under a GNU Affero General Public License v3.0 and is available at https://github.com/gavinmdouglas/FuncDiv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Langue d'origineEnglish
JournalBioinformatics
Volume39
Numéro de publication1
DOI
Statut de publicationPublished - janv. 1 2023

Note bibliographique

Publisher Copyright:
© The Author(s) 2022. Published by Oxford University Press.

ASJC Scopus Subject Areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

PubMed: MeSH publication types

  • Journal Article

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Citer

Douglas, G. M., Kim, S., Langille, M. G. I., & Shapiro, B. J. (2023). Efficient computation of contributional diversity metrics from microbiome data with FuncDiv. Bioinformatics, 39(1). https://doi.org/10.1093/bioinformatics/btac809