Efficient computation of contributional diversity metrics from microbiome data with FuncDiv

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

doi:10.1093/bioinformatics/btac809

Efficient computation of contributional diversity metrics from microbiome data with FuncDiv

Bioinformatics. 2023 Jan 1;39(1):btac809. doi: 10.1093/bioinformatics/btac809.

Authors

Gavin M Douglas^{1

2}, Sunu Kim², Morgan G I Langille³, B Jesse Shapiro^{1

2}

Affiliations

¹ Genome Centre, McGill University, Montréal, QC H3A 0G1, Canada.
² Department of Microbiology & Immunology, McGill University, Montréal, QC H3A 2B4, Canada.
³ Department of Pharmacology, Dalhousie University, Halifax, NS B3H 4R2, Canada.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Metagenomics
Microbiota*
Software

Grants and funding

National Sciences and Engineering Research Council of Canada