{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T23:07:49Z","timestamp":1781478469797,"version":"3.54.1"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"National Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>FuncDiv is distributed under a GNU Affero General Public License v3.0 and is available at https:\/\/github.com\/gavinmdouglas\/FuncDiv.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac809","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:39Z","timestamp":1671062439000},"source":"Crossref","is-referenced-by-count":6,"title":["Efficient computation of contributional diversity metrics from microbiome data with <i>FuncDiv<\/i>"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5164-6707","authenticated-orcid":false,"given":"Gavin M","family":"Douglas","sequence":"first","affiliation":[{"name":"Genome Centre, McGill University , Montr\u00e9al, QC H3A 0G1, Canada"},{"name":"Department of Microbiology & Immunology, McGill University , Montr\u00e9al, QC H3A 2B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunu","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Microbiology & Immunology, McGill University , Montr\u00e9al, QC H3A 2B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morgan G I","family":"Langille","sequence":"additional","affiliation":[{"name":"Department of Pharmacology, Dalhousie University , Halifax, NS B3H 4R2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6819-8699","authenticated-orcid":false,"given":"B Jesse","family":"Shapiro","sequence":"additional","affiliation":[{"name":"Genome Centre, McGill University , Montr\u00e9al, QC H3A 0G1, Canada"},{"name":"Department of Microbiology & Immunology, McGill University , Montr\u00e9al, QC H3A 2B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"2023051921404895700_btac809-B1","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1038\/s41586-019-0965-1","article-title":"A new genomic blueprint of the human gut microbiota","volume":"568","author":"Almeida","year":"2019","journal-title":"Nature"},{"key":"2023051921404895700_btac809-B2","doi-asserted-by":"crossref","first-page":"e65088","DOI":"10.7554\/eLife.65088","article-title":"Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with biobakery 3","volume":"10","author":"Beghini","year":"2021","journal-title":"eLife"},{"key":"2023051921404895700_btac809-B3","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1038\/s41587-019-0209-9","article-title":"Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2","volume":"37","author":"Bolyen","year":"2019","journal-title":"Nat. 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