{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:26:56Z","timestamp":1766068016211,"version":"3.41.2"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T00:00:00Z","timestamp":1721174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>16S rRNA gene sequencing is the most frequent approach for the characterization of the human gut microbiota. Despite different efforts in the literature, the inference of functional and metabolic interpretations from 16S rRNA gene sequencing data is still a challenging task. High-quality metabolic reconstructions of the human gut microbiota, such as AGORA and AGREDA, constitute a curated resource to improve functional inference from 16S rRNA data, but they are not typically integrated into standard bioinformatics tools.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we present q2-metnet, a QIIME2 plugin that enables the contextualization of 16S rRNA gene sequencing data into AGORA and AGREDA. In particular, based on relative abundances of taxa, q2-metnet determines normalized activity scores for the reactions and subsystems involved in the selected metabolic reconstruction. Using these scores, q2-metnet allows the user to conduct differential activity analysis for reactions and subsystems, as well as exploratory analysis using PCA and hierarchical clustering. We apply q2-metnet to a dataset from our group that involves 16S rRNA data from stool samples from lean, allergic to cow\u2019s milk, obese and celiac children, and the Belgian Flemish Gut Flora Project cohort, which includes faecal 16S rRNA data from obese and normal-weight adult individuals. In the first case, q2-metnet outperforms existing algorithms in separating different clinical conditions based on predicted pathway abundances and subsystem scores. In the second case, q2-metnet complements competing approaches in predicting functional alterations in the gut microbiota of obese individuals. Overall, q2-metnet constitutes a powerful bioinformatics tool to provide metabolic context to 16S rRNA data from the human gut microbiota.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Python code of q2-metnet is available in https:\/\/github.com\/PlanesLab\/q2-metnet and https:\/\/figshare.com\/articles\/dataset\/q2-metnet_package\/26180446.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae455","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T16:50:31Z","timestamp":1721148631000},"source":"Crossref","is-referenced-by-count":6,"title":["q2-metnet: QIIME2 package to analyse 16S rRNA data via high-quality metabolic reconstructions of the human gut microbiota"],"prefix":"10.1093","volume":"40","author":[{"given":"Francesco","family":"Balzerani","sequence":"first","affiliation":[{"name":"Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra , San Sebastian 20018,","place":["Spain"]}]},{"given":"Telmo","family":"Blasco","sequence":"additional","affiliation":[{"name":"Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra , San Sebastian 20018,","place":["Spain"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6398-7496","authenticated-orcid":false,"given":"Sergio","family":"P\u00e9rez-Burillo","sequence":"additional","affiliation":[{"name":"Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra , San Sebastian 20018,","place":["Spain"]}]},{"given":"M Pilar","family":"Francino","sequence":"additional","affiliation":[{"name":"Area de Gen\u00f3mica y Salud, Fundaci\u00f3n para el Fomento de la Investigaci\u00f3n Sanitaria y Biom\u00e9dica de la Comunitat Valenciana-Salud P\u00fablica , Valencia, 46020,","place":["Spain"]},{"name":"CIBER en Epidemiolog\u00eda y Salud P\u00fablica , Madrid,","place":["Spain"]}]},{"given":"Jos\u00e9 \u00c1","family":"Rufi\u00e1n-Henares","sequence":"additional","affiliation":[{"name":"Departamento de Nutrici\u00f3n y Bromatolog\u00eda, Instituto de Nutrici\u00f3n y Tecnolog\u00eda de los Alimentos, Centro de Investigaci\u00f3n Biom\u00e9dica, Universidad de Granada , Granada, 18071,","place":["Spain"]},{"name":"Instituto de Investigaci\u00f3n Biosanitaria ibs.GRANADA, Universidad de Granada , Granada, 18071,","place":["Spain"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3769-5419","authenticated-orcid":false,"given":"Luis V","family":"Valcarcel","sequence":"additional","affiliation":[{"name":"Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra , San Sebastian 20018,","place":["Spain"]},{"name":"Biomedical Engineering Center, University of Navarra , Pamplona 31009,","place":["Spain"]},{"name":"Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra , Pamplona 31009,","place":["Spain"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1155-3105","authenticated-orcid":false,"given":"Francisco J","family":"Planes","sequence":"additional","affiliation":[{"name":"Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra , San Sebastian 20018,","place":["Spain"]},{"name":"Biomedical Engineering Center, University of Navarra , Pamplona 31009,","place":["Spain"]},{"name":"Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra , Pamplona 31009,","place":["Spain"]}]}],"member":"286","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"key":"2024111406110785300_btae455-B1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool","volume":"215","author":"Altschul","year":"1990","journal-title":"J Mol 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Appl"},{"key":"2024111406110785300_btae455-B5","doi-asserted-by":"crossref","first-page":"4728","DOI":"10.1038\/s41467-021-25056-x","article-title":"An extended reconstruction of human gut microbiota metabolism of dietary compounds","volume":"12","author":"Blasco","year":"2021","journal-title":"Nat Commun"},{"key":"2024111406110785300_btae455-B6","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/s40168-018-0470-z","article-title":"Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2\u2019s q2-feature-classifier plugin","volume":"6","author":"Bokulich","year":"2018","journal-title":"Microbiome"},{"key":"2024111406110785300_btae455-B7","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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