{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:17:18Z","timestamp":1774513038737,"version":"3.50.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2020,4,27]],"date-time":"2020-04-27T00:00:00Z","timestamp":1587945600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012913","name":"Tata Consultancy Services","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012913","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group \u2018meta-data\u2019 and \u2018inter-omic\u2019 functional profiles that are often associated with microbiome studies. This not only necessitates the need for categorical networks, but also integrated and bi-partite networks. Multiple options of network inference algorithms further add to the efforts required for performing correlation-based microbiome interaction studies. We present MetagenoNets, a web-based application, which accepts multi-environment microbial abundance as well as functional profiles, intelligently segregates \u2018continuous and categorical\u2019 meta-data and allows inference as well as visualization of categorical, integrated (inter-omic) and bi-partite networks. Modular structure of MetagenoNets ensures logical flow of analysis (inference, integration, exploration and comparison) in an intuitive and interactive personalized dashboard driven framework. Dynamic choice of filtration, normalization, data transformation and correlation algorithms ensures, that end-users get a one-stop solution for microbial network analysis. MetagenoNets is freely available at https:\/\/web.rniapps.net\/metagenonets.<\/jats:p>","DOI":"10.1093\/nar\/gkaa254","type":"journal-article","created":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T19:13:51Z","timestamp":1585854831000},"page":"W572-W579","source":"Crossref","is-referenced-by-count":48,"title":["MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks"],"prefix":"10.1093","volume":"48","author":[{"given":"Sunil","family":"Nagpal","sequence":"first","affiliation":[{"name":"Bio-Sciences R&D Division, TCS Research, Pune, Maharashtra\u00a0411013, India"}]},{"given":"Rashmi","family":"Singh","sequence":"first","affiliation":[{"name":"Bio-Sciences R&D Division, TCS Research, Pune, Maharashtra\u00a0411013, India"}]},{"given":"Deepak","family":"Yadav","sequence":"first","affiliation":[{"name":"Bio-Sciences R&D Division, TCS Research, Pune, Maharashtra\u00a0411013, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2614-1982","authenticated-orcid":false,"given":"Sharmila S","family":"Mande","sequence":"first","affiliation":[{"name":"Bio-Sciences R&D Division, TCS Research, Pune, Maharashtra\u00a0411013, India"}]}],"member":"286","published-online":{"date-parts":[[2020,4,27]]},"reference":[{"key":"2020062614040405300_B1","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1038\/s41559-017-0340-2","article-title":"The microbiome beyond the horizon of ecological and evolutionary theory","volume":"1","author":"Koskella","year":"2017","journal-title":"Nat. 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