{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:58:33Z","timestamp":1776279513398,"version":"3.50.1"},"reference-count":127,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T00:00:00Z","timestamp":1606867200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["LSHBCT-2006-018996"],"award-info":[{"award-number":["LSHBCT-2006-018996"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["ERC-2009-AdG_20090506_250268"],"award-info":[{"award-number":["ERC-2009-AdG_20090506_250268"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi\u2019s wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children\u2019s rooms between samples from two study centers (Ulm and Munich).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https:\/\/github.com\/stefpeschel\/NetCoMi.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Contact<\/jats:title>\n                    <jats:p>Tel:+49 89 3187 43258; stefanie.peschel@mail.de<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Briefings in Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bib\/bbaa290","type":"journal-article","created":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T07:24:15Z","timestamp":1602055455000},"source":"Crossref","is-referenced-by-count":500,"title":["NetCoMi: network construction and comparison for microbiome data in R"],"prefix":"10.1093","volume":"22","author":[{"given":"Stefanie","family":"Peschel","sequence":"first","affiliation":[{"name":"Institute for Asthma and Allergy Prevention, Helmholtz Zentrum M\u00fcnchen, German Research Center for Environmental Health, Munich, Germany"}]},{"given":"Christian L","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Department of Statistics, LMU M\u00fcnchen, Munich, Germany"},{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen, German Research Center for Environmental Health, Munich, Germany"},{"name":"Center for Computational Mathematics, Flatiron Institute, New York, USA"}]},{"given":"Erika","family":"von Mutius","sequence":"additional","affiliation":[{"name":"Institute for Asthma and Allergy Prevention, Helmholtz Zentrum M\u00fcnchen, German Research Center for Environmental Health, Munich, Germany"},{"name":"Dr von Hauner Children\u2019s Hospital, LMU M\u00fcnchen, Munich, Germany"},{"name":"Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research, Munich, Germany"}]},{"given":"Anne-Laure","family":"Boulesteix","sequence":"additional","affiliation":[{"name":"Institute for Medical Information Processing, Biometry and Epidemiology, LMU M\u00fcnchen, Munich, Germany"}]},{"given":"Martin","family":"Depner","sequence":"additional","affiliation":[{"name":"Institute for Asthma and Allergy Prevention, Helmholtz Zentrum M\u00fcnchen, German Research Center for Environmental Health, Munich, Germany"}]}],"member":"286","published-online":{"date-parts":[[2020,12,3]]},"reference":[{"key":"2021072112294531000_ref1","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1128\/JCM.01228-07","article-title":"16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls","volume":"45","author":"Janda","year":"2007","journal-title":"J Clin 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