{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:39:32Z","timestamp":1785199172281,"version":"3.55.0"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T00:00:00Z","timestamp":1584057600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Technological advances in meta-transcriptomics have enabled a deeper understanding of the structure and function of microbial communities. \u2018Total RNA\u2019 meta-transcriptomics, sequencing of total reverse transcribed RNA, provides a unique opportunity to investigate both the structure and function of active microbial communities from all three domains of life simultaneously. A major step of this approach is the reconstruction of full-length taxonomic marker genes such as the small subunit ribosomal RNA. However, current tools for this purpose are mainly targeted towards analysis of amplicon and metagenomic data and thus lack the ability to handle the massive and complex datasets typically resulting from total RNA experiments.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this work, we introduce MetaRib, a new tool for reconstructing ribosomal gene sequences from total RNA meta-transcriptomic data. MetaRib is based on the popular rRNA assembly program EMIRGE, together with several improvements. We address the challenge posed by large complex datasets by integrating sub-assembly, dereplication and mapping in an iterative approach, with additional post-processing steps. We applied the method to both simulated and real-world datasets. Our results show that MetaRib can deal with larger datasets and recover more rRNA genes, which achieve around 60 times speedup and higher F1 score compared to EMIRGE in simulated datasets. In the real-world dataset, it shows similar trends but recovers more contigs compared with a previous analysis based on random sub-sampling, while enabling the comparison of individual contig abundances across samples for the first time.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of MetaRib is freely available at https:\/\/github.com\/yxxue\/MetaRib.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Contact<\/jats:title>\n                  <jats:p>yaxin.xue@uib.no or Inge.Jonassen@uib.no<\/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\/btaa177","type":"journal-article","created":{"date-parts":[[2020,3,11]],"date-time":"2020-03-11T12:40:01Z","timestamp":1583930401000},"page":"3365-3371","source":"Crossref","is-referenced-by-count":23,"title":["Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9516-286X","authenticated-orcid":false,"given":"Yaxin","family":"Xue","sequence":"first","affiliation":[{"name":"Computational Biology Unit , Department of Informatics, University of Bergen, Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anders","family":"Lanz\u00e9n","sequence":"additional","affiliation":[{"name":"AZTI-Tecnalia , Herrera Kaia, 20110 Pasaia, Spain"},{"name":"Ikerbasque , Basque Foundation for Science, 48011 Bilbao, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inge","family":"Jonassen","sequence":"additional","affiliation":[{"name":"Computational Biology Unit , Department of Informatics, University of Bergen, Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,3,13]]},"reference":[{"key":"2023062300081179500_btaa177-B1","author":"Bang-Andreasen","year":"2020"},{"key":"2023062300081179500_btaa177-B2","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1038\/ismej.2013.102","article-title":"Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses","volume":"7","author":"Blazewicz","year":"2013","journal-title":"ISME J"},{"key":"2023062300081179500_btaa177-B3","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/nmeth.3869","article-title":"DADA2: high-resolution sample inference from Illumina amplicon data","volume":"13","author":"Callahan","year":"2016","journal-title":"Nat. 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