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dc.contributor.authorXue, Yaxin
dc.contributor.authorLanzen, Anders
dc.contributor.authorJonassen, Inge
dc.date.accessioned2021-07-02T08:12:37Z-
dc.date.available2021-07-02T08:12:37Z-
dc.date.issued2020
dc.identifierISI:000550117300011
dc.identifier.citationBIOINFORMATICS, 2020, 36, 3365-3371
dc.identifier.issn1367-4803
dc.identifier.urihttp://dspace.azti.es/handle/24689/1116-
dc.description.abstractMotivation: Technological advances in meta-transcriptomics have enabled a deeper understanding of the structure and function of microbial communities. `Total RNA' 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. Results: 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.
dc.language.isoEnglish
dc.publisherOXFORD UNIV PRESS
dc.subjectMICROBIAL DIVERSITY
dc.subjectMETATRANSCRIPTOMICS
dc.subjectCOMMUNITIES
dc.titleReconstructing ribosomal genes from large scale total RNA meta-transcriptomic data
dc.typeArticle
dc.identifier.journalBIOINFORMATICS
dc.format.page3365-3371
dc.format.volume36
dc.identifier.e-issn1460-2059
dc.identifier.doi10.1093/bioinformatics/btaa177
Appears in Publication types:Artículos científicos



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