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dc.contributor.authorUmu, Sinan Ugur
dc.contributor.authorPaynter, Vanessa Molin
dc.contributor.authorTrondsen, Håvard
dc.contributor.authorBuschmann, Tilo
dc.contributor.authorRounge, Trine Ballestad
dc.contributor.authorPeterson, Kevin J.
dc.contributor.authorFromm, Bastian
dc.description.abstractThe annotation of microRNAs depends on the availability of transcriptomics data and expert knowledge. This has led to a gap between the availability of novel genomes and high-quality microRNA complements. Using >16,000 microRNAs from the manually curated microRNA gene database MirGeneDB, we generated trained covariance models for all conserved microRNA families. These models are available in our tool MirMachine, which annotates conserved microRNAs within genomes. We successfully applied MirMachine to a range of animal species, including those with large genomes and genome duplications and extinct species, where small RNA sequencing is hard to achieve. We further describe a microRNA score of expected microRNAs that can be used to assess the completeness of genome assemblies. MirMachine closes a long-persisting gap in the microRNA field by facilitating automated genome annotation pipelines and deeper studies into the evolution of genome regulation, even in extinct organisms.en_US
dc.identifier.citationUmu, Paynter, Trondsen, Buschmann, Rounge, Peterson, Fromm. Accurate microRNA annotation of animal genomes using trained covariance models of curated microRNA complements in MirMachine. Cell Genomics. 2023;3(8)en_US
dc.identifier.cristinIDFRIDAID 2181881
dc.relation.journalCell Genomics
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)en_US
dc.titleAccurate microRNA annotation of animal genomes using trained covariance models of curated microRNA complements in MirMachineen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US

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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)