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dc.contributor.authorNoviello, Teresa Maria Rosaria
dc.contributor.authorCeccarelli, Francesco
dc.contributor.authorCeccarelli, Michele
dc.contributor.authorCerulo, Luigi
dc.date.accessioned2020-11-23T23:18:33Z
dc.date.available2020-11-23T23:18:33Z
dc.date.issued2020-11-11
dc.date.submitted2020-05-28
dc.identifier.issn1553-734X
dc.identifier.otherpcompbiol-d-20-00903
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/313225
dc.description.abstractSmall non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information. Here we show that RNA function can be predicted with good accuracy from a lightweight representation of sequence information without the necessity of computing secondary structure features which is computationally expensive. This finding appears to go against the dogma of secondary structure being a key determinant of function in RNA. Compared to recent secondary structure based methods, the proposed solution is more robust to sequence boundary noise and reduces drastically the computational cost allowing for large data volume annotations. Scripts and datasets to reproduce the results of experiments proposed in this study are available at: https://github.com/bioinformatics-sannio/ncrna-deep.
dc.languageen
dc.publisherPublic Library of Science
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectResearch Article
dc.subjectBiology and life sciences
dc.subjectComputer and information sciences
dc.subjectResearch and analysis methods
dc.titleDeep learning predicts short non-coding RNA functions from only raw sequence data
dc.typeArticle
dc.date.updated2020-11-23T23:18:33Z
prism.issueIdentifier11
prism.publicationNamePLOS Computational Biology
prism.volume16
dc.identifier.doi10.17863/CAM.60331
dcterms.dateAccepted2020-09-28
rioxxterms.versionofrecord10.1371/journal.pcbi.1008415
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Ioshikhes, Ilya
dc.contributor.orcidNoviello, Teresa Maria Rosaria [0000-0002-3411-6752]
dc.contributor.orcidCeccarelli, Francesco [0000-0002-5995-5077]
dc.contributor.orcidCeccarelli, Michele [0000-0002-4702-6617]
dc.contributor.orcidCerulo, Luigi [0000-0001-8342-3487]
dc.identifier.eissn1553-7358
pubs.funder-project-idAssociazione Italiana per la Ricerca sul Cancro (IT) (21846)
pubs.funder-project-idMinistero dell’Istruzione, dell’Università e della Ricerca (2017XJ38A4-004)
pubs.funder-project-idRegione Campania (GENOMAeSALUTE)


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)