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dc.contributor.authorSingh, Jaswinder
dc.contributor.authorHanson, Jack
dc.contributor.authorPaliwal, Kuldip
dc.contributor.authorZhou, Yaoqi
dc.date.accessioned2020-04-02T00:25:57Z
dc.date.available2020-04-02T00:25:57Z
dc.date.issued2019
dc.identifier.issn2041-1723
dc.identifier.doi10.1038/s41467-019-13395-9
dc.identifier.urihttp://hdl.handle.net/10072/392903
dc.description.abstractThe majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only <250 nonredundant, high-resolution RNA structures are available for model training, we utilize transfer learning from a model initially trained with a recent high-quality bpRNA dataset of >10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherNature Publishing Group
dc.relation.ispartofissue1
dc.relation.ispartofjournalNature Communications
dc.relation.ispartofvolume10
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.subject.keywordsScience & Technology
dc.subject.keywordsMultidisciplinary Sciences
dc.subject.keywordsScience & Technology - Other Topics
dc.subject.keywordsTHERMODYNAMICS
dc.subject.keywordsIMPLEMENTATION
dc.titleRNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSingh, J; Hanson, J; Paliwal, K; Zhou, Y, RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning, Nature Communications, 2019, 10 (1)
dcterms.dateAccepted2019-11-01
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-04-02T00:23:08Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2019 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
gro.hasfulltextFull Text
gro.griffith.authorPaliwal, Kuldip K.
gro.griffith.authorZhou, Yaoqi
gro.griffith.authorSingh, Jaswinder
gro.griffith.authorHanson, Jack S.


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