Introme accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications

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dc.contributor.authorSullivan, P.J.
dc.contributor.authorGayevskiy, V.
dc.contributor.authorDavis, R.L.
dc.contributor.authorWong, M.
dc.contributor.authorMayoh, C.
dc.contributor.authorMallawaarachchi, A.
dc.contributor.authorHort, Y.
dc.contributor.authorMcCabe, M.J.
dc.contributor.authorBeecroft, S.
dc.contributor.authorJackson, M.R.
dc.contributor.authorArts, P.
dc.contributor.authorDubowsky, A.
dc.contributor.authorLaing, N.
dc.contributor.authorDinger, M.E.
dc.contributor.authorScott, H.S.
dc.contributor.authorOates, E.
dc.contributor.authorPinese, M.
dc.contributor.authorCowley, M.J.
dc.date.issued2023
dc.description.abstractPredicting the impact of coding and noncoding variants on splicing is challenging, particularly in non-canonical splice sites, leading to missed diagnoses in patients. Existing splice prediction tools are complementary but knowing which to use for each splicing context remains difficult. Here, we describe Introme, which uses machine learning to integrate predictions from several splice detection tools, additional splicing rules, and gene architecture features to comprehensively evaluate the likelihood of a variant impacting splicing. Through extensive benchmarking across 21,000 splice-altering variants, Introme outperformed all tools (auPRC: 0.98) for the detection of clinically significant splice variants. Introme is available at https://github.com/CCICB/introme .
dc.description.statementofresponsibilityPatricia J. Sullivan, Velimir Gayevskiy, Ryan L. Davis, Marie Wong, Chelsea Mayoh, Amali Mallawaarachchi, Yvonne Hort, Mark J. McCabe, Sarah Beecroft, Matilda R. Jackson, Peer Arts, Andrew Dubowsky, Nigel Laing, Marcel E. Dinger, Hamish S. Scott, Emily Oates, Mark Pinese, and Mark J. Cowley
dc.identifier.citationGenome Biology, 2023; 24(1):1-18
dc.identifier.doi10.1186/s13059-023-02936-7
dc.identifier.issn1474-7596
dc.identifier.issn1474-760X
dc.identifier.orcidJackson, M.R. [0000-0001-7547-7653]
dc.identifier.orcidArts, P. [0000-0002-6742-6239]
dc.identifier.orcidScott, H.S. [0000-0002-5813-631X]
dc.identifier.urihttps://hdl.handle.net/2440/138724
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1176265
dc.rights© The Author(s) 2023. Open Access 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
dc.source.urihttps://doi.org/10.1186/s13059-023-02936-7
dc.subjectSplicing
dc.subjectVariant interpretation
dc.subjectDeep intronic
dc.subjectSplice region
dc.subjectSplice site
dc.subjectIntronic variant
dc.subjectSplicing regulatory element
dc.subjectGenomics
dc.subjectClinical genetics
dc.subject.meshHumans
dc.subject.meshRNA Splice Sites
dc.subject.meshIntrons
dc.subject.meshRNA Splicing
dc.subject.meshMachine Learning
dc.subject.meshMutation
dc.titleIntrome accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications
dc.typeJournal article
pubs.publication-statusPublished

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