Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/138724
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Type: | Journal article |
Title: | Introme accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications |
Author: | Sullivan, P.J. Gayevskiy, V. Davis, R.L. Wong, M. Mayoh, C. Mallawaarachchi, A. Hort, Y. McCabe, M.J. Beecroft, S. Jackson, M.R. Arts, P. Dubowsky, A. Laing, N. Dinger, M.E. Scott, H.S. Oates, E. Pinese, M. Cowley, M.J. |
Citation: | Genome Biology, 2023; 24(1):1-18 |
Publisher: | Springer Nature |
Issue Date: | 2023 |
ISSN: | 1474-7596 1474-760X |
Statement of Responsibility: | Patricia 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 |
Abstract: | Predicting 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 . |
Keywords: | Splicing Variant interpretation Deep intronic Splice region Splice site Intronic variant Splicing regulatory element Genomics Clinical genetics |
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. |
DOI: | 10.1186/s13059-023-02936-7 |
Grant ID: | http://purl.org/au-research/grants/nhmrc/1176265 |
Published version: | http://dx.doi.org/10.1186/s13059-023-02936-7 |
Appears in Collections: | Genetics publications |
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File | Description | Size | Format | |
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hdl_138724.pdf | Published version | 3.64 MB | Adobe PDF | View/Open |
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