Introme accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications.
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ABSTRACT:
SUBMITTER: Sullivan PJ
PROVIDER: S-EPMC10190034 | biostudies-literature | 2023 May
REPOSITORIES: biostudies-literature
Publications
Introme accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications.
Sullivan Patricia J PJ Gayevskiy Velimir V Davis Ryan L RL Wong Marie M Mayoh Chelsea C Mallawaarachchi Amali A Hort Yvonne Y McCabe Mark J MJ Beecroft Sarah S Jackson Matilda R MR Arts Peer P Dubowsky Andrew A Laing Nigel N Dinger Marcel E ME Scott Hamish S HS Oates Emily E Pinese Mark M Cowley Mark J MJ
Genome biology 20230517 1
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 like ...[more]
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