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Introme accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications.
Sullivan, Patricia J; Gayevskiy, Velimir; Davis, Ryan L; Wong, Marie; Mayoh, Chelsea; Mallawaarachchi, Amali; Hort, Yvonne; McCabe, Mark J; Beecroft, Sarah; Jackson, Matilda R; Arts, Peer; Dubowsky, Andrew; Laing, Nigel; Dinger, Marcel E; Scott, Hamish S; Oates, Emily; Pinese, Mark; Cowley, Mark J.
Affiliation
  • Sullivan PJ; Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
  • Gayevskiy V; School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia.
  • Davis RL; University of New South Wales Centre for Childhood Cancer Research, UNSW Sydney, Sydney, NSW, Australia.
  • Wong M; Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.
  • Mayoh C; Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.
  • Mallawaarachchi A; Department of Neurogenetics, Kolling Institute, St. Leonards, NSW, Australia.
  • Hort Y; Sydney Medical School-Northern, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.
  • McCabe MJ; Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
  • Beecroft S; School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia.
  • Jackson MR; Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
  • Arts P; School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia.
  • Dubowsky A; Division of Genomics and Epigenetics, Garvan Institute of Medical Research, Sydney, Australia.
  • Laing N; Clinical Genetics Unit, Institute of Precision Medicine and Bioinformatics, Sydney Local Health District, Sydney, Australia.
  • Dinger ME; Division of Genomics and Epigenetics, Garvan Institute of Medical Research, Sydney, Australia.
  • Scott HS; Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.
  • Oates E; Centre for Medical Research, University of Western Australia, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, WA, Australia.
  • Pinese M; Department of Genetics and Molecular Pathology, Centre for Cancer Biology, An Alliance Between SA Pathology and the University of South Australia, Adelaide, Australia.
  • Cowley MJ; Australian Genomics, Parkville, VIC, Australia.
Genome Biol ; 24(1): 118, 2023 05 17.
Article in En | MEDLINE | ID: mdl-37198692
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 .
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: RNA Splicing / RNA Splice Sites Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Genome Biol Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2023 Document type: Article Affiliation country: Australia
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: RNA Splicing / RNA Splice Sites Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Genome Biol Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2023 Document type: Article Affiliation country: Australia