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

Humans RNA Splice Sites Introns RNA Splicing Machine Learning Mutation
Clinical genetics Deep intronic Genomics Intronic variant Splice region Splice site Splicing Splicing regulatory element Variant interpretation

Journal

Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660

Informations de publication

Date de publication:
17 05 2023
Historique:
received: 30 03 2022
accepted: 10 04 2023
medline: 19 5 2023
pubmed: 18 5 2023
entrez: 17 5 2023
Statut: epublish

Résumé

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 .

Identifiants

DOI: 10.1186/s13059-023-02936-7 PMID: 37198692 PMC: PMC10190034
pubmed: 37198692
doi: 10.1186/s13059-023-02936-7
pii: 10.1186/s13059-023-02936-7
pmc: PMC10190034
doi:

Substances chimiques

RNA Splice Sites 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

118

Informations de copyright

© 2023. The Author(s).

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Auteurs

Patricia J Sullivan (PJ)

Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia.
University of New South Wales Centre for Childhood Cancer Research, UNSW Sydney, Sydney, NSW, Australia.

Velimir Gayevskiy (V)

Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.

Ryan L Davis (RL)

Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.
Department of Neurogenetics, Kolling Institute, St. Leonards, NSW, Australia.
Sydney Medical School-Northern, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.

Marie Wong (M)

Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia.

Chelsea Mayoh (C)

Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia.

Amali Mallawaarachchi (A)

Division of Genomics and Epigenetics, Garvan Institute of Medical Research, Sydney, Australia.
Clinical Genetics Unit, Institute of Precision Medicine and Bioinformatics, Sydney Local Health District, Sydney, Australia.

Yvonne Hort (Y)

Division of Genomics and Epigenetics, Garvan Institute of Medical Research, Sydney, Australia.

Mark J McCabe (MJ)

Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.

Sarah Beecroft (S)

Centre for Medical Research, University of Western Australia, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, WA, Australia.

Matilda R Jackson (MR)

Department of Genetics and Molecular Pathology, Centre for Cancer Biology, An Alliance Between SA Pathology and the University of South Australia, Adelaide, Australia.
Australian Genomics, Parkville, VIC, Australia.

Peer Arts (P)

Department of Genetics and Molecular Pathology, Centre for Cancer Biology, An Alliance Between SA Pathology and the University of South Australia, Adelaide, Australia.

Andrew Dubowsky (A)

Department of Genetics and Molecular Pathology, SA Pathology, Adelaide, Australia.

Nigel Laing (N)

Centre for Medical Research, University of Western Australia, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, WA, Australia.

Marcel E Dinger (ME)

Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.
School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia.

Hamish S Scott (HS)

Department of Genetics and Molecular Pathology, Centre for Cancer Biology, An Alliance Between SA Pathology and the University of South Australia, Adelaide, Australia.
Australian Genomics, Parkville, VIC, Australia.
School of Medicine, University of Adelaide, Adelaide, SA, Australia.
ACRF Cancer Genomics Facility, Centre for Cancer Biology, An Alliance Between SA Pathology and the University of South Australia, Adelaide, SA, Australia.

Emily Oates (E)

School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia.

Mark Pinese (M)

Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia.

Mark J Cowley (MJ)

Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia. MCowley@ccia.org.au.
School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia. MCowley@ccia.org.au.
ORCID: 0000-0002-9519-5714

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Introme accurately predicts the impact of...
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Génome › Composants de génome › Gènes › Composants de gène › Sites d'épissage d'ARN › Introme accurately predicts the impact of...
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Génome › Composants de génome › Gènes › Composants de gène › Introns › Introme accurately predicts the impact of...
questionsmedicales.fr › Phénomènes génétiques › Régulation de l'expression des gènes › Maturation post-transcriptionnelle des ARN › Épissage des ARN › Introme accurately predicts the impact of...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Introme accurately predicts the impact of...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Mutation › Introme accurately predicts the impact of...