Evidence map›Paper›PMID 37198692›Full record

ArticleGenome biology2023

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

Patricia J Sullivan, Velimir Gayevskiy, Ryan L Davis, Marie Wong, Chelsea Mayoh, Amali Mallawaarachchi, Yvonne Hort, Mark J McCabe, Sarah Beecroft, Matilda R Jackson and 8 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
5.0field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors at 6 institutions in 1 country.

Patricia J SullivanChildren's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
Velimir GayevskiyKinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.
Ryan L DavisKinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.
Marie WongChildren's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
Chelsea MayohChildren's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
Amali MallawaarachchiDivision of Genomics and Epigenetics, Garvan Institute of Medical Research, Sydney, Australia.
Yvonne HortDivision of Genomics and Epigenetics, Garvan Institute of Medical Research, Sydney, Australia.
Mark J McCabeKinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.
Sarah BeecroftCentre for Medical Research, University of Western Australia, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, WA, Australia.
Matilda R JacksonDepartment of Genetics and Molecular Pathology, Centre for Cancer Biology, An Alliance Between SA Pathology and the University of South Australia, Adelaide, Australia.
Peer ArtsDepartment of Genetics and Molecular Pathology, Centre for Cancer Biology, An Alliance Between SA Pathology and the University of South Australia, Adelaide, Australia.
Andrew DubowskyDepartment of Genetics and Molecular Pathology, SA Pathology, Adelaide, Australia.
Nigel LaingCentre for Medical Research, University of Western Australia, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, WA, Australia.
Marcel E DingerKinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia.
Hamish S ScottDepartment of Genetics and Molecular Pathology, Centre for Cancer Biology, An Alliance Between SA Pathology and the University of South Australia, Adelaide, Australia.
Emily OatesSchool of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia.
Mark PineseChildren's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
Mark J CowleyChildren's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia. MCowley@ccia.org.au.ORCID 0000-0002-9519-5714
Cancer Institute of New South Wales · AUGarvan Institute of Medical Research · AUSouth Australia Pathology · AUThe University of Western Australia · AUThe University of Sydney · AUUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 .

Indexed as

RNA Splice SitesRNA SplicingHumansIntronsMachine LearningMutationRNA Splice SitesClinical geneticsDeep intronicGenomicsIntronic variantSplice regionSplice siteSplicingSplicing regulatory elementVariant interpretation

Identifiers

PMID37198692
PMCPMC10190034
OpenAlexW4377011933

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.