Evidence map›Paper›PMID 35188075›Full record

ArticleRNA biology2022

Background splicing as a predictor of aberrant splicing in genetic disease.

Diana Alexieva, Yi Long, Rupa Sarkar, Hansraj Dhayan, Emmanuel Bruet, Robert Maurice Winston, Igor Vorechovsky, Leoandro Castellano, Nicholas John Dibb

Open access · goldAbstract read
In one paragraph

Article in RNA biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.3field-weighted citation impact, top 50% 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

5 citing papers in PubMed, 3 citations in OpenAlex.

  1. A class of deep intronicmedRxiv : the preprint server for health sciences · 2026
    Article
  2. Article
  3. Review
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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

9 authors at 3 institutions in 1 country.

Diana AlexievaDepartment of Metabolism, Digestion and Reproduction, Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.
Yi LongDepartment of Metabolism, Digestion and Reproduction, Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.
Rupa SarkarDepartment of Metabolism, Digestion and Reproduction, Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.
Hansraj DhayanDepartment of Metabolism, Digestion and Reproduction, Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.
Emmanuel BruetDepartment of Metabolism, Digestion and Reproduction, Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.
Robert Maurice WinstonDepartment of Metabolism, Digestion and Reproduction, Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.
Igor VorechovskyFaculty of Medicine, University of Southampton, Southampton, UK.ORCID 0000-0002-6740-6502
Leoandro CastellanoDepartment of Surgery and Cancer, Imperial College London, Imperial Centre for Translational and Experimental Medicine (Ictem), London, UK.ORCID 0000-0002-3059-4829
Nicholas John DibbDepartment of Metabolism, Digestion and Reproduction, Institute of Reproductive and Developmental Biology, Imperial College London, London, UK.
Imperial College London · GBUniversity of Southampton · GBUniversity of Sussex · GB

Funding

Biotechnology and Biological Sciences Research Council BB/F017359/1
6 · The paper itself

Abstract

Mutations of splice sites, auxiliary splicing elements and the splicing machinery cause a wide range of genetic disease. Here we report that many of the complex effects of splicing mutations can be predicted from background splicing information, with emphasis on BRCA1, BRCA2 and DMD. Background splicing arises from very low level splicing between rarely used background splice sites and from low-level exon skipping between intron splice sites. We show how this information can be downloaded from the Snaptron database of spliced RNA, which we then compared with databases of human splice site mutations. We report that inactivating mutations of intron splice sites typically caused the non-mutated partner splice site to splice to a known background splice site in over 90% of cases and to the strongest background splice site in the large majority of cases. Consequently, background splicing information can usefully predict the effects of splice site mutations, which include cryptic splice activation and single or multiple exon skipping. In addition, de novo splice sites and splice sites involved in pseudoexon formation, recursive splicing and aberrant splicing in cancer show a 90% match to background splice sites, so establishing that the enhancement of background splicing causes a wide range of splicing aberrations. We also discuss how background splicing information can identify cryptic splice sites that might be usefully targeted by antisense oligonucleotides (ASOs) and how it might indicate possible multiple exon skipping side effects of ASOs designed to induce single exon skipping.

Indexed as

Alternative SplicingGene Expression RegulationGenetic Predisposition to DiseaseComputational BiologyDatabases, GeneticExonsGene Expression ProfilingGenetic Diseases, InbornHumansIntronsMutationRNA Splice SitesRNA SplicingRNA Splice Sitesbackground splicingcancerCryptic splice siteexon skippingpseudoexonsrecursive splicing

Identifiers

PMID35188075
PMCPMC8865296
OpenAlexW4213165146

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.