ArticlePloS one2014
Exon first nucleotide mutations in splicing: evaluation of in silico prediction tools.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Identification of novel germline and somatic mutations associated with hepatocellular carcinoma by next-generation sequencing.Frontiers in pharmacology · 2025Article
- Mutations Causing X-Linked Amelogenesis Imperfecta Alter miRNA Formation from Amelogenin Exon4.Journal of dental research · 2023Article
- Article
- Molecular analysis ofMolecular genetics and metabolism reports · 2020Article
- Splicing Enhancers at Intron-Exon Borders Participate in Acceptor Splice Sites Recognition.International journal of molecular sciences · 2020Article
- Variation benchmark datasets: update, criteria, quality and applications.Database : the journal of biological databases and curation · 2020Article
- Impact of acceptor splice site NAGTAG motif on exon recognition.Molecular biology reports · 2019Article
- Article
- Species-dependent splice recognition of a cryptic exon resulting from a recurrent intronic CEP290 mutation that causes congenital blindness.International journal of molecular sciences · 2015Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutations in the first nucleotide of exons (E(+1)) mostly affect pre-mRNA splicing when found in AG-dependent 3' splice sites, whereas AG-independent splice sites are more resistant. The AG-dependency, however, may be difficult to assess just from primary sequence data as it depends on the quality of the polypyrimidine tract. For this reason, in silico prediction tools are commonly used to score 3' splice sites. In this study, we have assessed the ability of sequence features and in silico prediction tools to discriminate between the splicing-affecting and non-affecting E(+1) variants. For this purpose, we newly tested 16 substitutions in vitro and derived other variants from literature. Surprisingly, we found that in the presence of the substituting nucleotide, the quality of the polypyrimidine tract alone was not conclusive about its splicing fate. Rather, it was the identity of the substituting nucleotide that markedly influenced it. Among the computational tools tested, the best performance was achieved using the Maximum Entropy Model and Position-Specific Scoring Matrix. As a result of this study, we have now established preliminary discriminative cut-off values showing sensitivity up to 95% and specificity up to 90%. This is expected to improve our ability to detect splicing-affecting variants in a clinical genetic setting.
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