Human Splicing Finder: an online bioinformatics tool to predict splicing signals

By François-Olivier Desmet, D. Hamroun, M. Lalande, G. Collod-Béroud, M. Claustres, C. Béroud. Nucleic Acids Research, 2009

2,504 citations recorded by the source corpus.

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-β Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5′ and 3′ splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c67f7d629c4500bd47056b03bdfaf2df2f3c9346

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Citations in the corpus

TP53 Variations in Human Cancers: New Lessons from the IARC TP53 Database and Genomics Data (2016, methodology): “…activities (such as promoter-specific transactivation, dominant-negative effect, gain of function, splicing, and so on) as well as predictions based on tools such as SIFT [Kumar et al., 2009], AGVGD [Mathe et al., 2006], Polyphen [Adzhubei et al., 2010], or HumanSpliceFinder [Desmet et al., 2009].”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, neutral): “TheHumanSplicingFinder (HSF)combinesresultsfromseveralsplicesiteandspliceregulatory sitepredictiontoolstoestimatetheimpactofvariationsonsplic-ingmotifs[Desmetetal.,2009].”
A Guide for Functional Analysis of BRCA1 Variants of Uncertain Significance (VUS) (2012, background): “be/HSF/ [Desmet et al., 2009] Splice Site Prediction by Neural Network Splice site finder http://www.”
In silico prediction of splice-altering single nucleotide variants in the human genome (2014, methodology): “To compare the predictive performance of PWM, MES, NNSplice, GeneSplicer, HSF, NetGene2, GENSCAN and SplicePredictor, we first examined the ability of these tools to identify wild-type splice sites for all variants in our dataset and treated them as a missing score if the wild-type score of a varian”