title: In silico prediction of splice-affecting nucleotide variants.
abstract: It appears that all types of genomic nucleotide variations can be deleterious by affecting normal pre-mRNA splicing via disruption/creation of splice site consensus sequences. As it is neither pertinent nor realistic to perform functional testing for all of these variants, it is important to identify those that could lead to a splice defect in order to restrict experimental transcript analyses to the most appropriate cases. In silico tools designed to provide this type of prediction are available. In this chapter, we present in silico splice tools integrated in the Alamut (Interactive Biosoftware) application and detail their use in routine diagnostic applications. At this time, in silico predictions are useful for variants that decrease the strength of wild-type splice sites or create a cryptic splice site. Importantly, in silico predictions are not sufficient to classify variants as neutral or deleterious: they should be used as part of the decision-making process to detect potential candidates for splicing anomalies, prompting molecular geneticists to carry out transcript analyses in a limited and pertinent number of cases which could be managed in routine settings.
authors.affiliation: Faculty of Pharmacy, Institut Curie, Paris Descartes University, Paris, France. claude.houdayer@curie.net
authors.forename: Claude
authors.initials: C
authors.lastname: Houdayer
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D012322:RNA Precursors / Q000235:genetics; D012326:RNA Splicing / Q000235:genetics*; D012984:Software
chemical_list: D012322:RNA Precursors