Precise spatiotemporal regulation of splicing is mediated by splicing cis-elements on pre-mRNA. Single-nucleotide variations (SNVs) affecting intronic cis-elements possibly compromise splicing, but no efficient tool has been available to identify them.
Following an effect-size analysis of each intronic nucleotide on annotated alternative splicing, researchers from Nagoya University extracted 105 parameters that could affect the strength of the splicing signals. However, they could not generate reliable support vector regression models to predict the percent-splice-in (PSI) scores for normal human tissues. Next, they generated support vector machine (SVM) models using 110 parameters to directly differentiate pathogenic SNVs in the Human Gene Mutation Database and normal SNVs in the dbSNP database, and obtained models with a sensitivity of 0.800±0.041 (mean and s.d.) and a specificity of 0.849±0.021. The researchers IntSplice models were more discriminating than SVM models that they generated with Shapiro–Senapathy score and MaxEntScan::score3ss.
They applied IntSplice to a naturally occurring and nine artificial intronic mutations in RAPSN causing congenital myasthenic syndrome. IntSplice correctly predicted the splicing consequences for nine of the ten mutants. The researchers created a web service program, IntSplice to predict splicing-affecting SNVs at intronic positions from −50 to −3.
Representative results of the IntSplice web service program
Predicted results are shown in the ‘RESULT’ column. The rightmost ‘NOTE’ column indicates which exon in which ENSEMBL transcript is predicted to lead to abnormal or normal splicing. The information from the columns ‘CHROM’ to ‘FILTER’ is included in the submitted VCF file, and is not edited by IntSplice. For example, a G-to-A transition at position 73 550 880 of chromosome 10, which is registered in HGMD, is predicted to cause aberrant splicing.
Availability – IntSplice is available at: http://www.med.nagoya-u.ac.jp/neurogenetics/IntSplice