Spliceman-A computational web server that predicts sequence variations in pre-mRNA splicing

Department of Molecular Biology, Cellular Biology and Biochemistry, Brown University, Providence, RI 02903, USA.
Bioinformatics (Impact Factor: 4.98). 02/2012; 28(7):1031-2. DOI: 10.1093/bioinformatics/bts074
Source: PubMed


It was previously demonstrated that splicing elements are positional dependent. We exploited this relationship between location and function by comparing positional distributions between all possible 4096 hexamers around a database of human splice sites. The distance measure used in this study found point mutations that produced higher distances disrupted splicing, whereas point mutations with smaller distances generally had no effect on splicing. Reasoning the idea that functional splicing elements have signature positional distributions around constitutively spliced exons, we introduce Spliceman-an online tool that predicts how likely distant mutations around annotated splice sites were to disrupt splicing. Spliceman takes a set of DNA sequences with point mutations and returns a ranked list to predict the effects of point mutations on pre-mRNA splicing. The current implementation included the analyses of 11 genomes: human, chimp, rhesus, mouse, rat, dog, cat, chicken, guinea pig, frog and zebrafish.
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Available from: Eric KH Lim, Apr 28, 2014
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    • "Primer sequences and PCR conditions are available upon request. The Spliceman web server (Lim and Fairbrother, 2012) was used to estimate the pathogenicity of the splice-site mutation identified in patient 3. "
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    • "The effect of noncoding variants on splicing and positioning and strength of exonic splice enhancers was evaluated using different in silico prediction programs integrated in Alamut Software (Interactive Software). These predictions were complemented by the ASSEDA tool ( and Spliceman algorithm ( [Lim and Fairbrother, 2012; Mucaki et al., 2013]. In addition, the localization of the variant in candidate regulatory regions was evaluated with RegulomeDb and datasets from the ENCODE project, and conservation was taken into account (Supp. "
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    • "Use of splice prediction tools is particularly important, as approximately 14% to 15% of all hereditary disease alleles are annotated as splicing mutations [55]. Groups that utilized a suite of splice prediction tools, such as the maximum entropy model MAXENT [62], ExonScan [63] or positional distribution analysis [64,65], were more likely to have identified potentially pathogenic mutations, particularly in the TTN gene in Family 1. "
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