Splicing defect detection software tools | Whole-genome sequencing data analysis
In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant.
Provides a splice site recognition method. NNSplice is a web application that analyzes the structure of donor and acceptor sites using a separate neural network recognizer for each site. This method was developed using a backpropagation feedforward neural network with one layer of hidden units to recognize donor and acceptor sites, respectively, using a novel optimized representative data set.
A service producing neural network predictions of splice sites in human, C. elegans and A. thaliana DNA. The prediction output for both server and mail server consist of the prediction for both direct (+) and complementary (-) strand. The output lists the predictions for donor and acceptor sites in the submitted sequence, as well as branchpoint predictions (for A. thaliana only).
Predicts the effects of mutations on splicing signals. HSF can forecast the disruption of the natural splice sites and is able to identify splicing motifs in any human sequence. This software combines more than 10 algorithms based on either position weight matrices (PWM), maximum entropy principle or motif comparison method. The PWM evaluates also the strength of 5' and 3' splice sites and branch points.
Scores Human 5' splice sites. MaxEntScan is based on the approach for modeling the sequences of short sequence motifs such as those involved in RNA splicing which simultaneously accounts for non-adjacent as well as adjacent dependencies between positions. This method is based on the 'maximum entropy principle' and generalizes most previous probabilistic models of sequence motifs such as weight matrix models and inhomogeneous Markov models.
Allows for the identification of putative exonic splicing enhancers (ESEs) and one of its most useful applications is the correct interpretation of the effects of disease-associated point mutations or polymorphisms. The development and refinement of reliable prediction tools for auxiliary splicing elements will have important implications for our ability to accurately identify the exon/intron structures of genes and predict their expression profile, to correctly interpret the effects of point mutations and/or polymorphisms, and to assess phenotypic risk.
Predicts which sequences have exonic splicing enhancer (ESE) activity by statistical analysis of exon-intron and splice site composition. When large data sets of human gene sequences were used, RESCUE-ESE identified 10 predicted ESE motifs. Representatives of all 10 motifs were found to display enhancer activity in vivo, whereas point mutants of these sequences exhibited sharply reduced activity. The motifs identified enable prediction of the splicing phenotypes of exonic mutations in human genes.
A flexible system for detecting splice sites in the genomic DNA of various eukaryotes. The system has been trained and tested successfully on Plasmodium falciparum (malaria), Arabidopsis thaliana, Human, Drosophila, and rice. Training data sets for Human and Arabidopsis thaliana are included.