Experimental detection of RNA splicing branchpoints, the nucleotide serving as the nucleophile in the first catalytic step of splicing, is difficult. To date, annotations exist for only 16-21% of 3' splice sites in the human genome and even these limited annotations have been shown to be plagued by noise.
Allows users to identify branchpoints throughout the human genome thanks to gene annotations. Branchpointer generates branchpoint window regions corresponding to annotated exon(s) within a queried gene, transcript or exon id. It takes predictions of branchpoint probabilities from a reference and alternative Single Nucleotide Polymorphism (SNP) and summarizes the effects of the SNP.
Allows branchpoint (BP) determination. GAEM is an ensemble of learning method that integrates several features and multiple classifiers to construct BP prediction models. The method can find TNA BPs as well as other types of BPs. It was evaluated on a benchmark dataset, using 5-fold cross-validation (5CV). This method is based on the utilization of linear relationship to proceed.
Provides accurate genome-wide branchpoint annotations. LaBranchoR is a computational method that disregards noise in the experimental data leading to robust predictions. It helps in the identification of pathogenic genetic variants. This method offers in silico mutagenesis scores able to distinguish pathogenic variants from variants in the general population. This show that explicit branchpoint prediction provides information not captured by others generic splicing models.
Allows branchpoint (BP) prediction. LREM is an ensemble of learning scheme that integrates different features and different classifiers to build BP prediction models. The method can predict TNA BPs as well as other types of BPs. It was tested on a benchmark dataset, using 5-fold cross-validation (5CV). This approach employs nonlinear relationship to proceed and is able to return satisfying results for the branchpoint prediction tasks.