The majority of human genes are spliced, forming a mature mRNA following intron removal and subsequent exon ligation in the spliceosome complex [1]. During this reaction, U1 snRNP and SF1 bind the 5’ splice site (5’ SS) and the branchpoint respectively, and a trans-esterification reaction forms an intron lariat intermediate. A subsequent trans-esterification reaction between the 5’ SS and the 3’ SS removes the intron lariat and forms a spliced RNA product from the flanking exons. Recognition of the sequence-based splicing elements – the 3’ SS, 5’ SS and the branchpoint – by small ribonucleoprotein particles (snRNPs) is a critical step in defining exon boundaries, and therefore the mature mRNA product formed [2]. Branchpoint elements have traditionally been difficult to identify, and their role in splicing regulation and disease poorly understood. Branchpointer is an R package for predicting splicing branchpoints using primary genome sequence and exon annotations alone. Branchpointer uses a machine-learning model, trained with empirical branchpoint annotations, to identify branchpoint elements in introns with best-in-class sensitivity (60.7%) and specificity (97.8%). In addition, branchpointer can evaluate the impact of mutations on branchpoint architecture to inform functional interpretation of genetic variants.
R-package branchpointer can be installed:
library(devtools)
install_github("betsig/branchpointer")
After installation, the package can be loaded into R.
library(branchpointer)
For details of how to use this package, please see the vignette.
branchpointer requires bedtools to be installed if using a local .fa file for sequence retrieval. Alternatively, an R BSgenome object can now be used (see vignette).
Package: branchpointer
Type: Package
Title: Prediction of intronic splicing branchpoints
Version: 1.3.1
Date: 2017-07-14
Author: Beth Signal
Maintainer: Beth Signal beth.signal@gmail.com
Description: Predicts branchpoint probability for sites in intronic branchpoint windows. queries can be supplied as intronic regions; or to evaluate the effects of mutations, SNPs.
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