Predicting RNA splicing branchpoints

RNA splicing is a process where introns are removed from pre-mRNA, resulting in mature mRNA. It requires three main signals, a donor splice site (5’ss), an acceptor splice site (3’ss) and a branchpoint (BP). Splice site prediction is a well-studied problem with several reliable prediction tools. However, branchpoint prediction is a harder problem, mainly due to varying nucleotide motifs in the branchpoint area and the existence of multiple branch-points in a single intron. An RNN based approach called LaBranchoR was introduced as the state-of-the-art method for predicting a single BP for each 3’ss. In this work, we explore the fact that previous research reported that 95% of introns have multiple BPs with an estimated average of 5 to 6 BPs per intron. To that end, we extend the existing encoder in the LaBranchoR network with a PointerNetwork decoder. We train our new encoder-decoder model, named RNA PtrNets, on 70-nucleotide-long annotated sequences taken from three publicly available datasets. We evaluate its accuracy and demonstrate how well the predictor can generate multiple branchpoints on the given datasets.