Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants

Abstract

Background: Branch points (BPs) map within short motifs upstream of acceptor splice sites (3'ss) and are essential for splicing of premature mRNA. Several BP-dedicated bioinformatics tools, including HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR and RNABPS were developed during the last decade. Here, we evaluated their capability to detect the position of BPs, and also to predict the impact on splicing of variants occurring upstream of 3'ss. Results: We used a large set of constitutive and alternative human 3'ss collected from Ensembl (n = 264,787 3'ss) and from in-house RNAseq experiments (n = 51,986 3'ss). We also gathered an unprecedented collection of functional splicing data for 120 variants (62 unpublished) occurring in BP areas of disease-causing genes. Branchpointer showed the best performance to detect the relevant BPs upstream of constitutive and alternative 3'ss (99.48 and 65.84% accuracies, respectively). For variants occurring in a BP area, BPP emerged as having the best performance to predict effects on mRNA splicing, with an accuracy of 89.17%. Conclusions: Our investigations revealed that Branchpointer was optimal to detect BPs upstream of 3'ss, and that BPP was most relevant to predict splicing alteration due to variants in the BP area.

Variant effect on splicing regulatory elements, branchpoint usage, and pseudoexonization: Strategies to enhance bioinformatic prediction using hereditary cancer genes as exemplars

“…This variant was not captured by in silico prediction methods but it is within the −18 to −44 nucleotide window of high‐confidence annotated BPs (Signal et al, 2018). Nine other variants within the BP window in BRCA1 , MLH1 , and RAD51C have been reported to lead to exon skipping (Leman et al, 2020).…”

“…It is important to note that variants predicted to disrupt a BP do not necessarily induce aberrant splicing, as introns can have multiple functional BPs (Mercer et al, 2015), which adds to the complexity of predicting the spliceogenicity of a single variant in the BP window. Moreover, in the analysis of Leman et al (2020), the use of score change to predict BP disruption by a variant was found to not be the best strategy to predict spliceogenic variants. According to Leman et al (2020), the best approach would be to consider a variant as potentially spliceogenic if it is located in the BP motif regardless of score change.…”

“…Moreover, in the analysis of Leman et al (2020), the use of score change to predict BP disruption by a variant was found to not be the best strategy to predict spliceogenic variants. According to Leman et al (2020), the best approach would be to consider a variant as potentially spliceogenic if it is located in the BP motif regardless of score change. Performance of BPP, Branchpointer, HSF, LaBranchoR, RNABPS, and SVM‐BPfinder was evaluated by checking the colocation of confirmed spliceogenic variants within predicted BP motifs, and revealed BPP as having the highest accuracy of 89% (Leman et al, 2020).…”

“…According to Leman et al (2020), the best approach would be to consider a variant as potentially spliceogenic if it is located in the BP motif regardless of score change. Performance of BPP, Branchpointer, HSF, LaBranchoR, RNABPS, and SVM‐BPfinder was evaluated by checking the colocation of confirmed spliceogenic variants within predicted BP motifs, and revealed BPP as having the highest accuracy of 89% (Leman et al, 2020). In their positive control set of 38 spliceogenic variants, 32 variants were within BP motifs predicted by BPP, which predicted a total of 39 BP motifs (Leman et al, 2020).…”

BP-GAN: Interpretable Human Branchpoint Prediction Using Attentive Generative Adversarial Networks

“…To summarize, the existing deep neural network-based approaches are merely straightforward applications of CNN or RNN to BP prediction and few of the previous studies addressed the issue of interpretability of the predictions. The work in [31] presented the comprehensive evaluations on the recent approaches for various performance measures on in-house RNA datasets as well as public ones.…”