Branch Point Selection in RNA Splicing Using Deep Learning

Nazari, Iman; Tayara, Hilal; Chong, Kil To

doi:10.1109/access.2018.2886569

Cited by 50 publications

(44 citation statements)

References 23 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The deep learning tools, LaBranchoR and RNABS showed the maximum number of common predicted BPs from Ensembl (28.63%) and from RNA-seq (33.57%) data. Indeed, these two tools are both based on the same deep learning approach (bidirectional long short-term memory) and used the same sequence length (70 nt) as input [20,21]. By comparison, RNABPS employed a dilated convolution model explaining and showed an improvement of prediction compared to LaB-ranchoR (73.06% against 64.77% of accuracy) using the Ensembl data (Table 3).…”

Section: Discussionmentioning

confidence: 99%

“…This collection of BPs was extended by two further studies: the first used 1.31 trillion reads from 17,164 RNA-seq data sets [16], and the second identified BPs by the spliceosome iCLIP method [17]. Thus, several bioinformatics tools for BP prediction have recently emerged: Branch Point Prediction (BPP) [18], Branchpointer [19], LaBranchoR [20] and RNA Branch Point Selection (RNABPS) [21] (Table 1). Briefly, HSF uses a position weighted matrix approach with a 7mer motif as a reference (5 nt upstream and 1 nt downstream of the branch point A) ( Fig.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

View full text Add to dashboard Cite

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 We also gathered an unprecedented collection of functional splicing data for 120 variants (62 unpublished) occurring in BP areas of disease-causing genes.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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The deep learning tools, LaBranchoR and RNABS showed the maximum number of common predicted BPs from Ensembl (28.63 %) and from RNA-seq (33.57 %) data. Indeed, these two tools are both based on the same deep learning approach (bidirectional long short-term memory) and used the same sequence length (70 nt) as input [20,21]. By comparison, RNABPS employed a dilated convolution model explaining and showed an improvement of prediction compared to LaBranchoR (73.06 % against 64.77% of accuracy) using the Ensembl data (Table 3).…”

Section: Discussionmentioning

confidence: 99%

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

Leman

Tubeuf

Raad

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Branch points (BPs) map within short motifs upstream of acceptor splice sites (3’ss) and are essential for splicing of pre-mature 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. Keywords: Branch Point, Prediction, RNA, Benchmark, HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR, RNABPS, Variants

show abstract

“…Earlier studies on BP prediction were largely based on classical machine learning with handcrafted sequential features [8]- [11], or deep neural networks [3], [12], [27], [28]. The former included features such as sequence conservation and positional bias for a support vector machine [8], motifs of an intron sequence for an ensemble of multiple algorithms [9], and a score function calculated from position-specific scoring matrix (PSSM) and binding energy of spliceosome [10].…”

Section: Machine Learning Approaches For Bp Predictionmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

Branchpoints (BPs) are essential sequence elements of ribonucleic acids (RNAs) in splicing, which is the process of creating a messenger RNA (mRNA) that is translated into proteins. This study proposes to develop deep neural networks for BP prediction. Extensive previous studies have shown that the existence of BP sites depends on sequence patterns called motifs; hence, the prediction model must accurately explain its decisions in terms of motifs. Existing approaches utilized either handcrafted features for interpretable, though less accurate, predictions or deep neural networks that were accurate but difficult to explain. To address the aforementioned difficulties, the proposed method incorporates 1) generative adversarial networks (GANs) to learn the latent structure of RNA sequences, and 2) an attention mechanism to learn sequence-positional long-term dependency for accurate prediction and interpretation. Our method achieves highly satisfying results in various performance metrics with adequate interpretability. We demonstrated that, without any prior biological knowledge, BP prediction by the proposed method is closely related to three motifs, the consensus sequence surrounding BPs, polypyrimidine tract, and 3' splice site, that are well-established in molecular biology. INDEX TERMS Branchpoint prediction, deep neural networks, generative adversarial networks, interpretability.

show abstract

Branch Point Selection in RNA Splicing Using Deep Learning

Cited by 50 publications

References 23 publications

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

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

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

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

Contact Info

Product

Resources

About