Recent methodology progress of deep learning for RNA–protein interaction prediction

Pan, Xiaoyong; Yang, Yang; Xia, Chun-Qiu; Mirza, Aashiq H.; Shen, Hong‐Bin

doi:10.1002/wrna.1544

Cited by 58 publications

(48 citation statements)

References 80 publications

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“…Based on next generation sequencing technologies including single cell RNA-sequencing hundreds of RBPs and their associated RNAs have been identified, which allowed the prediction of RNA–protein interactions using deep learning approaches [ 87 ]. It highlights the importance of RBPs in splicing and translation [ 72 ].…”

Section: Methodical Strategies and Specific Challenges Regarding Mmentioning

confidence: 99%

Expression, Regulation and Function of microRNA as Important Players in the Transition of MDS to Secondary AML and Their Cross Talk to RNA-Binding Proteins

Bauer

Vaxevanis

Heimer

et al. 2020

IJMS

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Myelodysplastic syndromes (MDS), heterogeneous diseases of hematopoietic stem cells, exhibit a significant risk of progression to secondary acute myeloid leukemia (sAML) that are typically accompanied by MDS-related changes and therefore significantly differ to de novo acute myeloid leukemia (AML). Within these disorders, the spectrum of cytogenetic alterations and oncogenic mutations, the extent of a predisposing defective osteohematopoietic niche, and the irregularity of the tumor microenvironment is highly diverse. However, the exact underlying pathophysiological mechanisms resulting in hematopoietic failure in patients with MDS and sAML remain elusive. There is recent evidence that the post-transcriptional control of gene expression mediated by microRNAs (miRNAs), long noncoding RNAs, and/or RNA-binding proteins (RBPs) are key components in the pathogenic events of both diseases. In addition, an interplay between RBPs and miRNAs has been postulated in MDS and sAML. Although a plethora of miRNAs is aberrantly expressed in MDS and sAML, their expression pattern significantly depends on the cell type and on the molecular make-up of the sample, including chromosomal alterations and single nucleotide polymorphisms, which also reflects their role in disease progression and prediction. Decreased expression levels of miRNAs or RBPs preventing the maturation or inhibiting translation of genes involved in pathogenesis of both diseases were found. Therefore, this review will summarize the current knowledge regarding the heterogeneity of expression, function, and clinical relevance of miRNAs, its link to molecular abnormalities in MDS and sAML with specific focus on the interplay with RBPs, and the current treatment options. This information might improve the use of miRNAs and/or RBPs as prognostic markers and therapeutic targets for both malignancies.

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Section: Methodical Strategies and Specific Challenges Regarding Mmentioning

confidence: 99%

Expression, Regulation and Function of microRNA as Important Players in the Transition of MDS to Secondary AML and Their Cross Talk to RNA-Binding Proteins

Bauer

Vaxevanis

Heimer

et al. 2020

IJMS

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“…Innovative genome‐wide approaches providing tissue‐ or cell type‐specific resolutions additionally have been designed to unravel lncRNA‐protein interactions across the genome (PIP‐seq; Box 1). Furthermore, advances in the computational prediction of RNA–protein interactions also have been reported (Pan et al ., 2019; Sagar & Xue, 2019).…”

Section: Long Noncoding Rnas Forming Ribonucleoprotein Complexesmentioning

confidence: 99%

Functional classification of plant long noncoding RNAs: a transcript is known by the company it keeps

et al. 2020

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The extraordinary maturation in high-throughput sequencing technologies has revealed the existence of a complex network of transcripts in eukaryotic organisms, including thousands of long noncoding (lnc) RNAs with little or no protein-coding capacity. Subsequent discoveries have shown that lncRNAs participate in a wide range of molecular processes, controlling gene expression and protein activity though direct interactions with proteins, DNA or other RNA molecules. Although significant advances have been achieved in the understanding of lncRNA biology in the animal kingdom, the functional characterization of plant lncRNAs is still in its infancy and remains a major challenge. In this review, we report emerging functional and mechanistic paradigms of plant lncRNAs and partner molecules, and discuss how cutting-edge technologies may help to identify and classify yet uncharacterized transcripts into functional groups.

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“…In addition, SMARTIV cannot predict RBP binding sites for a single RNA sequence. The backend predictor of the above webservers are non-deep learning-based methods, which are proved to be inferior to deep learning-based methods for predicting RBP binding sites [18]. Moreover, no online webserver is currently available for predicting RBP binding sites on circRNAs.…”

Section: Introductionmentioning

confidence: 99%

RBPsuite: RNA-protein binding sites prediction suite based on deep learning

Pan

Fang

et al. 2020

BMC Genomics

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Background RNA-binding proteins (RBPs) play crucial roles in various biological processes. Deep learning-based methods have been demonstrated powerful on predicting RBP sites on RNAs. However, the training of deep learning models is very time-intensive and computationally intensive. Results Here we present a deep learning-based RBPsuite, an easy-to-use webserver for predicting RBP binding sites on linear and circular RNAs. For linear RNAs, RBPsuite predicts the RBP binding scores with them using our updated iDeepS. For circular RNAs (circRNAs), RBPsuite predicts the RBP binding scores with them using our developed CRIP. RBPsuite first breaks the input RNA sequence into segments of 101 nucleotides and scores the interaction between the segments and the RBPs. RBPsuite further detects the verified motifs on the binding segments gives the binding scores distribution along the full-length sequence. Conclusions RBPsuite is an easy-to-use online webserver for predicting RBP binding sites and freely available at http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/.

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Recent methodology progress of deep learning for RNA–protein interaction prediction

Cited by 58 publications

References 80 publications

Expression, Regulation and Function of microRNA as Important Players in the Transition of MDS to Secondary AML and Their Cross Talk to RNA-Binding Proteins

Expression, Regulation and Function of microRNA as Important Players in the Transition of MDS to Secondary AML and Their Cross Talk to RNA-Binding Proteins

Functional classification of plant long noncoding RNAs: a transcript is known by the company it keeps

RBPsuite: RNA-protein binding sites prediction suite based on deep learning

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