BEAM web server: a tool for structural RNA motif discovery

Pietrosanto, Marco; Adinolfi, Marta; Casula, Riccardo; Ausiello, Gabriele; Ferré, Fabrizio; Helmer-Citterich, Manuela

doi:10.1093/bioinformatics/btx704

Cited by 11 publications

(17 citation statements)

References 19 publications

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“…LIN28A (43,44) has a zinc-finger domain CCHC that has been hypothesized to bind an internal loop in the hairpin containing the sequence motif, at variable distances (45,46). The sequence motif that BEAM identifies is similar to the one found by other methods: the motif found in the data from Cho and colleagues is found in 38% of the RNA, and the structure motif shows an internal loop structure in the hairpin, in 37% of RNAs.…”

Section: Resultsmentioning

confidence: 99%

Discovering sequence and structure landscapes in RNA interaction motifs

Adinolfi

Pietrosanto

Parca

et al. 2019

Nucleic Acids Research

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RNA molecules are able to bind proteins, DNA and other small or long RNAs using information at primary, secondary or tertiary structure level. Recent techniques that use cross-linking and immunoprecipitation of RNAs can detect these interactions and, if followed by high-throughput sequencing, molecules can be analysed to find recurrent elements shared by interactors, such as sequence and/or structure motifs. Many tools are able to find sequence motifs from lists of target RNAs, while others focus on structure using different approaches to find specific interaction elements. In this work, we make a systematic analysis of RBP–RNA and RNA–RNA datasets to better characterize the interaction landscape with information about multi-motifs on the same RNAs. To achieve this goal, we updated our BEAM algorithm to combine both sequence and structure information to create pairs of patterns that model motifs of interaction. This algorithm was applied to several RNA binding proteins and ncRNAs interactors, confirming already known motifs and discovering new ones. This landscape analysis on interaction variability reflects the diversity of target recognition and underlines that often both primary and secondary structure are involved in molecular recognition.

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Section: Resultsmentioning

confidence: 99%

Discovering sequence and structure landscapes in RNA interaction motifs

Adinolfi

Pietrosanto

Parca

et al. 2019

Nucleic Acids Research

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“…It has been well documented that linear representations such as Positional Weight Matrices (PWM) have their limitations in capturing the binding preferences (85,86). A number of methods such as BEAM or GraphProt have improved upon PWMs by adding structural descriptors to each position, i.e., helix, stem, loop, etc, which has showed improvement (16,25). Despite these recent developments, we feel there is still room for improvement.…”

Section: Discussionmentioning

confidence: 99%

“…For example, RNA binding domains can be grouped into single stranded or double stranded RNA binding domains, ssRBD and dsRBD, based on their preference for RNA targets that are either single stranded (unpaired) or double stranded (paired). A number of computational methods had been developed to ascertain the structure properties of these RBP bound RNAs with the aim to incorporate structural information into a predictive model that can help decode mechanisms that regulate protein-RNA interactions (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33). A number of these representative methods are briefly described below.…”

Section: Introductionmentioning

confidence: 99%

“…The structure-based methods differ in how they encode RNA structure information in their models thus they can be grouped into two broad categories according to the abstract level of structural encoding. Methods like MEMERIS (18), mCarts (29) and SMARTIV (22) apply simplified structure representations, including RNA accessibility score and discrete secondary structure notations such as paired and unpaired, while methods including RNAcontext (17), ssHMM (24), BEAM (25), GraphProt (16) and SARNAclust (27) characterize the shape of substructures with different labeling methods. Approaches such as RNAcontext (17) explicitly label individual nucleotides with an additional feature indicating the secondary structure of the particular nucleotide, i.e., paired, hairpin loop.…”

Section: Introductionmentioning

confidence: 99%

“…Methods such as Graphprot (16) and ssHMM (24) further captured structure inter-dependencies between neighboring nucleotides through graph based encoding or hidden Markov model. Moreover, methods such as BEAM (25) attempted to explicitly encode the pattern of nucleotide base pairing and catalogue the occurrence of specific secondary structure motifs based on the length of the stem or the size of the hairpin loop. SARNAclust (27) comprehensively studied the topology and annotations of the complete RNA structure and provides several options for graph transformations of RNA shapes.…”

Section: Introductionmentioning

confidence: 99%

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RNANetMotif: identifying sequence-structure RNA network motifs in RNA-protein binding sites

Wen

Xue

et al. 2021

Preprint

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RNA molecules can adopt stable secondary and tertiary structures, which is essential in mediating physical interactions with other partners such as RNA binding proteins (RBPs) and in carrying out their cellular functions. In vivo and in vitro experiments such as RNAcompete and eCLIP have revealed in vitro binding preferences of RBPs to RNA oligomers and in vivo binding sites in cells. Analysis of these binding data showed that the structure properties of the RNAs in these binding sites are important determinants of the binding events; however, it has been a challenge to incorporate the structure information into an interpretable model. Here we describe a new approach, RNANetMotif, which takes predicted secondary structure of thousands of RNA sequences bound by an RBP as input and uses a graph theory approach to recognize enriched subgraphs. These enriched subgraphs are in essence shared sequence-structure elements that are important in RBP-RNA binding. To validate our approach, we performed RNA structure modeling via discrete molecular dynamics folding simulations for selected 4 RBPs, and RNA-protein docking for LIN28. The simulation results, e.g., solvent accessibility and energetics, further support the biological relevance of the discovered network subgraphs.

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

et al. 2019

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Interactions between RNAs and proteins play essential roles in many important biological processes. Benefitting from the advances of next generation sequencing technologies, hundreds of RNA-binding proteins (RBP) and their associated RNAs have been revealed, which enables the large-scale prediction of RNA-protein interactions using machine learning methods. Till now, a wide range of computational tools and pipelines have been developed, including deep learning models, which have achieved remarkable performance on the identification of RNA-protein binding affinities and sites. In this review, we provide an overview of the successful implementation of various deep learning approaches for predicting RNA-protein interactions, mainly focusing on the prediction of RNA-protein interaction pairs and RBP-binding sites on RNAs. Furthermore, we discuss the advantages and disadvantages of these approaches, and highlight future perspectives on how to design better deep learning models. Finally, we suggest some promising future directions of computational tasks in the study of RNA-protein interactions, especially the interactions between noncoding RNAs and proteins. deep learning, feature representation, machine learning, motif discovery, RNA-protein interactions *The first two authors are co-first authors.

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BEAM web server: a tool for structural RNA motif discovery

Cited by 11 publications

References 19 publications

Discovering sequence and structure landscapes in RNA interaction motifs

Discovering sequence and structure landscapes in RNA interaction motifs

RNANetMotif: identifying sequence-structure RNA network motifs in RNA-protein binding sites

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

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