ArticleBMC bioinformatics2014
Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity.BMC bioinformatics · 2024Article
- KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.Journal of translational medicine · 2023Article
- Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes.Biology · 2023Article
- Deep neural networks for inferring binding sites of RNA-binding proteins by using distributed representations of RNA primary sequence and secondary structure.BMC genomics · 2020Article
- RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.BMC bioinformatics · 2020Article
- beRBP: binding estimation for human RNA-binding proteins.Nucleic acids research · 2019Article
- RNA-protein interactions in an unstructured context.FEBS letters · 2018Review
- Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.BMC genomics · 2018Article
- A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.Molecular therapy. Nucleic acids · 2018Article
- SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation data.PLoS computational biology · 2018Article
- RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.BMC bioinformatics · 2017Article
- Advances in the characterization of RNA-binding proteins.Wiley interdisciplinary reviews. RNA · 2016Review
- IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction.BMC genomics · 2016Article
- Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins.Bioinformatics (Oxford, England) · 2016Article
- Revealing protein-lncRNA interaction.Briefings in bioinformatics · 2016Review
- IRES-dependent translated genes in fungi: computational prediction, phylogenetic conservation and functional association.BMC genomics · 2015Article
- Specificity and nonspecificity in RNA-protein interactions.Nature reviews. Molecular cell biology · 2015Review
- Briefing in application of machine learning methods in ion channel prediction.TheScientificWorldJournal · 2015Review
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundRNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins. Protein-RNA interactions are typically characterized using in vivo and in vitro experiments but these may not detect all binding partners. Therefore, computational methods that capture the protein-dependent nature of such binding interactions could help to predict potential binding partners in silico.
resultsWe have developed three methods to predict whether an RNA can interact with a particular RNA-binding protein using support vector machines and different features based on the sequence (the Oli method), the motif score (the OliMo method) and the secondary structure (the OliMoSS method). We applied these approaches to different experimentally-derived datasets and compared the predictions with RNAcontext and RPISeq. Oli outperformed OliMoSS and RPISeq, confirming our protein-specific predictions and suggesting that tetranucleotide frequencies are appropriate discriminative features. Oli and RNAcontext were the most competitive methods in terms of the area under curve. A precision-recall curve analysis achieved higher precision values for Oli. On a second experimental dataset including real negative binding information, Oli outperformed RNAcontext with a precision of 0.73 vs. 0.59.
conclusionsOur experiments showed that features based on primary sequence information are sufficiently discriminating to predict specific RNA-protein interactions. Sequence motifs and secondary structure information were not necessary to improve these predictions. Finally we confirmed that protein-specific experimental data concerning RNA-protein interactions are valuable sources of information that can be used for the efficient training of models for in silico predictions. The scripts are available upon request to the corresponding author.
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