ArticlePLoS computational biology2010
RNAcontext: a new method for learning the sequence and structure binding preferences of RNA-binding proteins.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
123 citing papers in PubMed, 275 citations in OpenAlex.
- RNA-binding proteins in aging and age-related diseases: roles, mechanisms, and a large language model analysis.Biogerontology · 2026Review
- A protocol for single-sequence protein-RNA complex structure prediction using ProRNA3D-single.STAR protocols · 2026Article
- PRIME-BSPre: a genome-wide protein-RNA binding sites prediction method based on templates.BMC genomics · 2026Article
- Article
- Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models.Cell systems · 2025Article
- RBPsuite 2.0: an updated RNA-protein binding site prediction suite with high coverage on species and proteins based on deep learning.BMC biology · 2025Article
- rbpTransformer: A novel deep learning model for prediction of piRNA and mRNA bindings.PloS one · 2025Article
- Article
- Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes.Biology · 2023Article
- A systematic benchmark of machine learning methods for protein-RNA interaction prediction.Briefings in bioinformatics · 2023Article
- Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.Genome biology · 2023Article
- In silico and in vitro analysis of the impact of single substitutions within EXO-motifs on Hsa-MiR-1246 intercellular transfer in breast cancer cell.Journal of applied genetics · 2023Article
- ResidualBind: Uncovering Sequence-Structure Preferences of RNA-Binding Proteins with Deep Neural Networks.Methods in molecular biology (Clifton, N.J.) · 2023Article
- De novo prediction of RNA-protein interactions with graph neural networks.RNA (New York, N.Y.) · 2022Article
- PRIESSTESS: interpretable, high-performing models of the sequence and structure preferences of RNA-binding proteins.Nucleic acids research · 2022Article
- RNANetMotif: Identifying sequence-structure RNA network motifs in RNA-protein binding sites.PLoS computational biology · 2022Article
- DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites.BMC bioinformatics · 2022Article
- PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information.Bioinformatics (Oxford, England) · 2022Article
- Interrelationship between miRNA and splicing factors in pancreatic ductal adenocarcinoma.Epigenetics · 2022Review
- RNALigands: a database and web server for RNA-ligand interactions.RNA (New York, N.Y.) · 2022Article
63 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Metazoan genomes encode hundreds of RNA-binding proteins (RBPs). These proteins regulate post-transcriptional gene expression and have critical roles in numerous cellular processes including mRNA splicing, export, stability and translation. Despite their ubiquity and importance, the binding preferences for most RBPs are not well characterized. In vitro and in vivo studies, using affinity selection-based approaches, have successfully identified RNA sequence associated with specific RBPs; however, it is difficult to infer RBP sequence and structural preferences without specifically designed motif finding methods. In this study, we introduce a new motif-finding method, RNAcontext, designed to elucidate RBP-specific sequence and structural preferences with greater accuracy than existing approaches. We evaluated RNAcontext on recently published in vitro and in vivo RNA affinity selected data and demonstrate that RNAcontext identifies known binding preferences for several control proteins including HuR, PTB, and Vts1p and predicts new RNA structure preferences for SF2/ASF, RBM4, FUSIP1 and SLM2. The predicted preferences for SF2/ASF are consistent with its recently reported in vivo binding sites. RNAcontext is an accurate and efficient motif finding method ideally suited for using large-scale RNA-binding affinity datasets to determine the relative binding preferences of RBPs for a wide range of RNA sequences and structures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.