ArticleNucleic acids research2011
In silico characterization and prediction of global protein-mRNA interactions in yeast.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 66 citations in OpenAlex.
- LncPTPred: predicting lncRNA-protein interaction based on crosslinking and immunoprecipitation (CLIP-Seq) data.Briefings in bioinformatics · 2025Article
- Prediction of LncRNA-protein Interactions Using Auto-Encoder, SE-ResNet Models and Transfer Learning.MicroRNA (Shariqah, United Arab Emirates) · 2024Article
- A Max-Margin Model for Predicting Residue-Base Contacts in Protein-RNA Interactions.Life (Basel, Switzerland) · 2021Article
- Capsule-LPI: a LncRNA-protein interaction predicting tool based on a capsule network.BMC bioinformatics · 2021Article
- Cellular models of Batten disease.Biochimica et biophysica acta. Molecular basis of disease · 2020Review
- RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.BMC bioinformatics · 2020Article
- Article
- R-DeeP: Proteome-wide and Quantitative Identification of RNA-Dependent Proteins by Density Gradient Ultracentrifugation.Molecular cell · 2019Article
- beRBP: binding estimation for human RNA-binding proteins.Nucleic acids research · 2019Article
- RNA-protein interactions in an unstructured context.FEBS letters · 2018Review
- A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.Scientific reports · 2018Article
- A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.Molecular therapy. Nucleic acids · 2018Article
- RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence.Molecules (Basel, Switzerland) · 2018Article
- Prediction of RNA-protein interactions using conjoint triad feature and chaos game representation.Bioengineered · 2018Article
- Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks.Scientific reports · 2017Article
- Selecting high-quality negative samples for effectively predicting protein-RNA interactions.BMC systems biology · 2017Article
- IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction.BMC genomics · 2016Article
- Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks.Scientific reports · 2016Article
- aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events.PloS one · 2016Article
- Barcelona conference on epigenetics and cancer 2015: Coding and non-coding functions of the genome.Epigenetics · 2016Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Post-transcriptional gene regulation is mediated through complex networks of protein-RNA interactions. The targets of only a few RNA binding proteins (RBPs) are known, even in the well-characterized budding yeast. In silico prediction of protein-RNA interactions is therefore useful to guide experiments and to provide insight into regulatory networks. Computational approaches have identified RBP targets based on sequence binding preferences. We investigate here to what extent RBP-RNA interactions can be predicted based on RBP and mRNA features other than sequence motifs. We analyze global relationships between gene and protein properties in general and between selected RBPs and known mRNA targets in particular. Highly translated RBPs tend to bind to shorter transcripts, and transcripts bound by the same RBP show high expression correlation across different biological conditions. Surprisingly, a given RBP preferentially binds to mRNAs that encode interaction partners for this RBP, suggesting coordinated post-transcriptional auto-regulation of protein complexes. We apply a machine-learning approach to predict specific RBP targets in yeast. Although this approach performs well for RBPs with known targets, predictions for uncharacterized RBPs remain challenging due to limiting experimental data. We also predict targets of fission yeast RBPs, indicating that the suggested framework could be applied to other species once more experimental data are available.
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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.