ArticleNucleic acids research2016
A comprehensive comparison of general RNA-RNA interaction prediction methods.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Evaluating Plasma-Handling Effects on Extracellular Vesicle RNA Yield and Cargo Profiles in Fish: A Methodological Insight.Journal of extracellular biology · 2026Article
- Small Regulatory RNAs in Prokaryotes: Key Features, Identification, Environmental Roles, and Applications.Microorganisms · 2026Review
- Small non-coding RNAMicrobiology spectrum · 2026Article
- An encyclopedic regulatory and functional atlas of RNA interactomes.Nature methods · 2026Article
- Machine Learning Approaches for Predicting RNA-RNA/DNA Interactions.Methods in molecular biology (Clifton, N.J.) · 2026Review
- RAP-RNA: A Methodological Approach to Study Base-Pairing Between Endogenous RNAs.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Hybrid MPI/OpenMP Implementation of RIblast for Transcriptome-Scale lncRNA-RNA Interaction Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Hierarchical analysis of RNA secondary structures with pseudoknots based on sections.PLoS computational biology · 2026Article
- Benchmarking the methods for predicting base pairs in RNA-RNA interactions.Bioinformatics (Oxford, England) · 2025Article
- Experimental and computational methods for studying the dynamics of RNA-RNA interactions in SARS-COV2 genomes.Briefings in functional genomics · 2024Review
- CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.GigaScience · 2024Article
- How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA.Methods in molecular biology (Clifton, N.J.) · 2024Review
- LncRNAs exert indispensable roles in orchestrating the interaction among diverse noncoding RNAs and enrich the regulatory network of plant growth and its adaptive environmental stress response.Horticulture research · 2023Article
- TargetRNA3: predicting prokaryotic RNA regulatory targets with machine learning.Genome biology · 2023Article
- A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools.Briefings in bioinformatics · 2023Review
- LinearCoFold and LinearCoPartition: linear-time algorithms for secondary structure prediction of interacting RNA molecules.Nucleic acids research · 2023Article
- RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction.Nucleic acids research · 2023Article
- Web Services for RNA-RNA Interaction Prediction.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Women in the European Virus Bioinformatics Center.Viruses · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA-RNA interactions are fast emerging as a major functional component in many newly discovered non-coding RNAs. Basepairing is believed to be a major contributor to the stability of these intermolecular interactions, much like intramolecular basepairs formed in RNA secondary structure. As such, using algorithms similar to those for predicting RNA secondary structure, computational methods have been recently developed for the prediction of RNA-RNA interactions. We provide the first comprehensive comparison comprising 14 methods that predict general intermolecular basepairs. To evaluate these, we compile an extensive data set of 54 experimentally confirmed fungal snoRNA-rRNA interactions and 102 bacterial sRNA-mRNA interactions. We test the performance accuracy of all methods, evaluating the effects of tool settings, sequence length, and multiple sequence alignment usage and quality. Our results show that-unlike for RNA secondary structure prediction--the overall best performing tools are non-comparative energy-based tools utilizing accessibility information that predict short interactions on this data set. Furthermore, we find that maintaining high accuracy across biologically different data sets and increasing input lengths remains a huge challenge, causing implications for de novo transcriptome-wide searches. Finally, we make our interaction data set publicly available for future development and benchmarking efforts.
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Registered trials
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