ArticleBioinformatics (Oxford, England)2017
RIblast: an ultrafast RNA-RNA interaction prediction system based on a seed-and-extension approach.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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54 citing papers in PubMed.
- Genome-wide identification of long non-coding RNAs revealing their regulatory roles under seed development in ricebean (Vigna umbellata).BMC plant biology · 2026Article
- The Role of MNX1-AS1 in Ovarian Cancer Resistance and Tumor Progression via RNA-RNA Interactions.International journal of molecular sciences · 2026Article
- The role of low-complexity repeats in RNA-RNA interactions and a deep learning framework for duplex prediction.Nature communications · 2026Article
- Standardized Workflow for Long Noncoding RNA Prediction and Expression Profiling in Wheat Under Stem Rust Stress.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Hierarchical analysis of RNA secondary structures with pseudoknots based on sections.PLoS computational biology · 2026Article
- Hybrid MPI/OpenMP Implementation of RIblast for Transcriptome-Scale lncRNA-RNA Interaction Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Machine Learning Approaches for Predicting RNA-RNA/DNA Interactions.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Concentration-dependent formation of intersegment interactions in the viral inclusions of influenza A virus infected cells.iScience · 2025Article
- Identification of Rice LncRNAs and Their Roles in the Rice Blast Resistance Network Using Transcriptome and Translatome.Plants (Basel, Switzerland) · 2025Article
- Functional divergence of LncRNAs in wheat-fungal interactions: insights from stem rust-responsive wheat transcriptomes.Physiology and molecular biology of plants : an international journal of functional plant biology · 2025Article
- Integrated analysis of microRNAs and lncRNAs expression profiles reveals regulatory modules during adventitious shoot induction in Moringa oleifera Lam.BMC plant biology · 2024Article
- A bioinformatic approach for the prediction and functional classification of Toxoplasma gondii long non-coding RNAs.Scientific reports · 2024Article
- The roles of different gene expression regulators in acoustic variation in the intermediate horseshoe bat revealed by long-read and short-read RNA sequencing data.Current zoology · 2024Article
- Regulatory role of lncH19 in RAC1 alternative splicing: implication for RAC1B expression in colorectal cancer.Journal of experimental & clinical cancer research : CR · 2024Article
- Long Non-Coding RNAs and Alzheimer's Disease: Towards Personalized Diagnosis.International journal of molecular sciences · 2024Article
- lncRNA-mRNA Co-Expression and Regulation Analysis in Lung Fibroblasts from Idiopathic Pulmonary Fibrosis.Non-coding RNA · 2024Article
- Article
- CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.GigaScience · 2024Article
- Localization is the key to action: regulatory peculiarities of lncRNAs.Frontiers in genetics · 2024Review
- How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA.Methods in molecular biology (Clifton, N.J.) · 2024Review
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2 authors.
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Abstract
motivationLncRNAs play important roles in various biological processes. Although more than 58 000 human lncRNA genes have been discovered, most known lncRNAs are still poorly characterized. One approach to understanding the functions of lncRNAs is the detection of the interacting RNA target of each lncRNA. Because experimental detections of comprehensive lncRNA-RNA interactions are difficult, computational prediction of lncRNA-RNA interactions is an indispensable technique. However, the high computational costs of existing RNA-RNA interaction prediction tools prevent their application to large-scale lncRNA datasets.
resultsHere, we present 'RIblast', an ultrafast RNA-RNA interaction prediction method based on the seed-and-extension approach. RIblast discovers seed regions using suffix arrays and subsequently extends seed regions based on an RNA secondary structure energy model. Computational experiments indicate that RIblast achieves a level of prediction accuracy similar to those of existing programs, but at speeds over 64 times faster than existing programs. AVAILABILITY AND IMPLEMENTATION: The source code of RIblast is freely available at https://github.com/fukunagatsu/RIblast . CONTACT: t.fukunaga@kurenai.waseda.jp or mhamada@waseda.jp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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