ArticleBioinformatics (Oxford, England)2019
Integration of accessibility data from structure probing into RNA-RNA interaction prediction.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Recent Advances in Chemical Probing Strategies for RNA Structure Determination In Vivo.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA.Methods in molecular biology (Clifton, N.J.) · 2024Review
- tRFtarget: a database for transfer RNA-derived fragment targets.Nucleic acids research · 2021Article
- CopomuS-Ranking Compensatory Mutations to Guide RNA-RNA Interaction Verification Experiments.International journal of molecular sciences · 2020Article
- The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment.BMC bioinformatics · 2020Article
- GraphClust2: Annotation and discovery of structured RNAs with scalable and accessible integrative clustering.GigaScience · 2019Article
- ShaKer: RNA SHAPE prediction using graph kernel.Bioinformatics (Oxford, England) · 2019Article
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Authors and funding
4 authors.
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Abstract
summaryExperimental structure probing data has been shown to improve thermodynamics-based RNA secondary structure prediction. To this end, chemical reactivity information (as provided e.g. by SHAPE) is incorporated, which encodes whether or not individual nucleotides are involved in intra-molecular structure. Since inter-molecular RNA-RNA interactions are often confined to unpaired RNA regions, SHAPE data is even more promising to improve interaction prediction. Here, we show how such experimental data can be incorporated seamlessly into accessibility-based RNA-RNA interaction prediction approaches, as implemented in IntaRNA. This is possible via the computation and use of unpaired probabilities that incorporate the structure probing information. We show that experimental SHAPE data can significantly improve RNA-RNA interaction prediction. We evaluate our approach by investigating interactions of a spliceosomal U1 snRNA transcript with its target splice sites. When SHAPE data is incorporated, known target sites are predicted with increased precision and specificity. AVAILABILITY AND IMPLEMENTATION: https://github.com/BackofenLab/IntaRNA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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