ArticleBMC bioinformatics2020
The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 9 citations in OpenAlex.
- CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.GigaScience · 2024Article
- Posttranscriptional Regulation in Response to Different Environmental Stresses in Campylobacter jejuni.Microbiology spectrum · 2022Article
- sRNARFTarget: a fast machine-learning-based approach for transcriptome-wide sRNA target prediction.RNA biology · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeed and accessibility constraints are core features to enable highly accurate sRNA target screens based on RNA-RNA interaction prediction. Currently, available tools provide different (sets of) constraints and default parameter sets. Thus, it is hard to impossible for users to estimate the influence of individual restrictions on the prediction results.
resultsHere, we present a systematic assessment of the impact of established and new constraints on sRNA target prediction both on a qualitative as well as computational level. This is done exemplarily based on the performance of IntaRNA, one of the most exact sRNA target prediction tools. IntaRNA provides various ways to constrain considered seed interactions, e.g. based on seed length, its accessibility, minimal unpaired probabilities, or energy thresholds, beside analogous constraints for the overall interaction. Thus, our results reveal the impact of individual constraints and their combinations.
conclusionsThis provides both a guide for users what is important and recommendations for existing and upcoming sRNA target prediction approaches.We show on a large sRNA target screen benchmark data set that only by altering the parameter set, IntaRNA recovers 30% more verified interactions while becoming 5-times faster. This exemplifies the potential of seed, accessibility and interaction constraints for sRNA target prediction.
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