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The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment.

ABSTRACT: BACKGROUND:Seed 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. RESULTS:Here, 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. CONCLUSIONS:This 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.

SUBMITTER: Raden M

PROVIDER: S-EPMC6956497 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment.

Raden Martin M Müller Teresa T Mautner Stefan S Gelhausen Rick R Backofen Rolf R

BMC bioinformatics 20200113 1

<h4>Background</h4>Seed 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.<h4>Results</h4>Here, we present a systematic assessment of the impact of established and new constraints on sRNA target predi ...[more]

PMID: 31931703

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Project description:BACKGROUND: sRNAs, which belong to the non-coding RNA family and range from approximately 50 to 400 nucleotides, serve various important gene regulatory roles. Most are believed to be trans-regulating and function by being complementary to their target mRNAs in order to inhibiting translation by ribosome occlusion. Despite this understanding of their functionality, the global properties associated with regulation by sRNAs are not yet understood. Here we use topological analysis of sRNA targets in terms of protein-protein interaction and transcription-regulatory networks in Escherichia coli to shed light on the global correlation between sRNA regulation and cellular control networks. RESULTS: The analysis of sRNA targets in terms of their networks showed that some specific network properties could be identified. In protein-protein interaction network, sRNA targets tend to occupy more central positions (higher closeness centrality, p-val = 0.022) and more cliquish (larger clustering coefficient, p-val = 0.037). The targets of the same sRNA tend to form a network module (shorter characteristic path length, p-val = 0.015; larger density, p-val = 0.019; higher in-degree ratio, p-val = 0.009). Using the transcription-regulatory network, sRNA targets tend to be under multiple regulation (higher indegree, p-val = 0.013) and the targets usually are important to the transfer of regulatory signals (higher betweenness, p-val = 0.012). As was found for the protein-protein interaction network, the targets that are regulated by the same sRNA also tend to be closely knit within the transcription-regulatory network (larger density, p-val = 0.036), and inward interactions between them are greater than the outward interactions (higher in-degree ratio, p-val = 0.023). However, after incorporating information on predicted sRNAs and down-stream targets, the results are not as clear-cut, but the overall network modularity is still evident. CONCLUSIONS: Our results indicate that sRNA targeting tends to show a clustering pattern that is similar to the human microRNA regulation associated with protein-protein interaction network that was observed in a previous study. Namely, the sRNA targets show close interaction and forms a closely knit network module for both the protein-protein interaction and the transcription-regulatory networks. Thus, targets of the same sRNA work in a concerted way toward a specific goal. In addition, in the transcription-regulatory network, sRNA targets act as "multiplexor", accepting regulatory control from multiple sources and acting accordingly. Our results indicate that sRNA targeting shows different properties when compared to the proteins that form cellular networks.

Dataset Information

The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment.

Publications

The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment.

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