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Computational analysis of RNA structures with chemical probing data.

ABSTRACT: RNAs play various roles, not only as the genetic codes to synthesize proteins, but also as the direct participants of biological functions determined by their underlying high-order structures. Although many computational methods have been proposed for analyzing RNA structures, their accuracy and efficiency are limited, especially when applied to the large RNAs and the genome-wide data sets. Recently, advances in parallel sequencing and high-throughput chemical probing technologies have prompted the development of numerous new algorithms, which can incorporate the auxiliary structural information obtained from those experiments. Their potential has been revealed by the secondary structure prediction of ribosomal RNAs and the genome-wide ncRNA function annotation. In this review, the existing probing-directed computational methods for RNA secondary and tertiary structure analysis are discussed.

SUBMITTER: Ge P

PROVIDER: S-EPMC4437859 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Computational analysis of RNA structures with chemical probing data.

Ge Ping P Zhang Shaojie S

Methods (San Diego, Calif.) 20150214

RNAs play various roles, not only as the genetic codes to synthesize proteins, but also as the direct participants of biological functions determined by their underlying high-order structures. Although many computational methods have been proposed for analyzing RNA structures, their accuracy and efficiency are limited, especially when applied to the large RNAs and the genome-wide data sets. Recently, advances in parallel sequencing and high-throughput chemical probing technologies have prompted ...[more]

PMID: 25687190

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Project description:MotivationRNA molecules can undergo complex structural dynamics, especially during transcription, which influence their biological functions. Recently developed high-throughput chemical probing experiments that study RNA cotranscriptional folding generate nucleotide-resolution 'reactivities' for each length of a growing nascent RNA that reflect structural dynamics. However, the manual annotation and qualitative interpretation of reactivity across these large datasets can be nuanced, laborious, and difficult for new practitioners. We developed a quantitative and systematic approach to automatically detect RNA folding events from these datasets to reduce human bias/error, standardize event discovery and generate hypotheses about RNA folding trajectories for further analysis and experimental validation.ResultsDetection of Unknown Events with Tunable Thresholds (DUETT) identifies RNA structural transitions in cotranscriptional RNA chemical probing datasets. DUETT employs a feedback control-inspired method and a linear regression approach and relies on interpretable and independently tunable parameter thresholds to match qualitative user expectations with quantitatively identified folding events. We validate the approach by identifying known RNA structural transitions within the cotranscriptional folding pathways of the Escherichia coli signal recognition particle RNA and the Bacillus cereus crcB fluoride riboswitch. We identify previously overlooked features of these datasets such as heightened reactivity patterns in the signal recognition particle RNA about 12 nt lengths before base-pair rearrangement. We then apply a sensitivity analysis to identify tradeoffs when choosing parameter thresholds. Finally, we show that DUETT is tunable across a wide range of contexts, enabling flexible application to study broad classes of RNA folding mechanisms.Availability and implementationhttps://github.com/BagheriLab/DUETT.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Computational analysis of RNA structures with chemical probing data.

Publications

Computational analysis of RNA structures with chemical probing data.

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