University of Massachusetts Medical School Faculty Publications

UMMS Affiliation

Program in Bioinformatics and Integrative Biology

Publication Date

2016-06-18

Document Type

Article Preprint

Disciplines

Amino Acids, Peptides, and Proteins | Biochemistry, Biophysics, and Structural Biology | Bioinformatics | Computational Biology | Nucleic Acids, Nucleotides, and Nucleosides | Statistics and Probability

Abstract

The structure of RNA, which is considered to be a second layer of information alongside the genetic code, provides fundamental insights into the cellular function of both coding and non-coding RNAs. Several high-throughput technologies have been developed to profile transcriptome-wide RNA structures, i.e., the structurome. However, it is challenging to interpret the profiling data because the observed data represent an average over different RNA conformations and isoforms with different abundance. To address this challenge, we developed an RNA structurome quantification method (RSQ) to statistically model the distribution of reads over both isoforms and RNA conformations, and thus provide accurate quantification of the isoform-specific structurome. The quantified RNA structurome enables the comparison of isoform-specific conformations between different conditions, the exploration of RNA conformation variation affected by single nucleotide polymorphism (SNP) , and the measurement of RNA accessibility for binding of either small RNAs in RNAi-based assays or RNA binding protein in transcriptional regulation. The model used in our method sheds new light on the potential impact of the RNA structurome on gene regulation.

Keywords

bioinformatics, RNA, gene regulation, RNA structurome quantification method, RSQ, isoforms, RNA conformations, statistical model

Rights and Permissions

The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.

DOI of Published Version

10.1101/043232

Source

bioRxiv 043232; doi: https://doi.org/10.1101/043232. Link to preprint on bioRxiv service.

Journal/Book/Conference Title

bioRxiv

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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