University of Massachusetts Medical School Faculty Publications
UMMS Affiliation
Program in Bioinformatics and Integrative Biology
Publication Date
6-18-2016
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The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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
Repository Citation
Wang, Yunfei; University of Texas at Dallas; Zhu, Xiaopeng; Sun, Ming; Chen, Yong; Zhang, Michael Q.; University of Texas at Dallas; Chen, Yiwen; Tu, Shikui; Dai, Qi; Wang, Haozhe; and Bai, Boyang, "RSQ: a statistical method for quantification of isoform-specific structurome using transcriptome-wide structural profiling data" (2016). University of Massachusetts Medical School Faculty Publications. 1551.
https://escholarship.umassmed.edu/faculty_pubs/1551
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Amino Acids, Peptides, and Proteins Commons, Biochemistry, Biophysics, and Structural Biology Commons, Bioinformatics Commons, Computational Biology Commons, Nucleic Acids, Nucleotides, and Nucleosides Commons, Statistics and Probability Commons