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RSQ: a statistical method for quantification of isoform-specific structurome using transcriptome-wide structural profiling data

By Yunfei Wang, Xiaopeng Zhu, Ming Sun, Yong Chen, Yiwen Chen, Shikui Tu, Boyang Bai, Min Chen, Qi Dai, Haozhe Wang, Michael Q. Zhang, Zhenyu Xuan

Posted 18 Jun 2016
bioRxiv DOI: 10.1101/043232

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.

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