TY - JOUR T1 - RSQ: a statistical method for quantification of isoform-specific structurome using transcriptome-wide structural profiling data JF - bioRxiv DO - 10.1101/043232 SP - 043232 AU - Yunfei Wang AU - Xiaopeng Zhu AU - Ming Sun AU - Yong Chen AU - Yiwen Chen AU - Shikui Tu AU - Boyang Bai AU - Min Chen AU - Qi Dai AU - Haozhe Wang AU - Michael Q. Zhang AU - Zhenyu Xuan Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/06/18/043232.abstract N2 - 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. ER -