Joint modeling of RNase footprint sequencing profiles for genome-wide inference of RNA structure.

Recent studies have revealed significant roles of RNA structure in almost every step of RNA processing, including transcription, splicing, transport and translation. RNase footprint sequencing (RNase-seq) has emerged to dissect RNA structures at the genome scale. However, it remains challenging to analyze RNase-seq data because of the issues of signal sparsity, variability and correlations among various RNases. We present a probabilistic framework, joint Poisson-gamma mixture (JPGM), for integrative modeling of multiple RNase-seq profiles. Combining JPGM with hidden Markov model allows genome-wide inference of RNA structures. We apply the joint modeling approach for inferring base pairing states on simulated data sets and RNase-seq profiles of the double-strand specific RNase V1 and single-strand specific RNase S1 in yeast. We demonstrate that joint analysis of V1 and S1 profiles outputs interpretable RNA structure states, while approaches that analyze each profile separately do not. The joint modeling approach predicts the structure states of all nucleotides in 3196 transcripts of yeast without compromising accuracy, while the simple thresholding approach misses 43% of the nucleotides. Furthermore, the posterior probabilities outputted by our model are able to resolve the structural ambiguity of ≈300 000 nucleotides with overlapping V1 and S1 cleavage sites. Our model also generates RNA accessibilities, which are associated with three-dimensional conformations.