Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.

ABSTRACT: RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their

SUBMITTER: Li H 

PROVIDER: S-EPMC5807309 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature