Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets.

ABSTRACT: The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g. transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin de

SUBMITTER: Radecki P 

PROVIDER: S-EPMC8384053 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature