Computational Approaches for RNA Structure Ensemble Deconvolution from Structure Probing Data. Issue 18 (30th September 2022)

• Record Type: Journal Article
• Authors: Aviran, Sharon; Incarnato, Danny
• Is Part Of: Journal of molecular biology. Volume 434:Issue 18(2022)
• Publication Date: 2022-09-30
• Subjects: thermodynamics -- partition function -- clustering -- SHAPE -- RNA structure ensemble; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2022.167635
• Languages: English
• ISSNs: 0022-2836

Abstract

Graphical abstract: Highlights: RNA molecules populate a dynamic ensemble of alternative structures. RNA structure probing data provide an ensemble-averaged snapshot of structural signatures. Ensemble deconvolution methods can be classified as thermodynamics-dependent or independent. The first class uses thermodynamics to identify a set of folds most consistent with the data. The second class directly clusters sequencing reads into structural subpopulations. Abstract: RNA structure probing experiments have emerged over the last decade as a straightforward way to determine the structure of RNA molecules in a number of different contexts. Although powerful, the ability of RNA to dynamically interconvert between, and to simultaneously populate, alternative structural configurations, poses a nontrivial challenge to the interpretation of data derived from these experiments. Recent efforts aimed at developing computational methods for the reconstruction of coexisting alternative RNA conformations from structure probing data are paving the way to the study of RNA structure ensembles, even in the context of living cells. In this review, we critically discuss these methods, their limitations and possible future improvements.