Dataset Information

AliFreeFoldMulti: alignment-free method to predict secondary structures of multiple RNA homologs.

ABSTRACT: Predicting RNA structure is crucial for understanding RNA's mechanism of action. Comparative approaches for the prediction of RNA structures can be classified into four main strategies. The three first-align-and-fold, align-then-fold and fold-then-align-exploit multiple sequence alignments to improve the accuracy of conserved RNA-structure prediction. Align-and-fold methods perform generally better, but are also typically slower than the other alignment-based methods. The fourth strategy-alignment-free-consists in predicting the conserved RNA structure without relying on sequence alignment. This strategy has the advantage of being the faster, while predicting accurate structures through the use of latent representations of the candidate structures for each sequence. This paper presents aliFreeFoldMulti, an extension of the aliFreeFold algorithm. This algorithm predicts a representative secondary structure of multiple RNA homologs by using a vector representation of their suboptimal structures. aliFreeFoldMulti improves on aliFreeFold by additionally computing the conserved structure for each sequence. aliFreeFoldMulti is assessed by comparing its prediction performance and time efficiency with a set of leading RNA-structure prediction methods. aliFreeFoldMulti has the lowest computing times and the highest maximum accuracy scores. It achieves comparable average structure prediction accuracy as other methods, except TurboFoldII which is the best in terms of average accuracy but with the highest computing times. We present aliFreeFoldMulti as an illustration of the potential of alignment-free approaches to provide fast and accurate RNA-structure prediction methods.

SUBMITTER: Bossanyi MA

PROVIDER: S-EPMC7671329 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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aliFreeFoldMulti: alignment-free method to predict secondary structures of multiple RNA homologs.

Bossanyi Marc-André MA Carpentier Valentin V Glouzon Jean-Pierre S JS Ouangraoua Aïda A Anselmetti Yoann Y

NAR genomics and bioinformatics 20201027 4

Predicting RNA structure is crucial for understanding RNA's mechanism of action. Comparative approaches for the prediction of RNA structures can be classified into four main strategies. The three first-align-and-fold, align-then-fold and fold-then-align-exploit multiple sequence alignments to improve the accuracy of conserved RNA-structure prediction. Align-and-fold methods perform generally better, but are also typically slower than the other alignment-based methods. The fourth strategy-alignme ...[more]

PMID: 33575631

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Project description:The diverse landscape of RNA conformational space includes many canyons and crevices that are distant from the lowest minimum free energy valley and remain unexplored by traditional RNA structure prediction methods. A complete description of the entire RNA folding landscape can facilitate identification of biologically important conformations. The Crumple algorithm rapidly enumerates all possible non-pseudoknotted structures for an RNA sequence without consideration of thermodynamics while filtering the output with experimental data. The Crumple algorithm provides an alternative approach to traditional free energy minimization programs for RNA secondary structure prediction. A complete computation of all non-pseudoknotted secondary structures can reveal structures that would not be predicted by methods that sample the RNA folding landscape based on thermodynamic predictions. The free energy minimization approach is often successful but is limited by not considering RNA tertiary and protein interactions and the possibility that kinetics rather than thermodynamics determines the functional RNA fold. Efficient parallel computing and filters based on experimental data make practical the complete enumeration of all non-pseudoknotted structures. Efficient parallel computing for Crumple is implemented in a ring graph approach. Filters for experimental data include constraints from chemical probing of solvent accessibility, enzymatic cleavage of paired or unpaired nucleotides, phylogenetic covariation, and the minimum number and lengths of helices determined from crystallography or cryo-electron microscopy. The minimum number and length of helices has a significant effect on reducing conformational space. Pairing constraints reduce conformational space more than single nucleotide constraints. Examples with Alfalfa Mosaic Virus RNA and Trypanosome brucei guide RNA demonstrate the importance of evaluating all possible structures when pseduoknots, RNA-protein interactions, and metastable structures are important for biological function. Crumple software is freely available at http://adenosine.chem.ou.edu/software.html.

Dataset Information

AliFreeFoldMulti: alignment-free method to predict secondary structures of multiple RNA homologs.

Publications

aliFreeFoldMulti: alignment-free method to predict secondary structures of multiple RNA homologs.

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