RNA secondary structure prediction from multi-aligned sequences

TL;DR

This paper reviews methods for predicting conserved RNA secondary structures from aligned sequences, emphasizing the use of maximum expected gain estimators to classify and understand existing tools.

Contribution

It provides a systematic classification of RNA secondary structure prediction tools based on their underlying information and estimation methods.

Findings

Classifies tools using maximum expected gain estimators

Provides a unified framework for understanding prediction algorithms

Aids users in selecting appropriate tools for RNA structure prediction

Abstract

It has been well accepted that the RNA secondary structures of most functional non-coding RNAs (ncRNAs) are closely related to their functions and are conserved during evolution. Hence, prediction of conserved secondary structures from evolutionarily related sequences is one important task in RNA bioinformatics; the methods are useful not only to further functional analyses of ncRNAs but also to improve the accuracy of secondary structure predictions and to find novel functional RNAs from the genome. In this review, I focus on common secondary structure prediction from a given aligned RNA sequence, in which one secondary structure whose length is equal to that of the input alignment is predicted. I systematically review and classify existing tools and algorithms for the problem, by utilizing the information employed in the tools and by adopting a unified viewpoint based on maximum expected gain (MEG) estimators. I believe that this classification will allow a deeper understanding of each tool and provide users with useful information for selecting tools for common secondary structure predictions.