Abstract
BackgroundRNAalifold, a popular computational method for RNA consensus structure prediction, incorporates covarying mutations into a thermodynamic model to fold the aligned RNA sequences. When quantifying covariance, it evaluates conserved signals of two aligned columns with base-pairing rules. This scoring scheme performs better than some other approaches, such as mutual information. However it ignores the phylogenetic history of the aligned sequences, which is an important criterion to evaluate the level of sequence covariance.ResultsIn this article, in order to improve the accuracy of consensus structure folding, we propose a novel approach named PhyloRNAalifold. It incorporates the number of covarying mutations on the phylogenetic tree of the aligned sequences into the covariance scoring of RNAalifold. The benchmarking results show that the new scoring scheme of PhyloRNAalifold can improve the consensus structure detection of RNAalifold.ConclusionIncorporating additional phylogenetic information of aligned sequences into the covariance scoring of RNAalifold can improve its performance of consensus structures folding. This improvement is correlated with alignment characteristics, such as pair-wise identity and the number of sequences in the alignment.
Highlights
RNAalifold, a popular computational method for RNA consensus structure prediction, incorporates covarying mutations into a thermodynamic model to fold the aligned RNA sequences
The discovery of novel non-coding RNA families expanded our understanding of RNAs, which carry genetic codes for protein synthesis and participate in other functions, especially the regulatory processes, such as localization, replication, translation and degradation [1,2,3,4]
One type of popular algorithms adopts Minimum Free Energy (MFE) model to fold a single RNA sequence, which has been implemented in Mfold [11] and RNAfold [12]
Summary
RNAalifold, a popular computational method for RNA consensus structure prediction, incorporates covarying mutations into a thermodynamic model to fold the aligned RNA sequences. When quantifying covariance, it evaluates conserved signals of two aligned columns with base-pairing rules. MicroRNA (miRNA) is used as a marker to differ normal tissues from tumors [7,8,9]; long non-coding RNA (lncRNA) contributes to human disease etiology [10]. These findings fuel the research of RNA and pose new challenges. What’s more, single sequence folding may not be applied to discover new RNA families even if the predicted structures are correct, because the statistical signals in an RNA secondary structure are not strong enough to distinguish itself from the stable structures folding from random sequences [14,15]
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