Incorporating phylogenetic-based covarying mutations into RNAalifold for RNA consensus structure prediction

Abstract

Background: 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. 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.

Results: In 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.

Conclusion: Incorporating 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.

Figure 1

Covarying mutations in an RNA alignment. (a) A multiple RNA alignment and its phylogenetic tree. Three pairs of columns, which are marked with different colors, are analyzed in the following three sub-figures. (b) Possible covarying mutations in the red columns. In this case, only one pair-wise mutation is required at the root node. (c) Possible covarying mutations in the green columns. At least two pair-wise mutations occur at the internal nodes in this case; (d) Possible covarying mutations in the blue columns. There are non-pairing bases, ‘AG’ and ‘A-’. The label inference of the internal nodes does not depend on them. So in this case, the number of mutations is one.

Figure 2

MCC on the CMfinder dataset as a function of the β parameter. The MCC results of PhyloRNAalifold, with and without RIBOSUM matrix support, are shown in this figure. The dash lines are references for the curves, which show the performance of RNAalifold on the same dataset. It can be seen that except for β=1, the new phylogenetic-based covariance scoring scheme improves the performance of RNAalifold.

Figure 3

The effect of alignment pair-wise identity and sequence number on the structural prediction of PhyloRNAalifold (β=10). The MCC results of PhyloRNAalifold and RNAalifold on the third benchmarking dataset are shown in this figure. It can be seen that the performance difference between PhyloRNAalifold and RNAalifold increases with the increasing of the sequence number in the alignments. In addition, the maximum MCC difference is achieved in the range of 65∼75 identities.