Abstract
Motivation The accuracy of RNA secondary and tertiary structure prediction can be significantly improved by using structural restraints derived from evolutionary or direct coupling analysis. Currently, these coupling analyses relied on manually curated multiple sequence alignments collected in the Rfam database, which contains 3016 families. By comparison, millions of non-coding RNA sequences are known. Here, we established RNAcmap, a fully automatic method that enables evolutionary coupling analysis for any RNA sequences. The homology search was based on the covariance model built by Infernal according to two secondary structure predictors: a folding-based algorithm RNAfold and the latest deep-learning method SPOT-RNA.
Results We show that the performance of RNAcmap is less dependent on the specific evolutionary coupling tool but is more dependent on the accuracy of secondary structure predictor with the best performance given by RNAcmap (SPOT-RNA). The performance of RNAcmap (SPOT-RNA) is comparable to that based on Rfam-supplied alignment and consistent for those sequences that are not in Rfam collections. Further improvement can be made with a simple meta predictor RNAcmap (SPOT-RNA/RNAfold) depending on which secondary structure predictor can find more homologous sequences. Reliable base-pairing information generated from RNAcmap, for RNAs with high effective homologous sequences, in particular, will be useful for aiding RNA structure prediction.
Availability and implementation RNAcmap is available as a web server at https://sparks-lab.org/server/rnacmap/) and as a standalone application along with the datasets at https://github.com/sparks-lab-org/RNAcmap.
Competing Interest Statement
The authors have declared no competing interest.
