ConsAlifold: considering RNA structural alignments improves prediction accuracy of RNA consensus secondary structures

Abstract

Motivation: By detecting homology among RNAs, the probabilistic consideration of RNA structural alignments has improved the prediction accuracy of significant RNA prediction problems. Predicting an RNA consensus secondary structure from an RNA sequence alignment is a fundamental research objective because in the detection of conserved base-pairings among RNA homologs, predicting an RNA consensus secondary structure is more convenient than predicting an RNA structural alignment.

Results: We developed and implemented ConsAlifold, a dynamic programming-based method that predicts the consensus secondary structure of an RNA sequence alignment. ConsAlifold considers RNA structural alignments. ConsAlifold achieves moderate running time and the best prediction accuracy of RNA consensus secondary structures among available prediction methods.

Availability and implementation: ConsAlifold, data and Python scripts for generating both figures and tables are freely available at https://github.com/heartsh/consalifold.

Supplementary information: Supplementary data are available at Bioinformatics online.