LinearAlifold: Linear-Time Consensus Structure Prediction for RNA Alignments

TL;DR

LinearAlifold is a new linear-time algorithm for predicting consensus RNA structures from alignments, significantly faster and more accurate than previous methods like RNAalifold, especially for long viral genomes.

Contribution

It introduces LinearAlifold, a linear-time consensus RNA structure prediction method that outperforms RNAalifold in speed and accuracy for large genomic datasets.

Findings

LinearAlifold is over 36 times faster than RNAalifold on SARS-CoV-2 genomes.

It achieves higher accuracy in structure prediction compared to known structures.

LinearAlifold's predictions correlate well with experimental data on SARS-CoV-2.

Abstract

Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has many applications including viral diagnostics and therapeutics. However, the most commonly used tool for this task, RNAalifold, is prohibitively slow for long sequences, due to a cubic scaling with the sequence length, taking over a day on 400 SARS-CoV-2 and SARS-related genomes (~30,000nt). We present LinearAlifold, a much faster alternative that scales linearly with both the sequence length and the number of sequences, based on our work LinearFold that folds a single RNA in linear time. Our work is orders of magnitude faster than RNAalifold (0.7 hours on the above 400 genomes, or ~36$\times$ speedup) and achieves higher accuracies when compared to a database of known structures. More interestingly, LinearAlifold's prediction on SARS-CoV-2 correlates well with experimentally determined structures, substantially outperforming RNAalifold. Finally, LinearAlifold supports two energy models (Vienna and BL*) and four modes: minimum free energy (MFE), maximum expected accuracy (MEA), ThreshKnot, and stochastic sampling, each of which takes under an hour for hundreds of SARS-CoV variants. Our resource is at: https://github.com/LinearFold/LinearAlifold (code) and http://linearfold.org/linear-alifold (server).