LinearTurboFold: Linear-time Global Prediction of Conserved Structures for RNA Homologs with Applications to SARS-CoV-2.

Significance Conserved RNA structures are critical for designing diagnostic and therapeutic tools for many diseases including COVID-19. However, existing algorithms are much too slow to model the global structures of full-length RNA viral genomes. We present LinearTurboFold, a linear-time algorithm that is orders of magnitude faster, making it, to our knowledge, the first method to simultaneously fold and align whole genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, the longest known RNA virus (∼30 kb). Our work enables unprecedented global structural analysis and captures long-range interactions that are out of reach for existing algorithms but crucial for RNA functions. LinearTurboFold is a general technique for full-length genome studies and can help fight the current and future pandemics.