LinearCoFold and LinearCoPartition: linear-time algorithms for secondary structure prediction of interacting RNA molecules

• Published in: Nucleic acids research Vol. 51; no. 18; p. e94
• Main Authors: Zhang, He, Li, Sizhen, Dai, Ning, Zhang, Liang, Mathews, David H, Huang, Liang
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 13.10.2023
• Subjects: Algorithms; Base Pairing; Genomics; Humans; Methods; Nucleic Acid Conformation; RNA - chemistry; RNA - metabolism; RNA, Viral - chemistry; SARS-CoV-2 - chemistry
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkad664

Abstract Many RNAs function through RNA–RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA–RNA interaction is useful, however, existing tools are either too simplistic or too slow. To address this issue, we present LinearCoFold, which approximates the complete minimum free energy structure of two strands in linear time, and LinearCoPartition, which approximates the cofolding partition function and base pairing probabilities in linear time. LinearCoFold and LinearCoPartition are orders of magnitude faster than RNAcofold. For example, on a sequence pair with combined length of 26,190 nt, LinearCoFold is 86.8× faster than RNAcofold MFE mode, and LinearCoPartition is 642.3× faster than RNAcofold partition function mode. Surprisingly, LinearCoFold and LinearCoPartition’s predictions have higher PPV and sensitivity of intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the RNA–RNA interaction between SARS-CoV-2 genomic RNA (gRNA) and human U4 small nuclear RNA (snRNA), which has been experimentally studied, and observe that LinearCoFold’s prediction correlates better with the wet lab results than RNAcofold’s. Graphical Abstract Graphical Abstract