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Many ncRNAs function through RNA-RNA interactions. Fast and reliable RNAstructure prediction with consideration of RNA-RNA interaction is useful. Someexisting tools are less accurate due to omitting the competing ofintermolecular and intramolecular base pairs, or focus more on predicting thebinding region rather than predicting the complete secondary structure of twointeracting strands. Vienna RNAcofold, which reduces the problem into theclassical single sequence folding by concatenating two strands, scales in cubictime against the combined sequence length, and is slow for long sequences. Toaddress these issues, we present LinearCoFold, which predicts the completeminimum free energy structure of two strands in linear runtime, andLinearCoPartition, which calculates the cofolding partition function and basepairing probabilities in linear runtime. LinearCoFold and LinearCoPartitionfollows the concatenation strategy of RNAcofold, but are orders of magnitudefaster than RNAcofold. For example, on a sequence pair with combined length of26,190 nt, LinearCoFold is 86.8x faster than RNAcofold MFE mode (0.6 minutesvs. 52.1 minutes), and LinearCoPartition is 642.3x faster than RNAcofoldpartition function mode (1.8 minutes vs. 1156.2 minutes). Different from thelocal algorithms, LinearCoFold and LinearCoPartition are global cofoldingalgorithms without restriction on base pair length. Surprisingly, LinearCoFoldand LinearCoPartition's predictions have higher PPV and sensitivity ofintermolecular base pairs. Furthermore, we apply LinearCoFold to predict theRNA-RNA interaction between SARS-CoV-2 gRNA and human U4 snRNA, which has beenexperimentally studied, and observe that LinearCoFold's prediction correlatesbetter to the wet lab results.

LinearCoFold and LinearCoPartition: Linear-Time Algorithms for Secondary Structure Prediction of Interacting RNA molecules