Linear-Time Algorithms for RNA Structure Prediction.

RNA secondary structure prediction is widely used to understand RNA function. Existing dynamic programming-based algorithms, both the classical minimum free energy (MFE) methods and partition function methods, suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Inspired by incremental parsing for context-free grammars in computational linguistics, we designed linear-time heuristic algorithms, LinearFold and LinearPartition, to approximate the MFE structure, partition function and base pairing probabilities. These programs are orders of magnitude faster than Vienna RNAfold and CONTRAfold on long sequences. More interestingly, LinearFold and LinearPartition lead to more accurate predictions on the longest sequence families for which the structures are well established (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500 +  nucleotides apart). This chapter provides protocols for using LinearFold and LinearPartition for secondary structure prediction. © 2023. Springer Science+Business Media, LLC, part of Springer Nature.

Citation

He Zhang, Liang Zhang, Kaibo Liu, Sizhen Li, David H Mathews, Liang Huang. Linear-Time Algorithms for RNA Structure Prediction. Methods in molecular biology (Clifton, N.J.). 2023;2586:15-34

Mesh Tags

Substances

PMID: 36705896

search → result