RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy (MFE) methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classic partition function algorithm scales cubically with sequence length, and is therefore a slow calculation for long sequences. This slowness is even more severe than cubic-time MFE-based methods due to a larger constant factor in runtime. Inspired by the success of  LinearFold algorithm that computes the MFE structure in linear time, we address this issue by proposing a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base pairing probabilities. LinearPartition is 256x faster than Vienna RNAfold for a sequence with length 15,780, and 2,771x faster than CONTRAfold for a sequence with length 32, 753. Interestingly, although LinearPartition is approximate, it runs in linear time without sacrificing accuracy when base pair probabilities are used to assemble structures , and even leads to a small accuracy improvement on longer families (16S and 23S rRNA  ).
RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy (MFE) methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore a slow calculation for long sequences. This slowness is even more severe than cubic-time MFE-based methods due to a larger constant factor in runtime. Inspired by the success of our recently proposed LinearFold algorithm that computes the MFE structure in linear time, we propose a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base pairing probabilities. LinearPartition is 256x faster than Vienna RNAfold for a sequence with length 15,780, and 2,771x faster than CONTRAfold for a sequence with length 32, 753 (2.5 days vs. 1.3 minutes). Interestingly, although LinearPartition is approximate, the resulting base pairing probabilities are better correlated with the ground truth structures and lead to a small accuracy improvement on families with the longest length sequences (16S and 23S rRNA ) when used for downstream structure prediction, as well as a substantial improvement on long-distance base pairs (500+ nt apart ).
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it runs in linear time without sacrificing accuracy when base pair probabilities are used to assemble structures , and even leads
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) when used for downstream structure prediction, as well as a substantial improvement on long-distance base pairs (500+ nt apart