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LinearFold: Linear-Time Prediction of RNA Secondary Structures

Dezhong Deng, Kai Zhao, David Hendrix, David H. Mathews, Liang Huang
doi: https://doi.org/10.1101/263509
Dezhong Deng
aSchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR
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Kai Zhao
bGoogle, Inc., New York, NY
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David Hendrix
cDept. of Biochemistry & Biophysics, Oregon State University, Corvallis, OR
aSchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR
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David H. Mathews
dDept. of Biochemistry & Biophysics, Center for RNA Biology, and Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY
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Liang Huang
aSchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR
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  • For correspondence: liang.huang.sh@gmail.com
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Abstract

Predicting the secondary structure of an RNA sequence with speed and accuracy is useful in many applications such as drug design. The state-of-the-art predictors have a fundamental limitation: they have a run time that scales cubically with the length of the input sequence, which is slow for longer RNAs and limits the use of secondary structure prediction in genome-wide applications. To address this bottleneck, we designed the first linear-time algorithm for this problem. which can be used with both thermodynamic and machine-learned scoring functions. Our algorithm, like previous work, is based on dynamic programming (DP), but with two crucial differences: (a) we incrementally process the sequence in a left-to-right rather than in a bottom-up fashion, and (b) because of this incremental processing, we can further employ beam search pruning to ensure linear run time in practice (with the cost of exact search). Even though our search is approximate, surprisingly, it results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart).

Author contributions: L.H. conceived the idea based on D.H.’s suggestion. L.H., D.D., and K.Z. designed the algorithm. L.H. and D.D. implemented a prototype in Python. D.D. and K.Z. implemented the fast version In C++. D.H.M. and D.H. supervised testing of the algorithm. D.D. carried out testing and plotted figures. D.D., L.H., and D.H.M. wrote the manuscript; D.H. revised it.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 14, 2018.
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LinearFold: Linear-Time Prediction of RNA Secondary Structures
Dezhong Deng, Kai Zhao, David Hendrix, David H. Mathews, Liang Huang
bioRxiv 263509; doi: https://doi.org/10.1101/263509
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LinearFold: Linear-Time Prediction of RNA Secondary Structures
Dezhong Deng, Kai Zhao, David Hendrix, David H. Mathews, Liang Huang
bioRxiv 263509; doi: https://doi.org/10.1101/263509

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