RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

Jaswinder Singh, Jack Hanson, Kuldip Paliwal () and Yaoqi Zhou ()
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Jaswinder Singh: Griffith University
Jack Hanson: Griffith University
Kuldip Paliwal: Griffith University
Yaoqi Zhou: Griffith University

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: Abstract The majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only $$ $$>10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.

Date: 2019
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DOI: 10.1038/s41467-019-13395-9

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