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RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning
<span title="2019-11-27">2019</span>
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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/a4wan6l5o5dfzn767kyz7jqevi" style="color: black;">Nature Communications</a>
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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
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41467-019-13395-9">doi:10.1038/s41467-019-13395-9</a>
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... tertiary interactions. Since only [Formula: see text]250 nonredundant, high-resolution RNA structures are available for model training, we utilize transfer learning from a model initially trained with a recent high-quality bpRNA dataset of [Formula: see text]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.
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