Improving RNA secondary structure prediction via state inference with deep recurrent neural networks

Devin Willmott, David Murrugarra, Qiang Ye

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by different machine learning techniques. Successful state inference of RNA sequences can be used to generate auxiliary information for data-directed RNA secondary structure prediction. Typical tools for state inference, such as hidden Markov models, exhibit poor performance in RNA state inference, owing in part to their inability to recognize nonlocal dependencies. Bidirectional long short-term memory (LSTM) neural networks have emerged as a powerful tool that can model global nonlinear sequence dependencies and have achieved state-of-the-art performances on many different classification problems. This paper presents a practical approach to RNA secondary structure inference centered around a deep learning method for state inference. State predictions from a deep bidirectional LSTM are used to generate synthetic SHAPE data that can be incorporated into RNA secondary structure prediction via the Nearest Neighbor Thermodynamic Model (NNTM). This method produces predicted secondary structures for a diverse test set of 16S ribosomal RNA that are, on average, 25 percentage points more accurate than undirected MFE structures. Accuracy is highly dependent on the success of our state inference method, and investigating the global features of our state predictions reveals that accuracy of both our state inference and structure inference methods are highly dependent on the similarity of pairing patterns of the sequence to the training dataset. Availability of a large training dataset is critical to the success of this approach.

Original languageEnglish
Pages (from-to)36-50
Number of pages15
JournalComputational and Mathematical Biophysics
Issue number1
StatePublished - Jan 1 2020

Bibliographical note

Publisher Copyright:
© 2020 Devin Willmott et al., published by De Gruyter.


  • Deep learning
  • NNTM
  • RNA secondary structure inference
  • RNA state inference
  • Recurrent neural networks
  • SHAPE-directed NNTM

ASJC Scopus subject areas

  • Biophysics
  • Molecular Biology
  • Applied Mathematics
  • Computational Mathematics
  • Mathematical Physics


Dive into the research topics of 'Improving RNA secondary structure prediction via state inference with deep recurrent neural networks'. Together they form a unique fingerprint.

Cite this