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Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network.

Anil Kumar Hanumanthappa1, Jaswinder Singh1, Kuldip Paliwal1

  • 1Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia.

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Summary

RNAsnap2 accurately predicts RNA solvent accessibility, outperforming existing tools. This new method aids in understanding RNA structure and function for millions of transcripts with unknown characteristics.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • RNA solvent accessibility is crucial for understanding RNA structure and function.
  • Existing tools for predicting RNA solvent accessibility are limited in accuracy and availability.
  • Millions of RNA transcripts lack determined structures and functions, highlighting the need for improved prediction methods.

Purpose of the Study:

  • To develop a novel, accurate tool for predicting RNA solvent accessibility.
  • To improve upon the performance of existing RNA solvent accessibility predictors.
  • To provide a resource for characterizing the structural and functional regions of RNAs.

Main Methods:

  • Development of RNAsnap2, a deep learning model utilizing a dilated convolutional neural network.
  • Incorporation of a novel feature based on predicted base-pairing probabilities from LinearPartition.
  • Evaluation using standard metrics like Pearson Correlation Coefficient (PCC) and mean absolute errors.

Main Results:

  • RNAsnap2 demonstrated an 11% improvement in median PCC and a 9% improvement in mean absolute errors compared to the predictor RNAsol on a test set of 45 RNA chains.
  • A significant 22% improvement in median PCC was observed on a newly deposited, independent set of 31 RNA chains.
  • A single-sequence version, RNAsnap2 (SingleSeq), achieved performance comparable to profile-based methods.

Conclusions:

  • RNAsnap2 offers a substantial advancement in predicting RNA solvent accessibility.
  • The tool is effective for both protein-bound and protein-free RNAs.
  • RNAsnap2 and its single-sequence variant are valuable for identifying structural signatures and functional regions in non-coding RNAs.