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Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.

Fuyi Li1,2, Yue Bi3, Xudong Guo1

  • 1College of Information Engineering, Northwest A&F University, Yangling 712100, China.

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This study introduces Allocator, a deep learning tool that predicts messenger RNA (mRNA) multi-localization by integrating sequence and structure data. Allocator improves upon existing methods for understanding gene regulation.

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Area of Science:

  • Computational Biology
  • Molecular Biology
  • Bioinformatics

Background:

  • Asymmetrical mRNA distribution is crucial for protein synthesis and cellular processes in human cells.
  • Accurate identification of subcellular mRNA localization is essential for understanding gene regulatory networks.
  • Current computational tools often neglect multi-localization and RNA secondary structure influences.

Purpose of the Study:

  • To develop a computational framework for predicting mRNA multi-localization.
  • To integrate both RNA sequence and structure information for enhanced prediction accuracy.
  • To address limitations in existing methods regarding multi-localization and structural influences.

Main Methods:

  • Proposed Allocator, a multi-view parallel deep learning framework.
  • Integrated four feature extractors: two for sequence (MLP, multi-head self-attention) and two for structure (GNNs).
  • Enabled seamless integration of RNA sequence-level and structure-level information.

Main Results:

  • Allocator demonstrated superior performance compared to state-of-the-art methods in mRNA multi-localization prediction.
  • The framework effectively utilizes both sequence and structural RNA features.
  • Highlighted the importance of RNA secondary structure in localization predictions.

Conclusions:

  • Allocator provides an advanced computational tool for predicting mRNA multi-localization.
  • The integration of sequence and structure data significantly enhances prediction accuracy.
  • Findings contribute to a deeper understanding of gene regulatory networks and cellular organization.