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RNA Secondary Structure Prediction Using High-throughput SHAPE
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Deep learning framework for RNA 5hmC prediction using RNA language model embeddings.

Md Muhaiminul Islam Nafi1,2

  • 1Department of CSE, BUET, Dhaka, Bangladesh.

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|February 3, 2026
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Summary
This summary is machine-generated.

Predicting RNA 5-Hydroxymethylcytosine (5hmC) modifications is crucial for understanding gene regulation and disease. A new deep learning model, InTrans-RNA5hmC, accurately identifies these epigenetic marks, outperforming existing methods.

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Area of Science:

  • Epigenetics
  • Molecular Biology
  • Computational Biology

Background:

  • Ribonucleic Acid (RNA) 5-Hydroxymethylcytosine (5hmC) modifications influence gene expression and epigenetic regulation.
  • These modifications are implicated in various human diseases like cancer and diabetes.
  • Current experimental methods for identifying RNA 5hmC are expensive and time-consuming, necessitating computational approaches.

Purpose of the Study:

  • To develop an accurate computational model for predicting RNA 5hmC modifications.
  • To compare various feature descriptors and deep learning architectures for this task.
  • To understand the role of neighbourhood analysis in RNA 5hmC modification prediction.

Main Methods:

  • Feature descriptor analysis and selection.
  • Exploration of different deep learning models.
  • Development of a dual-branch deep learning model (InTrans-RNA5hmC) combining Inception and Transformer architectures.
  • Utilizing word embeddings and RiboNucleic Acid Language Model (RiNALMo) embeddings as feature descriptors.

Main Results:

  • The InTrans-RNA5hmC model demonstrated superior performance compared to state-of-the-art methods.
  • Achieved high metrics on an independent test set: 0.97 sensitivity, 0.985 balanced accuracy, and 0.985 F1 score.
  • Neighbourhood analysis provided insights into RNA 5hmC modification patterns.

Conclusions:

  • The proposed InTrans-RNA5hmC model offers an efficient and accurate computational method for predicting RNA 5hmC modifications.
  • This advancement can aid in disease research and biomarker discovery.
  • The study highlights the potential of deep learning in understanding complex epigenetic modifications.