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ResNet-Powered Multi-Class Identification of Sequence Patterns for Genome Replication Timing Analysis.

Zhen-Ning Yin1, Yu-Hao Zeng1, Feng Gao2,3,4

  • 1Department of Physics, School of Science, Tianjin University, Tianjin, 300072, China.

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|December 12, 2025
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Summary
This summary is machine-generated.

This study introduces RT-Predictor, a deep learning model that accurately predicts DNA replication timing (RT) domains using only DNA sequence patterns. The framework enhances understanding of genomic stability and its links to diseases like cancer.

Keywords:
Deep learningReplication originReplication timing

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Precise regulation of DNA replication timing (RT) is crucial for genomic stability.
  • Identifying sequence patterns influencing RT is challenging, with limited computational tools for accurate prediction.
  • Understanding RT dynamics is vital for deciphering mechanisms of genomic instability and diseases, including cancer.

Purpose of the Study:

  • To develop a deep learning-based framework, RT-Predictor, for accurate prediction of DNA replication timing domains using sequence patterns.
  • To classify human genome sequences into four distinct replication timing domains: early replication domain (ERD), down transition zone (DTZ), late replication domain (LRD), and up transition zone (UTZ).
  • To investigate the relationship between specific sequence patterns, replication origins (ORIs), and potential links to DNA damage repair mechanisms.

Main Methods:

  • Utilized a deep learning framework, specifically a residual network (ResNet), for sequence pattern classification.
  • Extracted 384 features, including positional and frequency-based characteristics, from DNA sequences to define replication dynamics.
  • Performed genome-wide RT prediction to analyze the distribution and characteristics of replication timing domains.

Main Results:

  • RT-Predictor achieved 74.58% accuracy, 0.6612 MCC, 0.7458 F1-score, and 0.7457 Recall using only DNA sequence patterns.
  • The model successfully resolved complex DNA replication timing patterns across the human genome.
  • Genome-wide analysis indicated that replication origins predominantly initiate replication during early S-phase, suggesting sequence-pattern-based regulation.

Conclusions:

  • Deep learning effectively decodes the regulatory significance of sequence patterns in DNA replication timing.
  • RT-Predictor provides a powerful tool for analyzing replication dynamics and understanding genomic stability.
  • Findings offer critical insights into the molecular basis of genomic stability and its disruption in diseases, particularly cancer.