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

The precise regulation of DNA replication timing (RT) relies on deciphering sequence patterns. Although significant advances have been made in identifying sequence patterns associated with replication timing, there are still few computational pipelines designed for accurate RT prediction. In this study, we propose a deep learning-based framework, named RT-Predictor, leveraging a residual network (ResNet) to classify sequence patterns associated with RT across the human genome into four distinct domains: early replication domain (ERD), down transition zone (DTZ), late replication domain (LRD), and up transition zone (UTZ). Using solely DNA sequence patterns, the model achieves an accuracy of 74.58%, a Matthews correlation coefficient (MCC) of 0.6612, an F1-score of 0.7458, and a Recall of 0.7457, demonstrating its ability to resolve complex DNA replication timing patterns. By incorporating positional and frequency-based features derived from DNA sequences, we extract a comprehensive set of 384 features that effectively characterize replication dynamics. Genome-wide RT prediction reveals that replication origins (ORIs) predominantly initiate replication during the early S-phase, potentially linking specific sequence patterns to DNA damage repair mechanisms. These findings demonstrate the power of deep learning in decoding the regulatory significance of sequence patterns in replication timing and provide critical insights into the molecular basis of genomic stability and its disruption in diseases, particularly cancer.