SARE: an optimized method for detection functional regulatory elements in genome

Abstract Background Metaheuristic algorithms have been widely employed to solve optimization problems, but their application in analyzing health-related data remains limited. This is primarily due to the complexity of health data, particularly genomic and epigenetic datasets. In genomics, a critical challenge is identifying genomic factors that collaboratively influence disease-associated genes. Enhancer–promoter interactions play a key role in gene regulation, and accurately predicting these elements is essential for developing tailored therapeutic strategies. Despite advances in computational tools, limitations such as fixed-fragment approaches and computational inefficiency hinder the detection of biologically relevant interactions. Materials and methods His study introduces the simulation annealing regulatory element (SARE) method, a novel approach based on the simulated annealing algorithm, to identify enhancer–promoter-like interactions from Hi-C (chromosome conformation capture) datasets. The SARE method addresses the limitations of traditional fixed-fragment approaches by detecting variable-length regulatory elements. The Hi-C dataset utilized in this research was derived from mouse embryonic stem cells (ESCs) and processed using advanced filtering techniques to retain valid interactions. A sensitivity analysis was performed to optimize critical parameters, such as the cooling schedule and initial configurations, ensuring robust performance across datasets. Results The proposed method was benchmarked against traditional techniques, including HiCUP and HiC-Pro, using statistical metrics such as precision (0.85), recall (0.78), and F1-score (0.81). SARE demonstrated superior performance, identifying a significantly higher number of interactions with increased biological relevance. Approximately 70% of the detected interactions overlapped with known enhancer–promoter pairs, while the remaining 30% potentially represent novel regulatory mechanisms. Computational efficiency analysis revealed that SARE reduced runtime and memory usage compared to traditional methods, making it suitable for high-throughput applications. Conclusion The SARE method represents a significant advancement in genomic interaction analysis, offering enhanced sensitivity, efficiency, and biological relevance. By addressing the limitations of traditional tools and identifying both known and novel regulatory elements, SARE provides valuable insights into the mechanisms of gene regulation and chromatin organization. Future studies should focus on expanding the application of SARE to diverse organisms, tissues, and cell types, as well as integrating complementary datasets such as chromatin accessibility and histone modification maps to further validate its findings. Additionally, benchmarking against machine learning-based approaches will establish its position as a robust tool in genomic research.