GPXplore: an intelligent computational framework for precise gene promoter extraction - Godara, Shruti; Godara, Samarth; Begam, Shbana; Singh, Anil Kumar (Frontiers in Bioinformatics, 2026)
Efficient and precise extraction of gene promoter regions is vital for understanding gene regulation, with broad implications in gene editing, functional genomics, and disease research. However, existing tools often fall short in scalability, usability and performance. To address these limitations, we present “GPXplore,” a computational tool designed for the precise and user-friendly extraction of gene promoters from genomic data. It leverages vectorized data processing techniques to significantly reduce data processing time, enhancing speed and efficiency in large-scale promoter extraction tasks. GPXplore retrieves upstream and downstream sequences relative to gene loci and supports customizable parameters, enabling users to define region lengths based on specific research needs. The tool is implemented in Python, features both a command-line and graphical user interface, and is compatible with Windows and Ubuntu platforms. GPXplore was rigorously validated using eight diverse genomic datasets, demonstrating high accuracy and reliability. By combining automation, flexibility, and accessibility, GPXplore provides a robust solution for researchers across varying levels of computational expertise, facilitating high-throughput promoter analysis in modern genomics.