همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

BiogenExplorer: A Robust Solution for Rapid Gene Presence Detection and Allelic Diversity Analysis in large Genomic Datasets

نویسندگان :

Mohammadreza Najafi Disfani¹ Danial Ghofrani² Mahdiar Mansouri³ Parastoo Saniee⁴

1- Faculty of Life Sciences and biotechnology, Shahid Beheshti University, Tehran, Iran 2- Faculty ofLife Sciences and biotechnology, Shahid Beheshti University, Tehran, Iran 3- Faculty of Life Sciences and biotechnology, Shahid Beheshti University, Tehran, Iran 4- Faculty of Life Sciences and biotechnology, Shahid Beheshti University, Tehran, Iran

کلمات کلیدی :

Gene presence،Allelic diversity،Bioinformatics automation،BLAST

چکیده :

Introduction: In the era of genomic research, understanding genetic variability is pivotal for advancing health diagnostics, disease prevention, and biotechnological innovations. The analysis of gene presence-absence and allelic diversity provides critical insights into evolutionary dynamics, population genetics, and the functional potential of organisms. However, the management and analysis of large genomic datasets can be daunting, often hampered by inefficiencies and a high likelihood of human error. To address these challenges, we introduce BiogenExplorer—a powerful and user-friendly tool designed to streamline the analysis of gene presence across complete genomes. With its robust integration of BLAST+, SQL, and Python, BiogenExplorer empowers researchers to efficiently analyze extensive datasets while ensuring high accuracy and speed. Methods: BiogenExplorer employs BLAST+ for precise sequence alignment, SQL for optimized data storage and querying, and Python for automation of workflows. Genomic sequences are systematically processed to identify the presence of specific genes across multiple samples. After establishing gene presence, the tool assesses allelic diversity by analyzing sequence variation within the identified genes. The system is engineered to manage large datasets while minimizing human intervention, facilitating rapid analysis, even with extensive sample cohorts. Results: BiogenExplorer was evaluated using various genomic datasets encompassing a range of sample sizes. The tool exhibited exceptional accuracy in detecting gene presence and delivering reliable assessments of allelic diversity. Its enhanced performance demonstrated notable advancements in speed and scalability when compared to traditional analytical methods, particularly beneficial for large sample numbers. Conclusion: BiogenExplorer emerges as a fast, reliable, and scalable solution for analyzing gene presence and allelic diversity in genomic studies. Its ability to efficiently manage large datasets provides profound value in health-related research and industrial applications alike. By automating the analysis process, BiogenExplorer significantly reduces human error and enhances throughput, establishing itself as an invaluable resource for large-scale genomic analysis.

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