Bioinformatics Tools for Analyzing Big Data in Genomics Research


  • Devid Alex, Yuan Jirelion Salford Business School, The University of Salford, Manchester


Bioinformatics, genomics, big data, next-generation sequencing, machine learning


Abstract: The advent of next-generation sequencing technologies has revolutionized genomics research, generating vast amounts of data that require sophisticated bioinformatics tools for effective analysis. This abstract reviews key bioinformatics tools utilized for analyzing big data in genomics, focusing on their applications, functionalities, and impact on scientific discoveries. Tools such as the Genome Analysis Toolkit (GATK), BWA, and SAMtools are essential for variant calling, alignment, and sequence data processing. High-throughput platforms like Galaxy and Bioconductor provide accessible interfaces for integrating diverse datasets and performing complex analyses. Machine learning algorithms and deep learning frameworks, such as TensorFlow and Scikit-learn, are increasingly employed to identify patterns and make predictions from genomic data. Additionally, cloud-based solutions like Google Genomics and Amazon Web Services (AWS) offer scalable resources to handle the computational demands of large-scale genomic studies. The integration of these bioinformatics tools facilitates the extraction of meaningful insights from genomic data, advancing our understanding of genetic variations, disease mechanisms, and therapeutic targets. As genomics research continues to evolve, the development and refinement of bioinformatics tools will be crucial for managing big data and translating findings into clinical applications. This review highlights the pivotal role of bioinformatics in the era of big data genomics.