Leveraging Big Data for Precision Medicine Through Infrastructure as-a Service(Iaas): A Case Study in Genomic Data Analysis

Advancements in precision medicine heavily rely on the analysis of large-scale genomic datasets. However, the increasing size and complexity of genomic data pose significant challenges in terms of resource allocation, scalability and cost-effectiveness. This study addressed the challenges involved in analyzing such datasets for precision medicine and evaluated the feasibility and effectiveness of using Cloud Genomics Infrastructure-as-a-Service (GMaaS) platforms. A dataset consisting of 100 samples with an average size of 50 GB is utilized as a case study. The proposed GMaaS model provides scalable and cost-effective infrastructure provisioning by optimizing the utilization of resources. The results demonstrated improved execution times for each stage of the genomic data analysis pipeline when adopting IaaS. On average data preprocessing, variant calling, functional annotation and downstream analysis take 20, 30, 25 and 40 minutes, respectively. The cost analysis highlighted the cost-effectiveness of GCMaaS which enablies efficient resource allocation. The scalability and flexibility of GCMaaS support collaborative precision medicine research. Cloud-based infrastructure particularly GCMaaS is essential for genomic data analysis in precision medicine.