PaaS for Computational Biology

The rapid growth of biological data in bioinformatics presents significant challenges in managing, processing, and storing large datasets, such as Protein Data Bank (PDB), Crystallographic Information File (CIF) files. These issues, including storage bottlenecks, file incompatibilities, and inefficiencies, hinder progress in RNA and protein structure studies. Moreover, a knowledge gap exists between biologists, who often lack server management expertise, and DevOps engineers, who rarely focus on bioinformatics. This project aims to bridge this gap by designing a robust server architecture tailored to computational biology. Utilizing technologies like containerization, distributed computing, and automated failover systems, the proposed solution will ensure scalability, reliability, and data integrity. Key deliverables include an optimized server infrastructure, comparative analyses of backend frameworks and programming languages, a load-balanced, auto-scaling platform, and CI/CD pipelines to enhance bioinformatics workflows and mitigate data inefficiencies.