The goal of the research project is to help the biology department visualize co-evolution between two microorganisms. In the lab, twenty-two communities of two microorganisms are cultured in a controlled environment over several thousand generations to investigate their co-evolution. Through a series of genome sequencing analyses, we were able to detect mutations evolving over generations. Determining co-evolution based on genome sequencing can be a complicated, manual, and multiple-step process. To automate the process, it took several steps. The first step was to create a MySQL database system to store the genome sequencing information. This step included creating a front-end with filtering capabilities that help trace the mutations’ changes and investigate the genome dynamics with the frequencies of each variation in a population. Next was determining the most accurate hierarchical clustering method for determining the cohorts and displayed the clustering script to the web with filtering capabilities. The Last was creating Muller diagrams based on the optimized clusters. Automating the Muller diagrams will help the biology department to visualize the evolutionary dynamics over time. The additional filtering capabilities will allow the biology department to determine the best clusters as the generations and genome sequence information grows.