Project Summary The exploration and comprehension of phylogenetic trees have emerged as fundamental aspects of contemporary biological research. Phylogenetic trees offer significant insights into the evolutionary interrelationships among organisms. Furthermore, they play a crucial role in elucidating the spread of diseases, including the origin and evolution of pathogens, the temporal and spatial distribution of prevalence, and the prediction of pathogen transmission patterns. Additionally, phylogenetic trees facilitate the investigation of the functional genomics of diverse species, including the emergence of novel body plans or metabolic pathways, molecular adaptation, the evolution of morphological characteristics, and demographic shifts in species that have recently diverged. Advances in sequencing technologies have greatly enhanced the analysis of phylogenetic trees, enabling the examination of extensive datasets, such as entire genomes. The utilization of phylogenetic analysis use cases offers significant value by providing researchers with a deeper understanding of the evolutionary progression of species and their relationships. While our phylogenetic analysis framework is designed to incorporate a generalized workflow, our primary focus will be on developing specific use cases tailored to meet the needs of our user base within our cloud-based learning modules. Because this learning module will be accessible through cloud computing, students will be able to concentrate on phylogeny analysis without the need to install software or verify software versions initially. Leveraging our use cases, we will expose learners to the diverse applications of phylogeny in biomedical science. The use of "small" datasets will ensure that cloud computing resources are not over-allocated. Additionally, users will have the ability to upload other sample data, and our module will include a data controller responsible for validating input and parameters, ensuring they conform to the specified "acceptable" range before executing the workflow. Impact: We will leverage our combined expertise to develop a Phylogeny Workflow Self- Learning Module. This learning module will include instructional videos, an interactive workflow implemented using Jupyter Notebook, and practical exercises that enable self- learning with toy datasets. This resource will provide the educational community with a valuable tool for understanding how biofilm impacts human health. The phylogeny analysis workflow will be a versatile solution, allowing researchers to deploy it on various platforms and apply it to a wide range of use cases.