Biofilms are complex formations of microbial communities composed of different types of microorganisms such as
bacteria, viruses and fungi. They are responsible for the majority of human microbial infections. Understanding how
biofilm impacts human health and how it can be controlled is becoming increasingly important for preventive
medicine. Here, we propose the development of a Biofilm Metagenomics Workflow Self-Learning Module to aid in the
understanding of biofilm’s role in human health. Metagenomics has emerged as a powerful tool for the genomic analysis
of biofilm through function-based gene sequence identification (functional metagenomics) and sequence-based function
identification (sequence metagenomics). Metagenomic sequencing provides the ability to comprehensively sample all
genes in all organisms present in each biofilm sample using Quorum Sensing signatures. Our Biofilm Metagenomics
Workflow Self-Learning Module will provide students with an analysis resource beneficial for the aggregation of
knowledge about speci¿c genes, microbial communities and its metabolic pathways.
Because this learning module will be available through cloud computing, students can focus on biofilm metagenomics
analysis rather than first having to install software and verifying software versioning. Our cost-effective optimization
Docker Image will include the packaging of the workflow along with sample datasets related to the three focus use cases.
These “small” datasets will ensure that the cloud computing resources will not be over allocated. Users will also be able to
upload other sample data and our module will include a data controller that will verify that before the workflow is
executed, the input and parameters are checked to ensure that they fall into the “acceptable” range.
Impact: We will use our combined expertise to develop a Biofilm Metagenomics Workflow Self-Learning Module. This
learning module will include instructional videos, a Dockerized, interactive workflow implemented using Jupyter
Notebook, and practicum exercise that will enable self-learning on toy datasets. This will provide the educational
community with a Biofilm Metagenomics resource that will aid in their understanding of how biofilm impacts human
health. The Biofilm Metagenomics Workflow is a generalized solution, allowing researchers to deploy it to alternate
platforms and apply it to a wide range of use cases.