Microorganisms are crucial components of ecosystems and human civilization. They play prominent roles in processes such as the formation and maintenance of soil, and the detoxification of environmental pollutants. Microorganisms also possess an immeasurable store of useful items ranging from enzymes for food processing to pharmaceuticals. Until recently, our understanding of this group of organisms has been limited by the small percentage of microorganisms that can be identified through traditional methods. Such methods involve growing the organisms in the laboratory. In the 1980s, new approaches were developed that allow organisms to be identified without the need to grow them in the lab. Such approaches identify microorganisms through the analysis of specific genes (ribosomal RNA genes). This development was a major breakthrough, which has led to the identification of thousands of novel organisms. Yet, despite this advance, current strategies to analyze these genes are either cost prohibitive or unable to generate more than superficial depictions of the microorganisms inhabiting most environments.
This proposal presents three objectives that address this technological deficit. First, the development of an experimental approach that allows thorough analysis of the microorganisms inhabiting any environment is planned. This will be accomplished through the use of new array-based technologies. Second, a public database that allows investigators to utilize the tools that will be created from this project will be developed. And finally, the project provides training for undergraduate, graduate and postgraduate students in microbial community analysis and bioinformatics. Overall, this proposal addresses the goals of the NSF Biological Databases and Informatics program by developing a public database that will facilitate thorough analysis of microbial communities and by disseminating the gained knowledge through interactive tutorials and a workshop. This work will fundamentally transform investigations into microbial community composition by making thorough examinations possible for the first time. This will lead to a greater understanding of how microorganisms function, which, in turn, will lead to more effective strategies to manage agriculture, maintain a clean environment, and advance biotechnology. The training component of this project will foster collaborations between computer scientists, statisticians, and biologists, which will produce the multidisciplinary researchers needed for this new era.
