Understanding and Analyzing the Human Microbiome: Taxonomic Identification and Potential Interactions




Naqvi, Arnmar S. Abbas

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The Microbiome Analysis Center, in collaboration with Rush University Medical Center and Case Western University, has been studying and characterizing gut microbiota in normal and diseased states such as ALD and HIV. Currently, the Multitag Pyrosequencing (MTPS) methodology developed by Dr. Gillevet is being used to inten-ogate the microbiome from dozens of samples at a time. As a result we have been receiving hundreds of samples of both bacterial and fungal microbiome of stool, mucosal biopsy, and oral samples from a large number of subjects and diseases. The vast volumes of data flowing from diverse sources has necessitated the development of analysis pipelines in order to intelligently and rapidly process the molecular information and to analyze, cluster, and con-elate the sample datasets. However, a fundamental and pre-requisite for most research in this particular is being able to efficiently and accurately identify the genus and species information given a set of SSU rRNA sequences. The cun-ent implementation of this type of investigation is widespread, but as datasets get very large it proves to be very impractical due to factors concerning run-time and memory. For this particular study, we have developed and designed a portable and robust tool to identify the bacterial taxonomy and distribution at the species level, specifically in patients with HIV looking at the vaginal microflora. Another very important aspect of the Microbiome is to understand the relationships of the bacteria between and amongst different classes (ie. healthy, diseased). In order to accomplish this we plan on applying a systems biology approach to the microflora. This study will produce an approach that will specifically look at the gut microbiota in relation to Alcohol Liver Disease at the graphical network level. A series of challenges is anticipated related to time and memory constraints, informative identification, and proper linkage of taxonomic identification to the clinical information in the microbiome. We discovered distinct and common features amongst our samples that will provide novel insights and ultimately broaden our understanding of how the microbiome influences human health, furthering future research in this rapidly progressing field.



Microbiome, Bio Informatics, Taxonomic