Date of Award
Doctor of Philosophy (PhD)
The research objective of this thesis is to develop and apply new computational tools and techniques in order to potentiate and expand the utility of microbiome research efforts, particularly in the application area of biofuels development. Tools for microbiome analysis, such as the Quantitative Insights Into Microbial Ecology (QIIME) package, facilitate the processing of raw data produced by next- generation sequencing instruments and also provide implementations of algorithms that aid in drawing biological conclusions from the data. However, the scope of typical modern analyses has increased significantly in the past five years, making integration of the heterogeneous multi-omic datasets challenging. Additionally, the size of datasets has grown beyond what was historically tractable, especially for meta-analyses that combine multiple studies.
Chapter one presents relevant background information about microbial ecology and a summary of the state of existing computational tools and techniques used in its exploration. Chapter two focuses on an in-depth analysis of the microbial community variations associated with algal bioreactors. Chapter three describes a novel computational method that permits broader and deeper microbiome analyses and its application to samples taken from the site of the Deepwater Horizon oil spill. Chapter three also highlights the power of meta- analysis and the need for tools that provide researchers an easier way to analyze their data in the context of comparable publicly available data. Chapter four discusses a variety of data storage models and their suitability for microbiome research, and chapter five describes Qiita, a new platform that takes lessons from chapters three and four and provides an integrated platform for accessing and analyzing data in meta-analyses. Lastly, the appendix presents detailed information on analyzing microbial communities with QIIME.
Robbins-Pianka, Adam, "Advanced Computational Tools for Analyzing Microbial Communities for Energy Production Environments" (2015). Computer Science Graduate Theses & Dissertations. 105.