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Developing a Phylogeny Based Machine Learning Algorithm for Metagenomics

Description: Metagenomics is the study of the totality of the complete genetic elements discovered from a defined environment. Different from traditional microbiology study, which only analyzes a small percent of microbes that could survive in laboratory, metagenomics allows researchers to get entire genetic information from all the samples in the communities. So metagenomics enables understanding of the target environments and the hidden relationships between bacteria and diseases. In order to efficiently analyze the metagenomics data, cutting-edge technologies for analyzing the relationships among microbes and communities are required. To overcome the challenges brought by rapid growth in metagenomics datasets, advances in novel methodologies for interpreting metagenomics data are clearly needed. The first two chapters of this dissertation summarize and compare the widely-used methods in metagenomics and integrate these methods into pipelines. Properly analyzing metagenomics data requires a variety of bioinformatcis and statistical approaches to deal with different situations. The raw reads from sequencing centers need to be processed and denoised by several steps and then be further interpreted by ecological and statistical analysis. So understanding these algorithms and combining different approaches could potentially reduce the influence of noises and biases at different steps. And an efficient and accurate pipeline is important to robustly decipher the differences and functionality of bacteria in communities. Traditional statistical analysis and machine learning algorithms have their limitations on analyzing metagenomics data. Thus, rest three chapters describe a new phylogeny based machine learning and feature selection algorithm to overcome these problems. The new method outperforms traditional algorithms and can provide more robust candidate microbes for further analysis. With the frowing sample size, deep neural network could potentially describe more complicated characteristic of data and thus improve model accuracy. So a deep learning framework is designed on top of the shallow learning algorithm stated above in order to further ...
Date: August 2017
Creator: Rong, Ruichen
Partner: UNT Libraries

Shotgun metaproteomics of the human distal gut microbiota

Description: The human gut contains a dense, complex and diverse microbial community, comprising the gut microbiome. Metagenomics has recently revealed the composition of genes in the gut microbiome, but provides no direct information about which genes are expressed or functioning. Therefore, our goal was to develop a novel approach to directly identify microbial proteins in fecal samples to gain information about the genes expressed and about key microbial functions in the human gut. We used a non-targeted, shotgun mass spectrometry-based whole community proteomics, or metaproteomics, approach for the first deep proteome measurements of thousands of proteins in human fecal samples, thus demonstrating this approach on the most complex sample type to date. The resulting metaproteomes had a skewed distribution relative to the metagenome, with more proteins for translation, energy production and carbohydrate metabolism when compared to what was earlier predicted from metagenomics. Human proteins, including antimicrobial peptides, were also identified, providing a non-targeted glimpse of the host response to the microbiota. Several unknown proteins represented previously undescribed microbial pathways or host immune responses, revealing a novel complex interplay between the human host and its associated microbes.
Date: October 15, 2008
Creator: VerBerkmoes, N.C.; Russell, A.L.; Shah, M.; Godzik, A.; Rosenquist, M.; Halfvarsson, J. et al.
Partner: UNT Libraries Government Documents Department

Metagenomic Insights into Evolution of a Heavy Metal-Contaminated Groundwater Microbial Community

Description: Understanding adaptation of biological communities to environmental change is a central issue in ecology and evolution. Metagenomic analysis of a stressed groundwater microbial community reveals that prolonged exposure to high concentrations of heavy metals, nitric acid and organic solvents (~;;50 years) have resulted in a massive decrease in species and allelic diversity as well as a significant loss of metabolic diversity. Although the surviving microbial community possesses all metabolic pathways necessary for survival and growth in such an extreme environment, its structure is very simple, primarily composed of clonal denitrifying ?- and ?-proteobacterial populations. The resulting community is over-abundant in key genes conferring resistance to specific stresses including nitrate, heavy metals and acetone. Evolutionary analysis indicates that lateral gene transfer could be a key mechanism in rapidly responding and adapting to environmental contamination. The results presented in this study have important implications in understanding, assessing and predicting the impacts of human-induced activities on microbial communities ranging from human health to agriculture to environmental management, and their responses to environmental changes.
Date: February 15, 2010
Creator: Hemme, Christopher L.; Deng, Ye; Gentry, Terry J.; Fields, Matthew W.; Wu, Liyou; Barua, Soumitra et al.
Partner: UNT Libraries Government Documents Department

Noncompetitive microbial diversity patterns in soils: their causes and implications for bioremediation

Description: This funding provided support for over nine years of research on the structure and function of microbial communities in subsurface environments. The overarching goal during these years was to understand the impact of mixed contaminants, particularly heavy metals like uranium, on the structure and function of microbial communities. In addition we sought to identify microbial populations that were actively involved in the reduction of metals because these species of bacteria hold the potential for immobilizing soluble metals moving in subsurface water. Bacterial mediated biochemical reduction of metals like uranium, technetium and chromium, greatly reduces their mobility through complexation and precipitation. Hence, by taking advantage of natural metabolic capabilities of subsurface microbial populations it is possible to bioremediate contaminated subsurface environments with a cost-effective in situ approach. Towards this end we have i.) identified bacterial populations that have thrived under the adverse conditions at the contaminated FRC site, ii.) phylogenetically identified populations that respond to imposed remediation conditions at the FRC, iii.) used metagenomics to begin a reconstruction of the metabolic web in a contaminated subsurface zone, iv.) investigated the metal reducing attributes of a Gram-positive spore forming rod also capable of dechlorination.
Date: July 5, 2007
Creator: Tiedje, James M.; Zhou, Jizhong; Palumbo, Anthony; Ostrom, Nathaniel & Marsh, Terence L.
Partner: UNT Libraries Government Documents Department

Environmental genomics reveals a single species ecosystem deep within the Earth

Description: DNA from low biodiversity fracture water collected at 2.8 km depth in a South African gold mine was sequenced and assembled into a single, complete genome. This bacterium, Candidatus Desulforudis audaxviator, comprises>99.9percent of the microorganisms inhabiting the fluid phase of this particular fracture. Its genome indicates a motile, sporulating, sulfate reducing, chemoautotrophic thermophile that can fix its own nitrogen and carbon using machinery shared with archaea. Candidatus Desulforudis audaxviator is capable of an independent lifestyle well suited to long-term isolation from the photosphere deep within Earth?s crust, and offers the first example of a natural ecosystem that appears to have its biological component entirely encoded within a single genome.
Date: September 17, 2008
Creator: Chivian, Dylan; Brodie, Eoin L.; Alm, Eric J.; Culley, David E.; Dehal, Paramvir S.; DeSantis, Todd Z. et al.
Partner: UNT Libraries Government Documents Department