We present research on the design, development and application of algorithms for DNA sequence analysis, with a focus on environmental DNA (metagenomes). We present an overview and primer on algorithm development for bioinformatics of metagenomes; work on frameshift detection in DNA sequencing data; work on a computational pipeline for the assembly, feature prediction, annotation and analysis of bacterial genomes; work on unsupervised phylogenetic clustering of metagenomic fragments using Markov Chain Monte Carlo methods; and work on estimation of bacterial genome plasticity and diversity, potential improvements to the measures of core and pan-genomes.

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The tumor microenvironment is a complex community consisting of neoplastic cells, surrounding stromal cells, a broad array of immune cells, and a microbiota. By sheer numbers, the microbiota has its greatest manifestation in colorectal cancer (CRC) because the colon contains up to 100 trillion bacteria, outnumbering human cells by a factor of 10 and encoding a gene-content that is 100-fold larger than that of the human genome. Indeed, previous studies using germ-free mice in a variety of genetic backgrounds have demonstrated that the microbiota can impact colorectal tumorigenesis. In addition, specific strains of enterotoxigenic bacteria have been shown to promote colitis-associated cancer in mice. Here, we explore the composition of the tissue-associated microbiota in human CRC and evaluate the role of tumor-enriched microbes in potentiating colorectal tumorigenesis in mice. Advances in DNA sequencing technology have fueled a renaissance in the microbiome field. Deep sequencing metagenomics enables rapid, culture-independent characterization of a microbial community. We present PathSeq, a highly scalable software tool that performs computational subtraction on high-throughput sequencing data to identify nonhuman nucleic acids. PathSeq makes it possible to analyze sequence datasets as large as human whole-genomes for the purpose of metagenomics and also to discover previously unsequenced microorganisms. We used PathSeq to characterize the composition of the microbiota in human CRCusing whole-genome sequencing on nine tumor/normal pairs and 16S rDNA sequencing on an additional 95 pairs. The genus Fusobacterium was highly enriched in tumors, while the Bacteroidetes and Firmicutes phyla were depleted.We show that in the \(Apc^{Min/+}\) mouse model of intestinal tumorigenesis, Fusobacterium nucleatum increases tumor multiplicity, selectively recruits tumor-infiltrating myeloid cells, and is associated with a pro-inflammatory expression signature that is shared with human fusobacteria-positive colorectal carcinomas. We find that Fusobacterium spp. are enriched in human colonic adenomas relative to surrounding tissues and fusobacterial abundance is increased in stool samples from patients with colorectal adenomas and carcinomas, compared to healthy subjects. Collectively, these data support that fusobacteria may be involved in early stages of intestinal tumorigenesis and, through recruitment of tumor-infiltrating immune cells, may generate a pro-inflammatory tissue microenvironment conducive to colorectal neoplasia progression.

At hydrothermal vents, thermal and chemical gradients generated by the mixing of hydrothermal fluids with seawater provide diverse niches for prokaryotic communities. To date, our knowledge of environmental factors that shape bacterial and archaeal community composition and metabolic activities across these gradients within the active sulfide structures is limited. While many studies have laid the foundation for our understanding of the extent of diversity in relation to varying hydrothermal settings, few studies exists regarding the detailed spatial relationships between vent geochemistry and the abundance, distribution, and metabolic characteristics of the endolithic hosted communities. Even fewer data have been generated on the magnitude of metabolic rates and factors controlling the kinetics of these reactions have not been well constrained.

Metagenomics holds unyielding potential in uncovering relationships within microbial communities that have yet to be discovered, particularly because the field circumvents the need to isolate and culture microbes from their natural environmental settings. A common research objective is to detect biomarkers, microbes are associated with changes in a status. For instance, determining such microbes across conditions such as healthy and diseased groups for instance allows researchers to identify pathogens and probiotics. This is often achieved via analysis of differential abundance of microbes. The problem is that differential abundance analysis looks at each microbe individually without considering the possible associations the microbes may have with each other. This is not favorable, since microbes rarely act individually but within intricate communities involving other microbes. An alternative would be variable selection techniques such as Lasso or Elastic Net which considers all the microbes simultaneously and conducts selection. However, Lasso often selects only a representative feature of a correlated cluster of features and the Elastic Net may incorrectly select unimportant features too frequently and erratically due to high levels of sparsity and variation in the data.\par In this research paper, the proposed method AdaLassop is an augmented variable selection technique that overcomes the misgivings of Lasso and Elastic Net. It provides researchers with a holistic model that takes into account the effects of selected biomarkers in presence of other important biomarkers. For AdaLassop, variable selection on sparse ultra-high dimensional data is implemented using the Adaptive Lasso with p-values extracted from Zero Inflated Negative Binomial Regressions as augmented weights. Comprehensive simulations involving varying correlation structures indicate that AdaLassop has optimal performance in the presence multicollinearity. This is especially apparent as sample size grows. Application of Adalassop on a Metagenome-wide study of diabetic patients reveals both pathogens and probiotics that have been researched in the medical field.

Upon request of the VPR and the thesis advisor this item has been made administrative access only until further notice. / Biodegradation of petroleum has been observed to be one of the most important factors that can alter reservoir chemistry. Biodegradation of petroleum has been connected to the generation of heavy oil at the expense of light hydrocarbon components. Generally, heavy oil is associated with the increasing in metal and sulfur content as well as viscosity. In addition, petroleum biodegradation will result in the production of certain metabolites that are implicated in forming emulsions and corrosion problems in the producing and refining facilities. However, identifying the microrganisms that catalyse this biodegradation is crucial to understanding their role in the hydrocarbons alteration. In this thesis, I addressed the connection between the petroleum biodegradation and the formation of light hydrocarbon components at the expense of heavy hydrocarbon components, and the increase in gas/oil ratio. A comparison between light, extra light, and medium sour crudes lends support to the hypothesis of light hydrocarbons formation through biodegradation of long chain oil components. The results suggested that there was no direct relationship between the relative density of oil and the level of biodegradation, but, there was a positive correlation between the level of biodegradation, the formation of light hydrocarbons, and an increase in the gas/oil ratio. As a first step in investigating this correlation, a metagenomics approach was used to identify and characterize the biodiversity in a European oil field. Extrapolation of the oilfield microbiome data based on an analysis of 200 species generated a hypothetical metabolic map that suggests a new model for petroleum formation and degradation that challenges the accepted dogma in which aerobic and anaerobic petroleum degradation is taking place in the hydrocarbons reservoir, as it is a matter of rate; where the aerobic petroleum degradation targets the short-chain hydrocarbons specifically methane and result in heavy oil generation; whereas the anaerobic petroleum degradation leads to form the gaseous components such as methane, carbon dioxide and hydrogen sulfide. Hence, the gaseous components have a direct impact on the oil density when they represent the majority of the oil field composition by making it more gaseous than liquid.

Viruses represent the most abundant biological entities on earth where, they are able to interact with all kingdoms of life. Yet their diversity, ecology and evolutionary aspects are only beginning to be fully elucidated, mainly due to technical limitations. The vast majority of the microbial world remains elusive to culture; more than 90% of genome sequenced viral isolates infect only 5 of the 54 prokaryotic phyla that are currently recognized. In contrast, viral metagenomics bypasses the need for cultures by directly sequencing fragmented genetic material of environmental viral communities. This dissertation uses viral metagenomics by applying well-tested bioinformatic protocols and expanding them to compare and contrast patterns of diversity, richness and specialization of large viral metagenomic datasets, in both local and global scales. First I demonstrate the utility of a functional-based perspective by adopting the protein cluster environment to estimate global viral diversity. Then, I use this PC approach to analyze metagenomes from two ecologically different environments, which by uncovering local gene specialization showcases the adequacy of a gene-centered workflow. Then I continue to expand upon this PC framework to study the Tara Oceans virome analyses of these data reveal patters of diversity that support a seed bank model. Finally, in search of a more meaningful ecological unit, I move from a gene-centered standpoint towards a population-based frame. We adopted a novel metagenomic technique that allowed me to uncover the discontinuity in the genomic sequence space, thus empirically defining a population. This final contribution will allow to sort and count viral communities, the first step to applying ecological and evolutionary theory.

The field of metagenomics has shown great promise in the ability to recover microbial DNA from communities whose members resist traditional cultivation techniques, although in most instances the recovered material comprises short anonymous genomic fragments rather than complete genome sequences. In order to effectively assess the microbial diversity and ecology represented in such samples, accurate methods for DNA classification capable of assigning metagenomic fragments into their most likely taxonomic unit are required. Existing DNA classification methods have shown high levels of accuracy in attempting to classify sequences derived from low-complexity communities, however genome distinguishability generally deteriorates for complex communities or those containing closely related organisms. The goal of this thesis was to identify factors both intrinsic or external to the genome that may lead to the improvement of existing DNA classification methods and to probe the fundamental limitations of composition-based genome distinguishability. To assess the suite of factors affecting the distinguishability of genomes, support vector machine classifiers were trained to discriminate between pairs of microbial genomes using the relative frequencies of oligonucleotide patterns calculated from orthologous genes or short genomic fragments, and the resulting classification accuracy scores used as the measure of genomic distinguishability. Models were generated in order to relate distinguishability to several measures of genomic and taxonomic similarity, and interesting outlier genome pairs were identified by large residuals to the fitted models. Examination of the outlier pairs identified numerous factors that influence genome distinguishability, including genome reduction, extreme G+C composition, lateral gene transfer, and habitat-induced genome convergence. Fragments containing multiple protein-coding and non-coding sequences showed an increased tendency for misclassification, except in cases where the genomes were very closely related. Analysis of the biological function annotations associated with each fragment demonstrated that certain functional role categories showed increased or decreased tendency for misclassification. The use of pre-processing steps including DNA recoding, unsupervised clustering, 'symmetrization' of oligonucleotide frequencies, and correction for G+C content did not improve distinguishability. Existing composition-based DNA classifiers will benefit from the results reported in this thesis. Sequence-segmentation approaches will improve genome distinguishability by decreasing fragment heterogeneity, while factors such as habitat, lifestyle, extreme G+C composition, genome reduction, and biological role annotations may be used to express confidence in the classification of individual fragments. Although genome distinguishability tends to be proportional to genomic and taxonomic relatedness, these trends can be violated for closely related genome pairs that have undergone rapid compositional divergence, or unrelated genome pairs that have converged in composition due to similar habitats or unusual selective pressures. Additionally, there are fundamental limits to the resolution of composition-based classifiers when applied to genomic fragments typical of current metagenomic studies.

Helicobacter pylori infects more than half of the world's population, and is known to be involved in several diseases including gastric cancer. Its close interactions with the stomach and host immune system serves as a good model to study the co-adaptation and co-evolution of the organisms in the stomach micro-environment. In this project, we utilized RNA-seq and data analysis tools to investigate differentially expressed genes by H. pylori in patients at different stages of early gastric cancer development. We also investigated the abundance and diversity of bacterial genera other than H. pylori, and looked for correlations with H. pylori presence and number. For differential gene expression of H. pylori, one gene was differentially expressed between samples of corpus atrophy without metaplasia vs. samples of antrum gastritis, and eight genes were found to be differentially expressed between samples of corpus atrophy with metaplasia vs. samples with pan-gastritis. When samples were clustered into different groups based on the expression data, 52 genes (shared or unique to the specific comparison groups) were found to be differentially expressed, but no apparent patterns were observed that could be explained by medical or sample collection data. For bacterial diversity and abundances, we found several genera colonizing the stomach, of which some have been previously identified. While most of these bacteria colonize regardless of the presence of H. pylori, the abundance of three genera, Wolinella, Campylobacter, and Veillonella, seem to be correlated with the presence of H. pylori.

Background During the first stages of development human infants are either fed human milk or human milk substitutes (infant formulas). The composition of infant formulas and human milk differ drastically, including a difference in protein constituents and bacterial load. Due to the high global frequency of infant formula use, the humanization of infant formulas to better reflect the complex nature of human milk is warranted. To better understand the role of human milk components, the fate and function of a key bacterial sensor in human milk, soluble CD14, was determined. Additionally, the microbiome of human milk was analyzed from a metagenomic standpoint in an attempt to determine which types of bacteria are present in human milk and what their potential biological function might be. Results In rodent models, ingested sCD14 persisted in the gastrointestinal tract and was transferred intact into the blood stream. Once transferred to the blood, ingested sCD14 retained its ability to recognize lipopolysaccharide and initiate an immune response in pups. This transfer of sCD14 across the epithelial barrier was also observed in human cells in vitro, where it appears to be dependent on Toll-like receptor 4. Using Illumina sequencing and the MG-RAST pipeline, the human milk metagenome of ten mothers was sequenced. DNA from human milk aligned to over 360 prokaryotic genera, and contained 30,128 open reading frames assigned to various functional categories. The DNA from human milk was also found to harbor immune-modulatory DNA motifs that may play a significant role in immune development of the infant. Conclusions Given the complex nature of human milk in comparison to its bovine or plant based substitutes, the results presented in this thesis warrant future modification of infant formulas to include non-nutritive bioactive components. Current human milk components not yet present in infant formulas include the diverse microbiome of human milk, the immune-modulatory DNAs which those microbes harbor, and bioactive human proteins such as sCD14.