<p> Lyme disease is the most commonly reported vector-borne disease in the United States. The causative agent of Lyme disease, can alter gene expression to enable survival in a diverse set of conditions, including the tick midgut and the mammalian host. External environmental changes can trigger gene expression in <i>B. burgdorferi,</i> and the data demonstrate that <i> B. burgdorferi</i> can similarly alter gene expression as a stress-response when it is treated with the antibiotic doxycycine. After treatment with the minimum bactericidal concentration (MBC) of doxycycline, a subpopulation can alter its phenotype to survive antibiotic treatment, and to host adapt and successfully infect a mammalian host. Furthermore, our data demonstrate that if a population is treated with the MBC of doxycycline, a subpopulation may alter its phenotype to adopt a state of dormancy until the removal of the antibiotic, whereupon the subpopulation can regrow. We demonstrate that the chance of regrowth occurring increases as a population reaches stationary phase, and present a mathematical model for predicting the probability of a persister subpopulation within a larger population, and ascertain the quantity of a persister subpopulation. To determine which genes are expressed as stress-response genes, RNA Sequencing analysis, or RNASeq, was performed on treated, untreated, and treated and regrown <i>B. burgdorferi</i> samples. The results suggest several genes were significantly different in the treated group, compared to the untreated group, and in the untreated and regrown group compared to the untreated group, including a 50S ribosomal stress-response protein, coded from BB_0786. The appendices discuss the theory and methods that were used in RNA Sequencing (RNASeq) analysis, and provide an overview of the database that was created for the <i>B. burgdorferi</i> transcriptome. Additional studies may demonstrate further how persister subpopulations form, and which genes can trigger a persister state in <i>B. burgdorferi.</i></p>

<p> Evolutionary forces such as recombination, demography, and selection can shape patterns of genetic diversity within populations and contribute to phenotypic variation. While theoretical models exist for each of these forces independently, mathematically modeling their joint impact on patterns of genetic diversity remains very challenging. Fortunately, it is possible to perform forward-in-time computer simulations of DNA sequences that incorporate all of these forces simultaneously. Here, I show that there are trade-offs between computational efficiency and accuracy for simulations of a widely investigated model of recurrent positive selection. I develop a theoretical model to explain this trade-off, and a simple algorithm that obtains the best possible computational performance for a given error tolerance. I then pivot to develop a framework for simulations of human DNA sequences and genetically complex phenotypes, incorporating recently inferred demographic models of human continental groups and selection on genes and non-coding elements. I use these simulations to investigate the power of rare variant association tests in the context of rampant selection and non-equilibrium demography. I show that the power of rare variant association tests is in some cases quite sensitive to underlying assumptions about the relationship between selection and effect sizes. This work highlights both the challenge and the promise of applying forward simulations in genetic studies that seek to infer the parameters of evolutionary models and detect statistical associations.</p>

<p>Structural genomics is a field of study that strives to derive and analyze the structural characteristics of proteins through means of experimentation and prediction using software and other automatic processes. Alongside implications for more effective drug design, the main motivation for structural genomics concerns the elucidation of each protein&rsquo;s function, given that the structure of a protein almost completely governs its function. Historically, the approach to derive the structure of a protein has been through exceedingly expensive, complex, and time consuming methods such as x-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. </p><p> In response to the inadequacies of these methods, three families of approaches developed in a relatively new branch of computer science known as bioinformatics. The aforementioned families include threading, homology-modeling, and the de novo approach. However, even these methods fail either due to impracticalities, the inability to produce novel folds, rampant complexity, inherent limitations, etc. In their stead, this work proposes the Fuzzy Greedy K-means Decision Forest model, which utilizes sequence motifs that transcend protein family boundaries to predict local tertiary structure, such that the method is cheap, effective, and can produce semi-novel folds due to its local (rather than global) prediction mechanism. This work further extends the FGK-DF model with a new algorithm, the Hierarchically Clustered-Hidden Markov Models (HC-HMM) method to extract protein primary sequence motifs in a more accurate and adequate manner than currently exhibited by the FGK-DF model, allowing for more accurate and powerful local tertiary structure predictions. Both algorithms are critically examined, their methodology thoroughly explained and tested against a consistent data set, the results thereof discussed at length. </p>

Differential gene expression between sexes is the main contributor of the morphological and behavioral differences observed between them. Studying the signatures of these differences at the genetic level will help us understand the forces acting on them. The existence of androdioecious and gonochoristic species in the genus Caenorhabditis makes it suitable for sex-biased gene expression studies. In this thesis, I have assembled the transcriptome of C. sp. 9 and C. sp. 5 using de novo and reference-based techniques. Evolutionary analysis of the assembled contigs showed that genes with male-biased expression evolve faster than those with a female bias, as observed in other taxa. Furthermore, I found a positive correlation between gene expression and codon usage bias.

Identification and characterization of regions influencing the precise spatial and temporal expression of genes is critical to our understanding of gene regulatory networks. Connecting transcription factors to the cis-regulatory elements that they bind and regulate remains a challenging problem in computational biology. The rapid accumulation of whole genome sequences and genome-wide expression data, and advances in alignment algorithms and motif-finding methods, provide opportunities to tackle the important task of dissecting how genes are regulated. Genes exhibiting similar expression profiles are often regulated by common transcription factors. We developed a method for identifying statistically over-represented regulatory motifs in the promoters of co-expressed genes using weight matrix models representing the specificity of known factors. Application of our methods to yeast fermenting in grape must revealed elements that play important roles in utilizing carbon sources. Extension of the method to metazoan genomes via incorporation of comparative sequence analysis facilitated identification of functionally relevant binding sites for sets of tissue-specific genes, and for genes showing similar expression in large-scale expression profiling studies. Further extensions address alternative promoters for human genes and coordinated binding of multiple transcription factors to cis-regulatory modules. Sequence conservation reveals segments of genes of potential interest, but the degree of sequence divergence among human genes and their orthologous sequences varies widely. Genes with a small number of well-distinguished, highly conserved non-coding elements proximal to the transcription start site may be well-suited for targeted laboratory promoter characterization studies. We developed a “regulatory resolution” score to prioritize lists of genes for laboratory gene regulation studies based on the conservation profile of their promoters. Additionally, genome-wide comparisons of vertebrate genomes have revealed surprisingly large numbers of highly conserved non-coding elements (HCNEs) that cluster nearby to genes associated with transcription and development. To further our understanding of the genomic organization of regulatory regions, we developed methods to identify HCNEs in insects. We find that HCNEs in insects have similar function and organization as their vertebrate counterparts. Our data suggests that microsynteny in insects has been retained to keep large arrays of HCNEs intact, forming genomic regulatory blocks that surround the key developmental genes they regulate.

Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide –A, T, C or G – is altered. Arguably, SNPs account for more than 90% of human genetic variation. Dr. Tebbutt's laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). The strength of this platform is its unique redundancy having multiple probes for a single SNP. Using this microarray platform, we have developed fully-automated genotype calling algorithms based on linear models for individual probe signals and using dynamic variable selection at the prediction level. The algorithms combine separate analyses based on the multiple probe sets to give a final confidence score for each candidate genotypes. Our proposed classification model achieved an accuracy level of >99.4% with 100% call rate for the SNP genotype data which is comparable with existing genotyping technologies. We discussed the appropriateness of the proposed model related to other existing high-throughput genotype calling algorithms. In this thesis we have explored three new ideas for classification with high dimensional data: (1) ensembles of various sets of predictors with built-in dynamic property; (2) robust classification at the prediction level; and (3) a proper confidence measure for dealing with failed predictor(s). We found that a mixture model for classification provides robustness against outlying values of the explanatory variables. Furthermore, the algorithm chooses among different sets of explanatory variables in a dynamic way, prediction by prediction. We analyzed several data sets, including real and simulated samples to illustrate these features. Our model-based genotype calling algorithm captures the redundancy in the system considering all the underlying probe features of a particular SNP, automatically down-weighting any ‘bad data’ corresponding to image artifacts on the microarray slide or failure of a specific chemistry. Though motivated by this genotyping application, the proposed methodology would apply to other classification problems where the explanatory variables fall naturally into groups or outliers in the explanatory variables require variable selection at the prediction stage for robustness.

Diss. (sammanfattning) Uppsala : Univ., 2004. / Härtill 6 uppstaser.

Licentiatavhandling Uppsala, Univ : 2004. / Härtill 4 uppsatser.

Thesis (M.S.)--Georgia State University, 2008. / Title from file title page. Jiawei Liu, Yu-sheng Hsu, committee co-chairs; Jeff Qin, committee member. Electronic text (82 p. : ill. (some col.)) : digital, PDF file. Description based on contents viewed Aug. 20, 2008. Includes bibliographical references (p. 65-66).

Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 73-82) Also available in print.