Miniature Inverted-repeat Transposable Elements (MITEs) are a type of transposable element (TE) that lacks coding capacity. It has been established that in rice that certain Stowaway MITEs are mobilized by transposases from the Tc1/Mariner superfamily of TEs. To retrieve all Tc1/Mariner TEs from the genome, bioinformatic approaches were performed. A total of 295 Tc1/Mariner TEs that encoded a full or partial transposase were recorded which 100 were newly described. Sequence alignment, and identification of the catalytic motif placed these transposases into eight groups. A functional link was established by comparing the terminal sequences of the Stowaway-like MITEs to the termini of the terminal sequences of Tc1/Mariner TEs. A yeast excision assay was used to experimentally test these functional links. Majority of the Stowaway-like MITE and transposase combinations tested did not indicate a functional link. However, a possible functional link was observed between the AATp3-13 transposase and AAStow-5 Stowaway-like MITEs.

The increasing prevalence of apicomplexan parasites such as Plasmodium, Toxoplasma, and Cryptosporidium represents a significant global healthcare burden. Treatment options are increasingly limited due to the emergence of new resistant strains. We postulate that parasites have evolved distinct metabolic strategies critical for growth and survival during human infections, and therefore susceptible to drug targeting using a systematic approach. I developed iCS306, a fully characterized metabolic network reconstruction of the model organism Toxoplasma gondii via extensive curation of available genomic and biochemical data. Using available microarray data, metabolic constraints for six different clinical strains of Toxoplasma were modeled. I conducted various in silico experiments using flux balance analysis in order to identify essential metabolic processes, and to illustrate the differences in metabolic behaviour across Toxoplasma strains. The results elucidate probable explanations for the underlying mechanisms which account for the similarities and differences among strains of Toxoplasma, and among species of Apicomplexa.

The objective of this study was to clarify the regulation of production and signal transduction of juvenile hormone (JH) in insects by experimentally examining the function and evolution of a putative receptor (USP/RXR) and a neuropeptide inhibitor (FGLamide allatostatin). To examine the role of USP/RXR, the cDNA sequence of the receptor was obtained from the cockroach Diploptera punctata. Transcript levels during developmentally critical periods for JH sensitivity may suggest USP/RXR is JH responsive. Comparative sequence analysis of evolutionary rates in the Mecopterida support current hypotheses which suggest some gain in function along this lineage, although this acquisition may have occurred more gradually than previously assumed. To examine allatostatin evolution within insects, ancestral peptides inferred using maximum likelihood ancestral reconstruction methods were assayed for in vitro inhibition of JH production in two cockroach species. Shifts in peptide potency in some ancestral peptides reconstructed may be related to peptide copy number evolution.

In order to identify expressologs (orthologs exhibiting the highest expression profile ranking) among a variety of plant species, bioinformatic methods were used in order to first identify sequence orthologs and subsequently to rank these orthologs based on expression profile similarity. Analyses conducted on these data suggested that expressologs exhibited greater functional equivalency. A comparison of drought response in A. thaliana and Populus showed that expressologs exhibited a higher correlation when computed using stress data as opposed to developmental data. This suggested that the use of condition-specific data sets is more appropriate when examining specific conditions. Analysis was conducted in order to investigate the hypothesis that neutral evolution was a predominant factor in gene expression divergence. Some evidence was found for selection acting on expression pattern maintenance. Further analysis will be required in order to confirm the type of selection acting to maintain expression patterns across species.

Miniature Inverted-repeat Transposable Elements (MITEs) are a type of transposable element (TE) that lacks coding capacity. It has been established that in rice that certain Stowaway MITEs are mobilized by transposases from the Tc1/Mariner superfamily of TEs. To retrieve all Tc1/Mariner TEs from the genome, bioinformatic approaches were performed. A total of 295 Tc1/Mariner TEs that encoded a full or partial transposase were recorded which 100 were newly described. Sequence alignment, and identification of the catalytic motif placed these transposases into eight groups. A functional link was established by comparing the terminal sequences of the Stowaway-like MITEs to the termini of the terminal sequences of Tc1/Mariner TEs. A yeast excision assay was used to experimentally test these functional links. Majority of the Stowaway-like MITE and transposase combinations tested did not indicate a functional link. However, a possible functional link was observed between the AATp3-13 transposase and AAStow-5 Stowaway-like MITEs.

The genomes of all eukaryotic organisms are packaged into chromatin, the fundamental unit of which is the nucleosome. Since the proposal of the nucleosome as the primary repeating unit of chromatin structure in 1974, it has become clear that the biological roles of nucleosomes extend far beyond simple DNA packaging and include virtually all processes involving the genome. Despite the integral roles of nucleosomes in many fundamental biological processes, the relative contributions of the cellular cues and sequence features that directly govern their arrangement on genomic DNA remain unclear. In this Thesis, I characterise the sequence preferences of nucleosomes using data sets derived from genome-wide studies. I describe the analysis of data derived from the first genome-wide map of in vivo nucleosome occupancy across a eukaryotic genome (in this case, the budding yeast, Saccharomyces cerevisiae). Using these data, I construct a sequence-based linear model of nucleosome occupancy that takes into account structural features of DNA (which correlate with simple base composition) as well as transcription factor (TF) binding site information, which has significant ability to predict nucleosome occupancy in vivo. I go on to test particular aspects of this model and show that genetic perturbation of TFs that the in vivo model deems important (Abf1, Reb1, and Rsc3) have the expected effects, an increase in nucleosome occupancy over their cognate binding sites as well as a reduction in transcription from the corresponding genes, suggesting that these factors are required for promoter function and definition. I also confirm that in vitro nucleosome occupancy correlates highly with sequence features important for nucleosome occupancy in vivo and go on to develop a simple model for nucleosome occupancy based solely on histone-DNA interactions. This model suggests that base composition (G+C content) is a dominant feature in determining intrinsic nucleosome occupancy. Finally, I apply a model of intrinsic nucleosome occupancy to the human genome and show that there is a fundamental difference in intrinsic nucleosome occupancy at regulatory regions across species. This finding illustrates a potential functional consequence of variation in base composition in eukaryotic genomes.

The genomes of all eukaryotic organisms are packaged into chromatin, the fundamental unit of which is the nucleosome. Since the proposal of the nucleosome as the primary repeating unit of chromatin structure in 1974, it has become clear that the biological roles of nucleosomes extend far beyond simple DNA packaging and include virtually all processes involving the genome. Despite the integral roles of nucleosomes in many fundamental biological processes, the relative contributions of the cellular cues and sequence features that directly govern their arrangement on genomic DNA remain unclear. In this Thesis, I characterise the sequence preferences of nucleosomes using data sets derived from genome-wide studies. I describe the analysis of data derived from the first genome-wide map of in vivo nucleosome occupancy across a eukaryotic genome (in this case, the budding yeast, Saccharomyces cerevisiae). Using these data, I construct a sequence-based linear model of nucleosome occupancy that takes into account structural features of DNA (which correlate with simple base composition) as well as transcription factor (TF) binding site information, which has significant ability to predict nucleosome occupancy in vivo. I go on to test particular aspects of this model and show that genetic perturbation of TFs that the in vivo model deems important (Abf1, Reb1, and Rsc3) have the expected effects, an increase in nucleosome occupancy over their cognate binding sites as well as a reduction in transcription from the corresponding genes, suggesting that these factors are required for promoter function and definition. I also confirm that in vitro nucleosome occupancy correlates highly with sequence features important for nucleosome occupancy in vivo and go on to develop a simple model for nucleosome occupancy based solely on histone-DNA interactions. This model suggests that base composition (G+C content) is a dominant feature in determining intrinsic nucleosome occupancy. Finally, I apply a model of intrinsic nucleosome occupancy to the human genome and show that there is a fundamental difference in intrinsic nucleosome occupancy at regulatory regions across species. This finding illustrates a potential functional consequence of variation in base composition in eukaryotic genomes.

The freshwater planarian Schmidtea mediterranea has been used in research for over 100 years, and is an emerging stem cell model. Exteriorly, planarians are covered in mucous secretions of unknown composition. While the planarian genome has been sequenced, it remains mostly unannotated. The goal my master’s research was to annotate the planarian proteome and mucous sub-proteome. Using a proteogenomics approach, I elucidated the proteome and mucous subproteome via mass spectrometry together with an in silico translated transcript database. I identified 1604 proteins, which were annotated using the Swiss-Prot BLAST algorithm and Gene Ontology analysis. The S. mediterranea proteome is highly similar to that predicted for the trematode Schistosoma mansoni associated with schistosomiasis. Remarkably, orthologs of 119 planarian mucous proteins are present in human mucosal secretions and tear fluid. I suggest planarians have potential to be a model system for parasitic worms and diseases underlined by mucous aberrancies.

Chemical genomics is an interdisciplinary field that combines small molecule perturbation with genomics to understand gene function and to study the mode(s) of drug action. Existing methods for correlating chemical genomic profiles are not ideal as they often require one to define the disrupting effects, commonly known as batch effects. These effects are not always known, and they can mask true biological differences. I present a method, Bucket Evaluations (BE), which surmounts these problems. This method is a non-parametric correlation approach, which is suitable for locating correlations in somewhat perturbed datasets such as chemical genomic profiles. BE can be used on other datasets such as those obtained via gene expression profiling and performs well on both array-based and sequence based readouts. Using BE, along with various correlation methods, on a collection of datasets, showed it to be highly accurate for locating similarity between experiments.

Chemical genomics is an interdisciplinary field that combines small molecule perturbation with genomics to understand gene function and to study the mode(s) of drug action. Existing methods for correlating chemical genomic profiles are not ideal as they often require one to define the disrupting effects, commonly known as batch effects. These effects are not always known, and they can mask true biological differences. I present a method, Bucket Evaluations (BE), which surmounts these problems. This method is a non-parametric correlation approach, which is suitable for locating correlations in somewhat perturbed datasets such as chemical genomic profiles. BE can be used on other datasets such as those obtained via gene expression profiling and performs well on both array-based and sequence based readouts. Using BE, along with various correlation methods, on a collection of datasets, showed it to be highly accurate for locating similarity between experiments.