Non-coding RNAs (ncRNAs) are transcripts that function directly as RNA molecule without ever being translated to protein. The transcriptional output of eukaryotic cells is diverse, pervasive, and multi-layered. It consists of spliced as well as unspliced transcripts of both protein-coding messenger RNAs and functional ncRNAs. However, it also contains degradable non-functional by-products and artefacts - certainly a reason why ncRNAs have long been wrongly disposed as transcriptional noise. Today, RNA-controlled regulatory processes are broadly recognized for a variety of ncRNA classes. The thermoresponsive ROSE ncRNA (repression of heat shock gene expression) is only one example of a regulatory ncRNA acting at the post-transcriptional level via conformational changes of its secondary structure. Bioinformatics helps to identify novel ncRNAs in the bulk of genomic and transcriptomic sequence data which are produced at ever increasing rates. However, ncRNA annotation is unfortunately not part of generic genome annotation pipelines. Dedicated computational searches for particular ncRNAs are veritable research projects in their own right. Despite best efforts, ncRNAs across the animal phylogeny remain to a large extent uncharted territory. This thesis describes a comprehensive collection of exploratory bioinformatic field studies designed to de novo predict ncRNA genes in a series of computational screens and in a multitude of newly sequenced genomes. Non-coding RNAs can be divided into subclasses (families) according to peculiar functional, structural, or compositional similarities. A simple but eligible and frequently applied criterion to classify RNA species is length. In line, the thesis is structured into two parts: We present a series of pilot-studies investigating (1) the short and (2) the long ncRNA repertoire of several model species by means of state-of-the-art bioinformatic techniques. In the first part of the thesis, we focus on the detection of short ncRNAs exhibiting thermodynamically stable and evolutionary conserved secondary structures. We provide evidence for the presence of short structured ncRNAs in a variety of different species, ranging from bacteria to insects and higher eukaryotes. In particular, we highlight drawbacks and opportunities of RNAz-based ncRNA prediction at several hitherto scarcely investigated scenarios, as for example ncRNA prediction in the light of whole genome duplications. A recent microarray study provides experimental evidence for our approach. Differential expression of at least one-sixth of our drosophilid RNAz predictions has been reported. Beyond the means of RNAz, we moreover manually compile sophisticated annotation of short ncRNAs in schistosomes. Obviously, accumulating knowledge about the genetic material of malaria causing parasites which infect millions of humans world-wide is of utmost scientific interest. Since the performance of any comparative genomics approach is limited by the quality of its input alignments, we introduce a novel light-weight and performant genome-wide alignment approach: NcDNAlign. Although the tool is optimized for speed rather than sensitivity and requires only a minor fraction of CPU time compared to existing programs, we demonstrate that it is basically as sensitive and specific as competing approaches when applied to genome-wide ncRNA gene finding and analysis of ultra-conserved regions. By design, however, prediction approaches that search for regions with an excess of mutations that maintain secondary structure motifs will miss ncRNAs that are unstructured or whose structure is not well conserved in evolution. In the second part of the thesis, we therefore overcome secondary structure prediction and, based on splice site detection, develop novel strategies specifically designed to identify long ncRNAs in genomic sequences - probably the open problem in current RNA research. We perform splice site anchored gene-finding in drosophilids, nematodes, and vertebrate genomes and, at least for a subset of obtained candidate genes, provide experimental evidence for expression and the existence of novel spliced transcripts undoubtedly confirming our approach. In summary, we found evidence for a large number of previously undescribed RNAs which consolidates the idea of non-coding RNAs as an abundant class of regulatory active transcripts. Certainly, ncRNA prediction is a complex task. This thesis, however, rationally advises how to unveil the RNA complement of newly sequenced genomes. Since our results have already established both subsequent computational as well as experimental studies, we believe to have enduringly stimulated the field of RNA research and to have contributed to an enriched view on the subject.

As sequencing technologies advance genome studies are becoming a basic tool for studying an organism, and with more genomes available comparative genomics is maturing into a powerful tool for biological research. This thesis demonstrates the strength of a comparative genomics approach on a group of understudied eukaryotes, the diplomonads. Diplomonads are a group of single cell eukaryotic flagellates living in oxygen-poor environments. Most diplomonads are intestinal parasites, like the well-studied human parasite Giardia intestinalis. There are seven different G. intestinalis assemblages (genotypes) affecting different hosts, and it’s under debate whether these are one species. A genome-wide study of three G. intestinalis genomes from different assemblages reveals little inter-assemblage sexual recombination, supporting that the different G. intestinalis assemblages are genetically isolated and thus different species. A genomic comparison between the fish parasite S. salmonicida and G. intestinalis reveals genetic differences reflecting differences in their parasitic lifestyles. There is a tighter transcriptional regulation and a larger metabolic reservoir in S. salmonicida, likely adaptations to the fluctuating environments it encounters during its systemic infection compared to G. intestinalis which is a strict intestinal parasite. The S. salmonicida genome analysis also discovers genes involved in energy metabolism. Some of these are experimentally shown to localize to mitochondrion-related organelles in S. salmonicida, indicating that they possess energy-producing organelles that should be classified as hydrogenosomes, as opposed to the mitosomes in G. intestinalis. A transcriptome analysis of the free-living Trepomonas is compared with genomic data from the two parasitic diplomonads. The majority of the genes associated with a free-living lifestyle, like phagocytosis and a larger metabolic capacity, are of prokaryotic origin. This suggests that the ancestor of the free-living diplomonad was likely host-associated and that the free-living lifestyle is a secondary adaptation acquired through horizontal gene transfers.  In conclusion, this thesis uses different comparative genomics approaches to broaden the knowledge on diplomonad diversity and to provide more insight into how the lifestyle differences are reflected on the genetic level. The bioinformatics pipelines and expertise gained in these studies will be useful in other projects in diplomonads and other organismal groups.

Black Cottonwood (Populus trichocarpa) is a fast-growing, economically important tree species. P. trichocarpa was the first tree to have its genome fully sequenced and is considered the model organism for genomic research in trees. Of the macronutrients in plants, Nitrogen (N) is required in the greatest amounts and is generally the limiting nutrient in terrestrial ecosystems. Inorganic N-transport is performed by four families of transporter proteins, AMT1 and AMT2 for ammonium (NH4+) and NRT1 and NRT2 for nitrate (NO3-). I have created phylogenetic reconstructions of each of these transporter families in 22 members of Viridiplantae whose genomes have been fully sequenced. Based on these phylogenies, I have introduced a new classification system for the transporter families that better represents the evolutionary and functional relatedness of the proteins. These phylogenies were supplemented with topology predictions, subcellular localization predictions, and in silico expression profiling in order to suggest functional characterization of the groups. This facilitated candidate gene selection for NH4+ and NO3- uptake transporters from P. trichocarpa. Expression profiling was performed on two of these candidates. Results suggest that PtAMT1-1 may be a high-affinity, root-localized NH4+ transporter. In contrast, PtNRT2-6 is a high-affinity NO3- transporter localized to the dormant bud, but its physiological functions remain largely enigmatic. Flux profiles of NH4+, NO3-, and H+ in the first 1.4 cm of root tips of three-week-old P. trichocarpa seedlings and cuttings were measured using the Microelectrode Ion Flux mEasurement (MIFE) system to demonstrate the activity of AMTs and NRTs under nutrient-abundant and nutrient-deficient conditions. I found mainly N-efflux from roots of cuttings while seedling roots exhibited N-uptake. This is the first report of such a difference. This highlights an unexpected but clear physiological difference between seedling and cutting roots, which are frequently used in experimental setups. / Graduate / 0817 / 0369 / 0715 / neilvonw@gmail.com

Comparative genomics provides a tool to investigate large biological datasets, i.e. genomic datasets. In my thesis I focused on inferring patterns of selection in coding and non-coding regions of avian genomes. Until recently, large comparative studies on selection were mainly restricted to model species with sequenced genomes. This limitation has been overcome with advances in sequencing technologies and it is now possible to gather large genomic data sets for non-model species.  Next-generation sequencing data was used to study patterns of nucleotide substitutions and from this we inferred how selection has acted in the genomes of 10 non-model bird species. In general, we found evidence for a negative correlation between neutral substitution rate and chromosome size in birds. In a follow up study, we investigated two closely related bird species, to study expression levels in different tissues and pattern of selection. We found that between 2% and 18% of all genes were differentially expressed between the two species. We showed that non-coding regions adjacent to genes are under evolutionary constraint in birds, which suggests that noncoding DNA plays an important functional role in the genome. Regions downstream to genes (3’) showed particularly high level of constraint. The level of constraint in these regions was not correlated to the length of untranslated regions, which suggests that other causes play also a role in sequence conservation. We compared the rate of nonsynonymous substitutions to the rate of synonymous substitutions in order to infer levels of selection in protein-coding sequences. Synonymous substitutions are often assumed to evolve neutrally. We studied synonymous substitutions by estimating constraint on 4-fold degenerate sites of avian genes and found significant evolutionary constraint on this category of sites (between 24% and 43%). These results call for a reappraisal of synonymous substitution rates being used as neutral standards in molecular evolutionary analysis (e.g. the dN/dS ratio to infer positive selection). Finally, the problem of sequencing errors in next-generation sequencing data was investigated. We developed a program that removes erroneous bases from the reads. We showed that low coverage sequencing projects and large genome sequencing projects will especially gain from trimming erroneous reads.

Autoimmune diseases are endemic, but the disease mechanisms are poorly understood. A way to better understand these are to find disease-regulating genes. However, this is difficult as the diseases are complex, with several genes as well as environmental factors influencing the development of disease. A way to facilitate the search for genes responsible for the diseases is to use comparative genomic studies. Animal models are relatively easy to analyze since control of environment and breeding are obtained. The University of California at Davies – line 200 (UCD-200) chickens have a hereditary disease that is similar to systemic sclerosis. Using a backcross between UCD-200 chickens and red junglefowl (RJF) chickens we identified three loci linked to the disease. The loci contained immune-regulatory genes suggested to be involved in systemic sclerosis in humans, as well as a previously unidentified linkage between systemic sclerosis in UCD-200 chickens and IGFBP3. The Dark brown (Db) gene enhances red pheomelanin and restricts expression of eumelanin in chickens. The Db phenotype is regulated by an 8 kb deletion upstream of SOX10. Pigmentation studies are potentially useful when trying to identify pathogenic mechanisms and candidate genes in vitiligo The Obese strain (OS) of chickens spontaneously develops an autoimmune thyroiditis which closely resembles human Hashimoto’s thyroiditis. By using an intercross between OS chickens and RJF chickens, we found several disease phenotypes that can be used in an ongoing linkage analysis with the goal to find candidate genes for autoimmune disease. An important phenotype to record and add to the linkage analysis is autoantibodies against thyroid peroxidase, since this phenotype is a key feature in Hashimoto’s thyroiditis. Previous attempts to measure these titres in OS chickens have failed, hence an assay was developed for this purpose.

The genome sequencing projects have generated a wealth of genomic data and the analysis of this data has provided many interesting findings. However, genome wide analysis of bacteria for promoters has lagged behind, because it has been difficult to accurately predict the promoters with so much background noise that are found in bacterial genomes. One approach to overcome this problem is to predict phylogenetically conserved promoters across multiple genomes of different bacteria, thus filtering out many of the false positives, which are predicted by the current methods. However, there are no programmes capable of doing this. Therefore, the work presented in this thesis has developed a position weight matrix (PWM) based programme called Multiscan that predicts conserved promoters across multiple bacterial genomes. Since Chlamydia is one of the most sequenced bacterial genera and has a high level of conservation of genes and large-scale conservation of gene order between species, Multiscan was developed and tested on Chlamydia. When Multiscan analysed a genome wide dataset of equivalent non-coding regions (NCRs) upstream of genes, from Chlamydia trachomatis, Chlamydia pneumoniae and Chlamydia caviae for σ66 promoters that are phylogenetically conserved, Multiscan predicted 42 promoters. Since only one of the 42 promoters predicted by Multiscan had previously available biological data to confirm its prediction, an additional subset of 10 of the remaining 41 σ66 promoters were analysed in C. trachomatis by mapping the 5' end of the transcripts. The primer extension assay synthesised cDNA products of the correct length for seven of the 10 genes chosen. When the performance of Multiscan was compared to one of the accepted method for genome wide prediction of promoters in bacteria, the &quotstandard PWM method", Multiscan predicted 32 more promoters than the &quotstandard PWM method" in Chlamydia. Furthermore, the promoters predicted by Multiscan were up to three more mismatches from the Escherichia coli σ70 consensus sequence than the promoters predicted by the standard PWM method. Although Multiscan predicted 42 promoters that were well conserved across the three chlamydial species, the analysis was unable to identify the 14 known σ66 promoters in C. trachomatis. These promoters were missed (1) because they were dissimilar to the E. coli σ70 consensus sequence and/or (2) because the promoters were poorly conserved across the three chlamydial species. To address the second possibility, the 14 false negatives were analysed by another phylogenetic footprinting method. Fourteen sets of equivalent NCRs located upstream of the homologous genes from the three chlamydiae were aligned with the computer programme Clustal W and the alignment analysed &quotby eye" for evidence of phylogenetic footprints containing the 14 false negatives. The analysis identified that seven of the 14 false negatives were poorly conserved across the chlamydial species. Analysis of two of the seven promoters that could not be footprinted, the promoters of ltuA and ltuB, by mapping the transcriptional start sites in C. caviae, confirmed their poor conservation across C. trachomatis and C. caviae. This analysis showed that substantial differences exist in chlamydial σ66 promoters from equivalent NCRs upstream of genes. This study has developed a new computer programme for genome wide prediction of promoters that are phylogenetically conserved and has shown the value of this programme by identifying seven new well conserved promoters and seven candidate poorly conserved promoters in Chlamydia.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.