The success of microbial life on Earth can be attributed not only to environmental factors, but also to the surprising hardiness, adaptability and flexibility of the microbes themselves. They are able to quickly adapt to new niches or circumstances through gene evolution and also by sheer strength of numbers, where statistics favor otherwise rare events. An integral part of adaptation is the plasticity of the genome; losing and acquiring genes depending on whether they are needed or not. Genomes can also be the birthplace of new gene functions, by duplicating and modifying existing genes. Genes can also be acquired from outside, transcending species boundaries. In this work, the focus is set primarily on duplication, deletion and import (lateral transfer) of genes – three factors contributing to the versatility and success of microbial life throughout the biosphere. We have developed a compositional method of identifying genes that have been imported into a genome, and the rate of import/deletion turnover has been appreciated in a number of organisms. Furthermore, we propose a model of genome evolution by duplication, where through the principle of gene amplification, novel gene functions are discovered within genes with weak- or secondary protein functions. Subsequently, the novel function is maintained by selection and eventually optimized. Finally, we discuss a possible synergic link between lateral transfer and duplicative processes in gene innovation.

Functional genomics refers to the task of determining gene and protein function for whole genomes, and requires computational analysis of large amounts of biological data including DNA and protein sequences, protein structures and gene expressions. Machine learning methods provide a powerful tool to this end by first inducing general models from such data and already characterized genes or proteins and then by providing hypotheses on the functions of the remaining, uncharacterized cases. This study contains four parts giving novel contributions to functional genomics through the analysis of different biological data and different aspects of biological functions. Gene Ontology played an important part in this research providing a controlled vocabulary for describing the cellular roles of genes and proteins in terms of specific molecular functions and broad biological processes. The first part used gene expression time profiles to learn models capable of predicting the participation of genes in biological processes. The model consists of IF-THEN rules associating biological processes with minimal set of discrete changes in expression level over limited periods of time. The models were used to hypothesize new biological processes for both characterized and uncharacterized genes. The second part investigated the combinatorial nature of gene regulation by inducing IF-THEN rules associating minimal combinations of sequence motifs common to genes with similar expression profiles. Such combinations were shown to be significantly correlated to function, and provided hypotheses on the mechanisms behind the regulation of gene expression in several biological responses. The third part used a novel concept of local descriptors of protein structure to investigate sequence patterns governing protein structure at a local level and to predict the topological class (fold) of protein domains from sequence. Finally, the fourth part used local descriptors to represent protein structure and induced IF-THEN rule models predicting molecular function from structure.

<p>Methods using oligonucleotide probes are powerful tools for the analysis of nucleic acids. During recent years, many such methods have been developed that enable the simultaneous interrogation of multiple qualities of a sample. Many of these multiplexing techniques share common limitations. This thesis discusses new developments to overcome the problems of multiplex amplification of genomic sequences and design of sets of oligonucleotide probes for multiplex genetic analyses.</p><p>A novel molecular technique, termed the selector method, is described. This method allows circularization of an arbitrary selection of restriction fragments from genomic DNA, and the subsequent amplification of these circular products in parallel using common primers. The utility of the method is demonstrated by a 96-plex amplification experiment. Furthermore, the PieceMaker software for selection of restriction enzymes and restriction fragments is described. These two developments allow the selective amplification of subsets of genomes for further analyses.</p><p>A software tool for the design of sequence-tagged oligonucleotide probes is presented. The ProbeMaker software is a framework for design of sets of probes composed of separate functional elements, and uses an extension mechanism to incorporate support for new probe types as needed. An approach to a unified system for oligonucleotide design is also presented. This system will serve to decrease development times for new oligonucleotide design applications by allowing extensive code reuse.</p>

Methods using oligonucleotide probes are powerful tools for the analysis of nucleic acids. During recent years, many such methods have been developed that enable the simultaneous interrogation of multiple qualities of a sample. Many of these multiplexing techniques share common limitations. This thesis discusses new developments to overcome the problems of multiplex amplification of genomic sequences and design of sets of oligonucleotide probes for multiplex genetic analyses. A novel molecular technique, termed the selector method, is described. This method allows circularization of an arbitrary selection of restriction fragments from genomic DNA, and the subsequent amplification of these circular products in parallel using common primers. The utility of the method is demonstrated by a 96-plex amplification experiment. Furthermore, the PieceMaker software for selection of restriction enzymes and restriction fragments is described. These two developments allow the selective amplification of subsets of genomes for further analyses. A software tool for the design of sequence-tagged oligonucleotide probes is presented. The ProbeMaker software is a framework for design of sets of probes composed of separate functional elements, and uses an extension mechanism to incorporate support for new probe types as needed. An approach to a unified system for oligonucleotide design is also presented. This system will serve to decrease development times for new oligonucleotide design applications by allowing extensive code reuse.

In this thesis, nutrient signalling in yeast is used as a model to study several features of gene regulation, such as combinatorial gene regulation, the role of motif context and chromatin modifications. The nutrient signalling system in yeast consists of several pathways that transmit signals about the availability of key nutrients, and regulate the transcription of a large part of the genome. Some of the signalling pathways are also conserved in other eukaryotic species where they are implicated in processes such as aging and in human disease.   Combinatorial gene regulation is examined in papers I and II. In paper I, the role of Mig1, Mig2 and Mig3 is studied. To elucidate how the three proteins contribute to the control of gene expression, we used microarrays to study the expression of all yeast genes in the wild type and in all seven possible combinations of mig1, mig2 and mig3 deletions. In paper II, a similar strategy is used to investigate Gis1 and Rph1, two related transcription factors. Our results reveal that Rph1 is involved in nutrient signalling together with Gis1, and we find that both the activities and the target specificities of Gis1 and Rph1 depend on the growth phase.   Paper III describes ContextFinder, a program for identifying constraints on sequence motif locations and orientations. ContextFinder was used to analyse over 300 cases of motifs that are enriched in experimentally selected groups of yeast promoters. Our results suggest that motif context frequently is important for stable DNA binding and/or regulatory activity of transcription factors.   In paper IV, we investigated how gene expression changes resulting from nitrogen starvation are accompanied by chromatin modifications. Activation of gene expression is concentrated to specific genomic regions. It is associated with nucleosome depletion (in both promoters and coding regions) and increased levels of H3K9ac (but not H4K5ac).

Background: In P. falciparum a similarity between the transit peptides of apicoplast and mitochondrial proteins in the context of net positive charge has previously been observed in few proteins. Existing P. falciparum protein localization prediction tools were leveraged in this study to study this similarity in larger sets of these proteins. Results: The online public-domain malarial repository PlasmoDB was utilized as the source of apicoplast and mitochondrial protein sequences for the similarity study of the two types of transit peptides. It was found that many of the 551 apicoplast-targeted proteins (NEAT proteins) of PlasmoDB may have been wrongly annotated to localize to the apicoplast, as some of these proteins lacked annotations for signal peptides, while others also had annotations for localization to the mitochondrion (NEMT proteins). Also around 50 NEAT proteins could contain signal anchors instead of signal peptides in their N-termini, something that could have an impact on the current theory that explains localization to the apicoplast [1]. The P. falciparum localization prediction tools were then used to study the similarity in net positive charge between the transit peptides of NEAT and NEMT proteins. It was found that NEAT protein prediction tools like PlasmoAP and PATS could be made to recognize NEMT proteins as NEAT proteins, while the NEMT predicting tool PlasMit could be made to recognize a significant number of NEAT proteins as NEMT. Based on these results a conjecture was proposed that a single technique may be sufficient to predict both apicoplast and mitochondrial transit peptides. An implementation in PERL called TargetPf was implemented to test this conjecture (using PlasmoAP rules), and it reported a total of 408 NEAT proteins and 1504 NEMT proteins. This number of predicted NEMT proteins (1504) was significantly higher than the annotated 258 NEMT proteins of plasmoDB, but more in line with the 1200 predictions of the tool PlasMit. Conclusions: Some possible ambiguities in the PlasmoDB annotations related to NEAT protein localization were identified in this study. It was also found that existing P. falciparum localization prediction tools can be made to detect transit peptides for which they have not been trained or built for.

Proteins combine in various ways to execute different essential functions. Cellular processes are enormously complex and it is a great challenge to explain the underlying organization. Various methods have been applied in attempt to reveal the organization of the cell. Gene expression analysis uses the mRNA levels in the cell to predict which proteins are present in the cell simultaneously. This method is useful but also known to sometimes fail. Proteins that are known to be functionally related do not always show a significant correlation in gene expression. This fact might be explained by the dynamic organization of the proteome. Proteins can have diverse functions and might interact with some proteins only during a few time points, which would probably not result in significant correlation in their gene expression. In this work we tried to address this problem by combining gene expression data with data for physical interactions between proteins. We used a method for modular decomposition introduced by Gagneur et al. (2004) that aims to reveal the logical organization in protein-protein networks. We extended the interpretation of the modular decomposition to localize the dynamics in the protein organization. We found evidence that protein-interactions supported by gene expression data are very likely to be related in function and thus can be used to predict function for unknown proteins. We also identified negative correlation in gene expression as an overlooked area. Several hypotheses were generated using combination of these methods. Some could be verified by the literature and others might shed light on new pathways after additional experimental testing.

The number of experimentally determined protein structures in the protein data bank (PDB) is continuously increasing. The common features like; cellular location, function, topology, primary structure, secondary structure, tertiary structure, domains or fold are used to classify them. Therefore, there are various methods available for classification of proteins. In this work we are attempting an additional method for making appropriate classification, i.e. solvent energy. Solvation is one of the most important properties of macromolecules and biological membranes by which they remain stabilized in different environments. The energy required for solvation can be measured in term of solvent energy. Proteins from similar environments are investigated for similar solvent energy. That is, the solvent energy can be used as a measure to analyze and classify proteins. In this project solvent energy of proteins present in the Protein Data Bank (PDB) was calculated by using Jones’ algorithm. The proteins were classified into two classes; transmembrane and globular. The results of statistical analysis showed that the values of solvent energy obtained for two main classes (globular and transmebrane) were from different sets of populations. Thus, by adopting classification based on solvent energy will definitely help for prediction of cellular placement.

The topic of gene regulation is a major research area in the bioinformatics community. In this thesis prior knowledge from Gene Ontology in the form of templates is integrated into the fitness function of an evolutionary algorithm to predict gene regulatory networks. The resulting multi-objective fitness functions are then tested with MAPK network data taken from KEGG to evaluate their respective performances. The results are presented and analyzed. However, a clear tendency cannot be observed. The results are nevertheless promising and can provide motivation for further research in that direction. Therefore different ideas and approaches are suggested for future work.

State-of-the-art methods for topology of α-helical membrane proteins are based on the use of time-consuming multiple sequence alignments obtained from PSI-BLAST or other sources. Here, we examine if it is possible to use the consensus of topology prediction methods that are based on single sequences to obtain a similar accuracy as the more accurate multiple sequence-based methods. Here, we show that TOPCONS-single performs better than any of the other topology prediction methods tested here, but ~6% worse than the best method that is utilizing multiple sequence alignments. AVAILABILITY AND IMPLEMENTATION: TOPCONS-single is available as a web server from http://single.topcons.net/ and is also included for local installation from the web site. In addition, consensus-based topology predictions for the entire international protein index (IPI) is available from the web server and will be updated at regular intervals.