<p>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.</p><p>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</p><p>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].</p><p>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</p><p>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.</p><p>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.</p>

<p>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.</p>

Diss. - Abo Akademi University, 2008. / Includes bibliographical references.

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This thesis presents several developments on Variance component (VC) approach for Quantitative Trait Locus (QTL) mapping. The first part consists of methodological improvements: a new fast and efficient method for estimating IBD matrices, have been developed. The new method makes a better use of the computer resources in terms of computational power and storage memory, facilitating further improvements by resolving methodological bottlenecks in algorithms to scan multiple QTL. A new VC model have also been developed in order to consider and evaluate the correlation of the allelic effects within parental lines origin in experimental outbred crosses. The method was tested on simulated and experimental data and revealed a higher or similar power to detect QTL than linear regression based QTL mapping. The second part focused on the prospect to analyze multi-generational pedigrees by VC approach. The IBD estimation algorithm was extended to include haplotype information in addition to genotype and pedigree to improve the accuracy of the IBD estimates, and a new haplotyping algorithm was developed for limiting the risk of haplotyping errors in multigenerational pedigrees. Those newly developed methods where subsequently applied for the analysis of a nine generations AIL pedigree obtained after crossing two chicken lines divergently selected for body weight. Nine QTL described in a F2 population were replicated in the AIL pedigree, and our strategy to use both genotype and phenotype information from all individuals in the entire pedigree clearly made efficient use of the available genotype information provided in AIL.

The regulation of gene expression is driven primarily by transcription factors binding to short DNA sequences. Here three studies related to promoter cis-regulatory motif discovery in plant promoters are presented. In the first study, an exact discriminative seeding DNA motif discovery addressing key issues associated with popular DNA motif discovery algorithms is proposed. The Seeder algorithm outperforms popular motif discovery tools on biological benchmark data. In the second study, the algorithm is applied to the identification of cis-regulatory motifs in seed storage protein gene promoters. Known and new motifs are discovered. In the third study, groups of orthologous genes are identified among five dicotyledonous plant species, and DNA motif discovery is carried out in the proximal promoter sequence within each group. The presence of three large clusters of groups of orthologous promoters sharing similar motifs is revealed. / L'expression des gènes est régulée, en grande partie, par la liaison des facteurs de transcription à de courtes séquences d'ADN. Trois études sont présentées, portant sur l'identification in silico de motifs régulateurs dans les séquences promotrices de gènes végétaux. Dans la première étude, un algorithme d'initiation discriminative exacte est présenté. L'algorithme surpasse plusieurs algorithmes populaires lorsque appliqué à des données biologiques de référence. Dans la deuxième étude, l'algorithme est utilisé pour l'identification de motifs cis-régulateurs conservés dans les promoteurs de gènes de protéines de réserve des graines chez diverses espèces végétales. Des motifs connus ainsi que de nouveaux motifs sont identifiés. Dans la troisième étude, des groupes de gènes orthologues sont identifiés chez cinq espèces dicotylédones, et une recherche de motifs cis-régulateurs est réalisée dans les séquences promotrices proximales pour chaque groupe. La présence de trois larges grappes de groupes d'orthologues partageant des motifs similaires est mise en évidence.

Chemokines belong to a family of important chemoattractants that guide the directional migration of the cell. The cognate chemokine receptor on the cell senses the chemokine gradient and the cell moves towards the signal of increasing chemokine concentration. However, several chemokine receptors were recently identified as non-signaling (decoy), based on their ability to bind the chemokine but produce no measurable signal for the cell. The function of these decoy receptors is yet unknown. We hypothesized that the ligand binding by the decoy receptor may help maintaining a sharper chemokine gradient and thus stimulate the cell migration. We first assessed if the expression of decoy and corresponding signaling receptors changes when cancer cells acquire migratory phenotype – become metastatic. Using publically available database of gene expression in normal prostate, carcinoma and metastatic prostate cancer samples, we have found that the expression of decoy receptors CCX-CKR and OPG increased in metastatic cancer cells compared to normal prostate and positively correlated with the expression of signaling receptors CCR7 and RANK respectively. We next developed mathematical model that described the dynamics of chemokine ligand, normal receptor and decoy receptor as well as subsequent cell movement. Using this model we first assessed how the cells expressing signaling receptors only migrate towards the source of ligand given at different concentrations. At low levels of ligand, cell migration increased with the increase in ligand concentration. However, at higher concentrations, when the ligand levels exceeded the signaling receptor capacity, further increase in ligand resulted in the decrease the distance of cell migration. Importantly, at high levels of ligand the presence of the decoy receptor improved the speed and distance of cell migration. This study suggests the novel function for the non-signaling chemokine receptors in maintaining the chemokine gradient and positively regulating directional cell migration. / Les chimiokines appartiennent à une importante famille de ligands chimiotactiques qui guident la direction migratoire des cellules. Sur une cellule-cible, des récepteurs spécifiques à une chimiokine donnée répondent à un gradient du ligand, provoquant la migration cellulaire vers le signal avec une concentration croissante. Cependant, quelques récepteurs pouvant liés des chimiokines ont récemment été identifiés comme muets (leurre) parce que la liaison du ligand ne stimule pas de signalisation mesurable dans la cellule. La fonction de ces récepteurs-leurres n'est pas connue actuellement.Nous avons émis l'hypothèse que l'interaction des chimiokines à ces récepteurs-leurres contribue à maintenir un gradient de ligand plus prononcé et donc stimule les cellules à migrer. Afin de tester cette hypothèse, nous avons en premier comparé l'expression de récepteurs signalant et de récepteurs-leurres pour un même ligand, quand des cellules deviennent métastatiques. En utilisant des bases de données publiques sur l'expression des gènes dans des échantillons de prostate normale, de carcinomes prostatiques, et de métastases prostatiques, nous avons remarqué que l'expression des récepteurs-leurres CCX-CKR et OPG est augmentée dans les cellules métastatiques lorsque comparée avec les cellules de prostate normales. Nous avons aussi trouvé une corrélation positive avec les niveaux d'expression des récepteurs signalants CCR7 et RANK. Par la suite, nous avons développé un modèle mathématique qui prédit la dynamique des concentrations de chimiokines, l'expression des récepteurs signalants, des récepteurs-leurres, et des mouvements de la cellule résultants. Nous avons tout d'abord utilisé ce modèle afin de prédire comment des cellules exprimant seulement des récepteurs signalant migrent vers la source du ligand selon sa concentration. En présence de faibles concentrations de ligand, la migration cellulaire augmente proportionnellement à l'augmentation de la concentration du ligand. Cependant, à des concentrations plus élevées dépassant la capacité de liaison du récepteur signalant, une augmentation subséquente diminue la distance migrée par la cellule. L'expression concomittante de récepteurs-leurres améliore la vitesse et la distance de la migration cellulaire lorsque la concentration du ligand est élevée. Cette étude suggère donc que les récepteurs-leurres des chimiokines contribuent au gradient chimiotactique et augmentent la migration des cellules.

Bioinformatics is a crucial component of genomics research because it enables the analyses of large and complex data sets. Conventionally, these analyses involve the use of sophisticated software, and are largely performed by those with prior experience in bioinformatics using adequate computational resources.Massively parallel DNA sequencing (MPS) platforms have democratized genome sequencing, making it affordable to the biologist. For many biologists this will be their first venture into bioinformatics and genomics. Consequently, they may be unfamiliar with bioinformatics or lack the necessary computer resources. For these biologists, the potential of using MPS platforms for genome analysis is half fulfilled; providing affordable genomic data without the means to easily analyze it. One approach to close this gap is to build software oriented towards those with limited bioinformatics expertise or resources.This dissertation describes a paradigm to close the gap between genome analysis and the biologist. Using this paradigm, I have developed software tools for three bioinformatics tasks in genome analysis: [i] assessment of a genome assembly, [ii] display and integrated analysis of genomic data, and [iii] deriving biological insight using public information. The first tool I developed was cgb, a program that creates custom UCSC Genome Browsers, allowing biologists to use this browser for genome sequences obtained from MPS platforms. Using cgb for a comparative genomics study of Clostridium difficile assisted us to identify diagnostic DNA markers associated with disease severity and to estimate that the pan-genome is larger than previously estimated. Next I developed contiGo, a general purpose tool to inspect genome assemblies via a web browser, thus bypassing the need for the biologist to install software, satisfy hardware requirements, and download large datasets. Along with cgb, this program enabled us to evaluate the performance of the Roche/454 Genome Sequencer-FLX MPS platform across five sequencing core facilities, and to produce a high quality genome sequence of the fungus Ophiostoma novo-ulmi. Lastly, I developed BL!P, a program to automate NCBI BLAST searches and explore the results in a dynamic interface. This program was inspired by my work on characterizing the genome of a multi-drug resistant and pathogenic strain of Escherichia fergusonii, for which cgb and contiGo were also used in data analysis. These applications have been used in other genomics projects by users with a range of bioinformatics expertise and resources. Other data-intensive fields of science could benefit from a similar software development paradigm. / La bioinformatique fait maintenant partie intégrante de la recherche en génomique, car elle permet des analyses de bases de données larges et complexes. Conventionnellement, ces analyses impliquent l'utilisation de logiciels sophistiqués et sont généralement faites par des personnes expérimentées en bioinformatique qui utilisent des ressources informatiques adéquates.Les plateformes de séquençage haut débit d'ADN ont démocratisé le séquençage du génome, le rendant ainsi accessible aux biologistes. Pour de nombreux biologistes, ce sera leur première incursion dans les domaines de la bioinformatique et de la génomique. Par conséquent, ils ne sont probablement pas familiers avec la bioinformatique ou n'ont pas les ressources informatiques nécessaires afin d'analyser les résultats. Pour ces biologistes, l'utilisation des plateformes de séquençage haut débit permet l'obtention abordable de données génomiques, mais n'offre pas les outils pour les analyser facilement. Le développement de logiciels ciblant les chercheurs ayant une expertise en bioinformatique limitée ou avec peu de ressources permettrait de combler cet écart.Cette dissertation décrit un paradigme visant à réduire, voire même à fermer, l'écart entre l'analyse du génome et le biologiste. En utilisant ce paradigme, j'ai développé des outils informatiques pour trois tâches facilitant l'analyse génomique : [i] l'évaluation de l'assemblage du génome, [ii] l'affichage et l'analyse intégrée des données génomiques, et [iii] l'obtention de connaissances biologiques utilisant de l'information publique. Le premier outil que j'ai développé était cgb, un programme qui crée des navigateurs personnalisés « UCSC Genome ». Il permet aux biologistes d'utiliser ces navigateurs pour évaluer les séquences obtenues à partir de plateformes de séquençage haut débit. L'utilisation de cgb lors d'une étude génomique comparative de Clostridium difficile nous a permis d'identifier des marqueurs diagnostics d'ADN associés à la gravité de la maladie et de démontrer que son pan-génome est plus grand qu'estimé précédemment. Ensuite, j'ai développé contiGo, un outil d'usage général pour réviser les assemblages de séquences génomiques par l'intermédiaire d'un navigateur web. Cette application permet aux biologistes de contourner la nécessité d'installer un logiciel, de satisfaire les exigences de l'équipement informatique, et de télécharger des larges bases de données. Conjointement avec cgb, ce programme nous a permis d'évaluer la performance de la plateforme de séquençage haut débit Roche/454 Genome Sequencer FLX, à travers cinq installations de séquençage, ainsi qu'à générer une séquence génomique de grande qualité du champignon Ophiostoma novo-ulmi. Finalement, j'ai développé BL!P, un programme pour automatiser les recherches BLAST NCBI et pour explorer les résultats obtenus dans une interface dynamique. Ce programme a été inspiré par mon travail sur la caractérisation du génome d'une souche pathogène et multi résistante d'Escherichia fergusonii, et pour laquelle cgb et contiGo ont également été utilisés dans l'analyse des données. Ces applications ont été utilisées dans d'autres projets de génomique par des utilisateurs possédant un éventail de compétences et de ressources bioinformatiques. D'autres domaines scientifiques générant des multitudes de données pourraient bénéficier d'un paradigme similaire de développement de logiciel informatique.

<p> Advances in sequencing technology have resulted in the sequencing of whole genomes from many simple organisms such as fungi and bacteria, while allowing the assembly of much more complex genomes like humans and chimpanzees. Consequently, association of segments of newly sequenced genomes to specific function (i.e. annotation) is being completed by comparative study of protein coding regions from previously annotated genome data. While this is an ideal procedure to process and annotate huge number of available genomic sequences, this approach can potentially lead to propagating erroneous annotation in a public sequence repository and vastly diminish the integrity of these new annotation of genome sequences. In this project, the WRongly Annotated Protein identification System (WRAPS) has been created to analyze previously annotated proteins quickly and efficiently. The likeliness that the protein is correctly annotated is determined by weighted scoring schema based on conservation of protein domain, the domains present in different reading frames, and isoelectric point. A study of 88,023 proteins of Yersinia, Staphylococcus, and Bacillus using WRAPS show that there are several proteins that can be considered wrongly annotated, as well as the correctness of annotation among these proteins. </p>