A coccidiose aviária é uma doença entérica causada por protozoários parasitas do gênero Eimeria. Visando uma maior compreensão dos mecanismos moleculares envolvidos na regulação do ciclo de vida dos parasitas, foram geradas 15.000 seqüências expressas (ORESTES) para cada uma das três espécies mais importantes: E. tenella, E. maxima e E. acervulina. O presente trabalho consistiu no desenvolvimento de componentes de anotação automática de seqüências para o sistema EGene, plataforma previamente desenvolvida pelo nosso grupo (Durham et al. Bioinformatics 21: 2812-2813, 2005) para a construção de processamentos encadeados (pipelines). Estes componentes foram utilizados para a construção de pipelines de anotação automática de seqüências-consenso obtidas a partir da montagem dos ORESTES de Eimeria spp. A anotação consistiu na identificação dos genes e atribuição da função dos respectivos produtos protéicos, baseando-se em um conjunto de evidências. As seqüências também foram classificadas e quantificadas utilizando-se um vocabulário controlado de termos de ontologia gênica (GO). / Avian coccidiosis is an enteric disease caused by protozoan parasites of the genus Eimeria. Aiming at obtaining a better understanding of the molecular mechanisms that regulate the life cycle of the parasites, our group generated 15,000 expressed sequences (ORESTES) for each one of the three most important species: E. tenella, E. maxima and E. acervulina. In the present work, we report the development of a set of components for the automated sequence annotation through EGene, a platform for pipeline construction previously described by our group (Durham et al. Bioinformatics 21: 2812-2813, 2005). These components were used to construct pipelines for the automated annotation of assembled sequences of ORESTES of Eimeria spp. The annotation process consisted in the identification of genes and the corresponding protein function based on a set of evidences. The sequences were also mapped and quantified using a controlled vocabulary of gene ontology (GO) terms.

A coccidiose aviária é uma doença entérica causada por protozoários parasitas do gênero Eimeria. Visando uma maior compreensão dos mecanismos moleculares envolvidos na regulação do ciclo de vida dos parasitas, foram geradas 15.000 seqüências expressas (ORESTES) para cada uma das três espécies mais importantes: E. tenella, E. maxima e E. acervulina. O presente trabalho consistiu no desenvolvimento de componentes de anotação automática de seqüências para o sistema EGene, plataforma previamente desenvolvida pelo nosso grupo (Durham et al. Bioinformatics 21: 2812-2813, 2005) para a construção de processamentos encadeados (pipelines). Estes componentes foram utilizados para a construção de pipelines de anotação automática de seqüências-consenso obtidas a partir da montagem dos ORESTES de Eimeria spp. A anotação consistiu na identificação dos genes e atribuição da função dos respectivos produtos protéicos, baseando-se em um conjunto de evidências. As seqüências também foram classificadas e quantificadas utilizando-se um vocabulário controlado de termos de ontologia gênica (GO). / Avian coccidiosis is an enteric disease caused by protozoan parasites of the genus Eimeria. Aiming at obtaining a better understanding of the molecular mechanisms that regulate the life cycle of the parasites, our group generated 15,000 expressed sequences (ORESTES) for each one of the three most important species: E. tenella, E. maxima and E. acervulina. In the present work, we report the development of a set of components for the automated sequence annotation through EGene, a platform for pipeline construction previously described by our group (Durham et al. Bioinformatics 21: 2812-2813, 2005). These components were used to construct pipelines for the automated annotation of assembled sequences of ORESTES of Eimeria spp. The annotation process consisted in the identification of genes and the corresponding protein function based on a set of evidences. The sequences were also mapped and quantified using a controlled vocabulary of gene ontology (GO) terms.

Sequence alignment is one of the most intensely studied problems in bioinformatics, and is an important step in a wide range of analyses. An issue that has gained much attention in recent years is the fact that downstream analyses are often highly sensitive to the specific choice of alignment. One way to address this is to jointly sample alignments along with other parameters of interest. In order to extend the range of applicability of this approach, the first chapter of this thesis introduces a probabilistic evolutionary model for protein structures on a phylogenetic tree; since protein structures typically diverge much more slowly than sequences, this allows for more reliable detection of remote homologies, improving the accuracy of the resulting alignments and trees, and reducing sensitivity of the results to the choice of dataset. In order to carry out inference under such a model, a number of new Markov chain Monte Carlo approaches are developed, allowing for more efficient convergence and mixing on the high-dimensional parameter space. The second part of the thesis presents a directed acyclic graph (DAG)-based approach for representing a collection of sampled alignments. This DAG representation allows the initial collection of samples to be used to generate a larger set of alignments under the same approximate distribution, enabling posterior alignment probabilities to be estimated reliably from a reasonable number of samples. If desired, summary alignments can then be generated as maximum-weight paths through the DAG, under various types of loss or scoring functions. The acyclic nature of the graph also permits various other types of algorithms to be easily adapted to operate on the entire set of alignments in the DAG. In the final part of this work, methodology is introduced for alignment-DAG-based sequence annotation using hidden Markov models, and RNA secondary structure prediction using stochastic context-free grammars. Results on test datasets indicate that the additional information contained within the DAG allows for improved predictions, resulting in substantial gains over simply analysing a set of alignments one by one.