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Propagação semi-automática de termos Gene Ontology a proteínas com potencial biotecnológico para a produção de bioenergia / Semi-automatic propagation of Gene Ontology terms to proteins with biotechnology potential for bioenergy productionTaniguti, Lucas Mitsuo 18 November 2014 (has links)
O aumento no volume de dados biológicos, oriundos principalmente do surgimento de sequenciadores de segunda geração, configura um desafio para a manutenção dos bancos de dados, que devem armazenar, disponibilizar e, no caso de bancos secundários, propagar informações biológicas para sequências sem caracterização experimental. Tal propagação é crucial , pois o fluxo com que novas sequências são depositadas é muito superior ao que proteínas são experimentalmente caracterizadas. De forma análoga ao EC number (Enzyme Commission number), a organização de proteínas em famílias visa organizar e facilitar operações automáticas nos bancos de dados. Dentro desse contexto este trabalho teve como objetivos a geração de modelos computacionais para famílias de proteínas envolvidas em processos microbianos biotecnologicamente interessantes para a produção de bioenergia. Para a geração dos modelos estatísticos foram escolhidas proteínas referência analisadas a priori em colaboração com o projeto MENGO1 . A partir da proteína referência foram realizadas buscas no UniProtKB com o objetivo de encontrar proteínas representativas para cada família e descrições de função com base na literatura científica. Com a coleção de sequências primárias das proteínas selecionadas foram realizados alinhamentos múltiplos de sequências com o programa MUSCLE 3.7 e posteriormente com o programa HMMER foram gerados os modelos computacionais (perfis de cadeia oculta de Markov). Os modelos passaram por consecutivas revisões para serem utilizados na propagação dos termos do Gene Ontology com confiança.Um total de 1.233 proteínas puderam receber os termos GO. Dessas proteínas 79% não apresentavam os termos GO disponibilizados no banco de dados UniProtKB. Uma comparação dos perfis-HMM com a utilização de redes de similaridade a um E-value de 10-14 confirmou a utilidade dos modelos na propagação adequada dos termos. Uma segunda validação utilizando um banco de dados construído com sequências aleatórias com base nos modelos e na frequência de codons das proteínas anotadas do SwisProt permitiu verificar a sensibilidade da estratégia quanto a recuperar membros não pertencentes aos modelos gerados. / The increase of biological data produced mainly by the second generation technologies stands as a challenge for the biological databases, that needs to adress issues like storage, data availability and, in the case of secondary databases, to propagate biological information to sequences with no experimental characterization. The propagation is important since the flow that new sequences are submited into databases is much higher than proteins having their function described by experiments. Similarly to the EC. number (Enzyme Commission number), an organization of protein families aims to organize and help automatic processes in databases. In this context this work had as goals the generation of computational models for protein families related to microbial processes with biotechnology potential for production of bioenergy. Several proteins annotated by MENGO2, a project in collaboration, were used as seeds to the statistic models. Alignments were made on UniProtKB, querying the seeds proteins, looking for representatives for each family generated and the existence of function descriptions referenced on the cientific literature. Multiple sequence alignment were made on each collection of seeds proteins, representatives of the families, thorough the MUSCLE 3.7 program, and after were generated the computational models (profile Hidden Markov Models) with the HMMER package. The models were consecutively reviewed until the curator consider it reliable for propagation of Gene Ontology terms. A set of 1,233 proteins from UniProtKB were classified in our families, suggesting that they could be annotated by the GO terms using MENGOfams families. From those proteins, 79% were not annotated by the MENGO specific GO terms. To compare the results that would be obtained using only BLAST similarity measures and using pHMMs we generated similarity networks, using an Evaue cutoff of 10-14. The results showed that the classification results of pHMMs are valuable for biological annotation propagation because it identifies precisely members of each family. A second analysis was applied for each family, using the respective pHMMs to query a collection of sequences generated by a null model. For null model were assumed that all sequences were not homologous and could be represented just by the aminoacid frequencies observed in the SwissProt database. No non-homologous proteins were classified as members by the MENGOfams models, suggesting that they were sensitive to identify only true member sequences.
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Propagação semi-automática de termos Gene Ontology a proteínas com potencial biotecnológico para a produção de bioenergia / Semi-automatic propagation of Gene Ontology terms to proteins with biotechnology potential for bioenergy productionLucas Mitsuo Taniguti 18 November 2014 (has links)
O aumento no volume de dados biológicos, oriundos principalmente do surgimento de sequenciadores de segunda geração, configura um desafio para a manutenção dos bancos de dados, que devem armazenar, disponibilizar e, no caso de bancos secundários, propagar informações biológicas para sequências sem caracterização experimental. Tal propagação é crucial , pois o fluxo com que novas sequências são depositadas é muito superior ao que proteínas são experimentalmente caracterizadas. De forma análoga ao EC number (Enzyme Commission number), a organização de proteínas em famílias visa organizar e facilitar operações automáticas nos bancos de dados. Dentro desse contexto este trabalho teve como objetivos a geração de modelos computacionais para famílias de proteínas envolvidas em processos microbianos biotecnologicamente interessantes para a produção de bioenergia. Para a geração dos modelos estatísticos foram escolhidas proteínas referência analisadas a priori em colaboração com o projeto MENGO1 . A partir da proteína referência foram realizadas buscas no UniProtKB com o objetivo de encontrar proteínas representativas para cada família e descrições de função com base na literatura científica. Com a coleção de sequências primárias das proteínas selecionadas foram realizados alinhamentos múltiplos de sequências com o programa MUSCLE 3.7 e posteriormente com o programa HMMER foram gerados os modelos computacionais (perfis de cadeia oculta de Markov). Os modelos passaram por consecutivas revisões para serem utilizados na propagação dos termos do Gene Ontology com confiança.Um total de 1.233 proteínas puderam receber os termos GO. Dessas proteínas 79% não apresentavam os termos GO disponibilizados no banco de dados UniProtKB. Uma comparação dos perfis-HMM com a utilização de redes de similaridade a um E-value de 10-14 confirmou a utilidade dos modelos na propagação adequada dos termos. Uma segunda validação utilizando um banco de dados construído com sequências aleatórias com base nos modelos e na frequência de codons das proteínas anotadas do SwisProt permitiu verificar a sensibilidade da estratégia quanto a recuperar membros não pertencentes aos modelos gerados. / The increase of biological data produced mainly by the second generation technologies stands as a challenge for the biological databases, that needs to adress issues like storage, data availability and, in the case of secondary databases, to propagate biological information to sequences with no experimental characterization. The propagation is important since the flow that new sequences are submited into databases is much higher than proteins having their function described by experiments. Similarly to the EC. number (Enzyme Commission number), an organization of protein families aims to organize and help automatic processes in databases. In this context this work had as goals the generation of computational models for protein families related to microbial processes with biotechnology potential for production of bioenergy. Several proteins annotated by MENGO2, a project in collaboration, were used as seeds to the statistic models. Alignments were made on UniProtKB, querying the seeds proteins, looking for representatives for each family generated and the existence of function descriptions referenced on the cientific literature. Multiple sequence alignment were made on each collection of seeds proteins, representatives of the families, thorough the MUSCLE 3.7 program, and after were generated the computational models (profile Hidden Markov Models) with the HMMER package. The models were consecutively reviewed until the curator consider it reliable for propagation of Gene Ontology terms. A set of 1,233 proteins from UniProtKB were classified in our families, suggesting that they could be annotated by the GO terms using MENGOfams families. From those proteins, 79% were not annotated by the MENGO specific GO terms. To compare the results that would be obtained using only BLAST similarity measures and using pHMMs we generated similarity networks, using an Evaue cutoff of 10-14. The results showed that the classification results of pHMMs are valuable for biological annotation propagation because it identifies precisely members of each family. A second analysis was applied for each family, using the respective pHMMs to query a collection of sequences generated by a null model. For null model were assumed that all sequences were not homologous and could be represented just by the aminoacid frequencies observed in the SwissProt database. No non-homologous proteins were classified as members by the MENGOfams models, suggesting that they were sensitive to identify only true member sequences.
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Human Activity Recognition By Gait AnalysisKepenekci, Burcu 01 February 2011 (has links) (PDF)
This thesis analyzes the human action recognition problem. Human actions are modeled as a time evolving temporal texture. Gabor filters, which are proved to be a robust 2D texture representation tool by detecting spatial points with high variation, is extended to 3D domain to capture motion texture features. A well known filtering algorithm and a recent unsupervised clustering algorithm, the Genetic Chromodynamics, are combined to select salient spatio-temporal features of the temporal texture and to segment the activity sequence into temporal texture primitives. Each activity sequence is represented as a composition of temporal texture primitives with its salient spatio-temporal features, which are also the symbols of our codebook. To overcome temporal variation between different performances of the same action, a Profile Hidden Markov Model is applied with Viterbi Path Counting (ensemble training). Not only parameters and structure but also codebook is learned during training.
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