Detalhes bibliográficos
| Ano de defesa: |
2010 |
| Autor(a) principal: |
IDALINO, Rita de Cássia de Lima
 |
| Orientador(a): |
SANTORO, Kleber Régis |
| Banca de defesa: |
GOMES FILHO, Manoel Adrião,
STOSIC, Tatijana,
FERREIRA, Tiago Alessandro Espíndola |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal Rural de Pernambuco
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Biometria e Estatística Aplicada
|
| Departamento: |
Departamento de Estatística e Informática
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5258
|
Resumo: |
Given the large amount of data that is generated in the field of molecular genetics, is of paramount importance that techniques which allow the organization and interpretation of such data be developed and widely disseminated. Initially, we carried out a composition analysis of three gene sequences of the species: ox (Bos taurus), goat (Capra hircus), and sheep (Ovis aries), then we applied alignment techniques for identification of similarities between them. Subsequently, we used the Markov Chain theory with hidden states, i.e. Hidden Markov Models (HMMs, hereafter), in the application of discrimination problem of homogeneous regions in DNA sequences. We used the Viterbi algorithm as an auxiliary tool to obtain homogeneous regions, and then the Baum-Eelch algorithm in order to maximize the probability of a sequence of observations. We analyzed portions of HSP70.1 and NRAMP-1 genes for three different species. |
