Sistema para an?lise de sequ?ncias nucleot?dicas do HIV dispon?veis no GenBank

• Ano de defesa: 2015
• Autor(a) principal: Gon?alves, Jos? Irahe Kasprzykowski
• Orientador(a): Queiroz, Artur Trancoso Lopo de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual de Feira de Santana
• Programa de Pós-Graduação: Mestrado em Computa??o Aplicada
• Departamento: DEPARTAMENTO DE CI?NCIAS EXATAS
• País: Brasil
• Palavras-chave em Português: HIV; Sequencias nucleot?dicas; Subtipo; Gen?tipo; Gen?tica
• Palavras-chave em Inglês: HIV; Nucleotide sequenqces; Subtypes; Genotypes; Genetics
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO; CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: http://localhost:8080/tede/handle/tede/327

HIV infects over 40 million people worldwide and is considered by the World Health Organization a large scale pandemic. Which the associated disease has no cure. New data and analysis can help new treatment and vaccine development. However, the dataset is vast, with over 500,000 sequences available on GenBank. This data still lacks essential information such as subtyping and genome location. To help minimize these problems we developed a system for automated analysis from GenBank data. The tool performs sequence map according to HXB2 and subtyping by comparison with subtype reference sequences. This process uses Needleman-Wusch and Smith-Waterman respectively. All 582,678 sequences were mapped in 5 days and 14 hours and subtyped in 1 day and 7 hours with our algorithm, while the original approach was estimated to finish in 36 and 97 years respectively. Our tool was able to analyse the massive data in a reliable time. No current subtyping tool can analyse this high-throughput data. Our results showed that pol and gag genes were the most prevalent genes on the dataset, and could be explained because treatment and subtyping are based on these genes. Moreover, the structural genes were most prevalent, with 66.41%. This highlighted the low representation of regulatory genes on available data. The subtyping results showed that the subtype B was most frequent, with 45.96%. The recombinants together represent 43.37%. Furthermore, subtype C presented only 4.12% and the other pure subtypes less than 4%. Also, the geographical data was recovered from database and USA presented higher frequency, with 24.50%, showing a significant country bias. Our results present a new HIV subtype distribution with the most complete and recent dataset.Herein, we presented a new user friendly software for massive data analysis of viruses. This software is able to analyse highly mutational virus data, such as HCV and HIV in reliable time. Further, severe country bias raises questions regarding world subtype distribution. The analysis of all sequences from HIV provides new epidemy insights about subtypes and country distribution.