Desenvolvimento de uma metodologia computacional para análise de variantes gênicas no locus de cadeia pesada de imunoglobulinas humanas

Detalhes bibliográficos
Ano de defesa: 2022
Autor(a) principal: Fábio Rodrigues Martins
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Minas Gerais
Brasil
ICB - INSTITUTO DE CIÊNCIAS BIOLOGICAS
Programa de Pós-Graduação em Bioinformatica
UFMG
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/1843/44593
Resumo: The B and T cells of the adaptive immune response of humans and other vertebrates have an enormous diversity of receptors. The diversity of these receptors is generated by some processes such as: variability of the immunoglobulin locus, recombination process and somatic hypermutation. These processes influence the identification of new human immunoglobulin alleles, because given a variant it is not a simple task to say if it really came from the individual's germ line or if it was generated by the processes mentioned above. However, identifying new alleles of human immunoglobulin is important for studies of maturation and affinity of these molecules. The correct identification of alleles can help in the studies of several human diseases associated with the antibody repertoire and in the development of new therapeutics through antibody engineering techniques. To contribute to the process of discovering new alleles, the advent of Next Generation Sequencing (NGS) made it possible for human genomes to be sequenced with good coverage. With this, several tools, databases and approaches that study the human antibody repertoire have been developed for the mapping and identification of new immunoglobulin alleles. The methodology developed in this work identified 10,550 alleles of IGHV genes, of which 10,156 are new putative alleles. We also identified 524 alleles of IGHD genes and 670 alleles of IGHJ gene. A database integrated to a web platform was created (YVr-DB) to store and make accessible the likely new variants found.