Utilização do método Suvrel no ranqueamento de genes envolvidos em expressão diferencial e na análise de otimização de predição de epítopos lineares de células B
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2019-10-24
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Universidade Estadual Paulista (Unesp)
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RNA-seq. Na primeira parte desta tese, uma abordagem geométrica baseada no Método Suvrel (\Supervised Variational Relevance Learning") é comparada com pacotes R destinados a expressão diferencial. O método Suvrel procura determinar as relevâncias das características (por exemplo, genes ou transcritos) baseado em comparações de distâncias interclasses e intraclasses. A investigação foi realizada utilizando replicatas técnicas e biológicas. A análise utilizando replicatas técnicas foi realizada por meio de curvas ROC enquanto que as replicatas biológicas foram analisadas por meio de robustez. De forma geral, é mostrado que a análise proposta obteve melhores resultados na maioria dos casos. Além disso, é um método simples que não faz nenhuma suposição sobre a distribuição associada ao conjunto de dados de RNA-seq. Desta perspectiva, a relevância deste estudo foi mostrar que um método simples pode fornecer boa acurácia tanto quanto os métodos mais complexos. Otimização de predição de epítopos lineares de células B. Epítopos são definidos como fragmentos constituintes de um antígeno (substância que ao entrar em um organismo é capaz de iniciar uma resposta imune) que interagem com receptores de células B, T e anticorpos. Após a ativação da resposta imune adaptativa é possível que o sistema imune crie memória, onde uma outra invasão será respondida de forma mais rápida. A identificação de epítopos utilizando processos experimentais ainda permanece difícil. Devido ao potencial de aplicação de ferramentas preditivas que podem auxiliar a identificação, uma série de algoritmos foram propostos. Dado o panorama exposto, a proposta da presente tese é a análise de possível melhora da acurácia de predição de epítopos lineares de células B associada ao preditor LBTOPE por meio do método Suvrel. A metodologia apresentada melhorou consideravelmente medidas estatísticas em dados gerados pelos autores do preditor e também em dados gerados por outros autores.
RNA-seq. Although differential gene expression (DGE) profiling in RNAseq is used by many researchers, new packages and pipelines are continuously being presented as a result of an ongoing investigation. In the present work, a geometric approach based on Supervised Variational Relevance Learning (Suvrel) was compared with DEpackages (edgeR, DESEq, baySeq, PoissonSeq, and limma) in the DGE profiling. The Suvrel method seeks to determine the relevance of characteristics (e.g., gene or transcript) based on intraclass and interclass distances. The comparison was performed using technical and biological replicates. The results showed that geometric approach had a better performance than the DEpackages. Therefore, the conclusion is that the geometric approach had a slight overall better performance than other methods. Moreover, it is a simple method that does not make any assumption about the distribution associated with RNA-seq data set. From this perspective, the relevance of this study was to show that a simple method can provide as good performance as more complex methods. Prediction optimization of linear B-cell epitopes. Epitopes are defined as fragments of an antigen (a substance that invade an organism capable of start an immune response) that interact with B cell receptors, T and antibodies. After activation of the adaptive immune response it is possible for the immune system to generate memory where another invasion will be attacked faster. Identifying epitopes using experimental procedures still di cult. However, predictive tools can aid the identification. The purpose of this thesis is to analyze the possible improvement of the prediction performance of linear B-cell epitopes associated to predictor LBTOPE using the Suvrel method. The results show that the methodology improved statistical measures in data generated by the authors of predictor and also in data generated by other authors.
RNA-seq. Although differential gene expression (DGE) profiling in RNAseq is used by many researchers, new packages and pipelines are continuously being presented as a result of an ongoing investigation. In the present work, a geometric approach based on Supervised Variational Relevance Learning (Suvrel) was compared with DEpackages (edgeR, DESEq, baySeq, PoissonSeq, and limma) in the DGE profiling. The Suvrel method seeks to determine the relevance of characteristics (e.g., gene or transcript) based on intraclass and interclass distances. The comparison was performed using technical and biological replicates. The results showed that geometric approach had a better performance than the DEpackages. Therefore, the conclusion is that the geometric approach had a slight overall better performance than other methods. Moreover, it is a simple method that does not make any assumption about the distribution associated with RNA-seq data set. From this perspective, the relevance of this study was to show that a simple method can provide as good performance as more complex methods. Prediction optimization of linear B-cell epitopes. Epitopes are defined as fragments of an antigen (a substance that invade an organism capable of start an immune response) that interact with B cell receptors, T and antibodies. After activation of the adaptive immune response it is possible for the immune system to generate memory where another invasion will be attacked faster. Identifying epitopes using experimental procedures still di cult. However, predictive tools can aid the identification. The purpose of this thesis is to analyze the possible improvement of the prediction performance of linear B-cell epitopes associated to predictor LBTOPE using the Suvrel method. The results show that the methodology improved statistical measures in data generated by the authors of predictor and also in data generated by other authors.
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RNA-seq, Abordagem geométrica, Epítopos, Protozoários, Predição, Suvrel, Expressão diferencial, Diferential expression, Geometric approach, Epitopes, Protozoa, Prediction, Suvrel