137 resultados para Bioinformática


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Pós-graduação em Ciências Biológicas (Genética) - IBB

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Pós-graduação em Genética - IBILCE

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Os sequenciadores de nova geração como as plataformas Illumina e SOLiD geram uma grande quantidade de dados, comumente, acima de 10 Gigabytes de arquivos-texto. Particularmente, a plataforma SOLiD permite o sequenciamento de múltiplas amostras em uma única corrida (denominada de corrida multiplex) por meio de um sistema de marcação chamado Barcode. Esta funcionalidade requer um processo computacional para separação dos dados por amostra, pois, o sequenciador fornece a mistura de todas amostras em uma única saída. Este processo deve ser seguro a fim de evitar eventuais embaralhamentos que possam prejudicar as análises posteriores. Neste contexto, o presente trabalho propõe desenvolvimento de um modelo probabilístico capaz de caracterizar sistema de marcação utilizado em sequenciamentos multiplex. Os resultados obtidos corroboraram a suficiência do modelo obtido, o qual permite, dentre outras coisas, identificar faltas em algum passo do processo de sequenciamento; adaptar e desenvolver de novos protocolos para preparação de amostras, além de atribuir um Grau de Confiança aos dados gerados e guiar um processo de filtragem que respeite as características de cada sequenciamento, não descartando sequências úteis de forma arbitrária.

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O surgimento das plataformas de sequenciamento de nova geração (NGS) proporcionou o aumento do volume de dados produzidos, tornando possível a obtenção de genomas completos. Apesar das vantagens alcançadas com estas plataformas, são observadas regiões de elevada ou baixa cobertura, em relação à média, associadas diretamente ao conteúdo GC. Este viés GC pode afetar análises genômicas e dificultar a montagem de genomas através da abordagem de novo, além de afetar as análises baseadas em referência. Além do que, as maneiras de avaliar o viés GC deve ser adequada para dados com diferentes perfis de relação/associação entre GC e cobertura, tais como linear e quadrático. Desta forma, este trabalho propõe o uso do Coeficiente de Correlação de Pearson (r) para analisar a correlação entre conteúdo GC e Cobertura, permitindo identificar aintensidade da correlação linear e detectar associações não-lineares, além de identificar a relação entre viés GC e as plataformas de sequenciamento. Os sinais positivos e negativos de r também permitem inferir relações diretamente proporcionais e inversamente proporcionais respectivamente. Utilizou-se dados da espécie Corynebacterium pseudotuberculosis, conhecido por serem genomas clonais obtidas através de diferentes tecnologias de sequenciamento para identificar se há relação do viés GC com as plataformas utilizadas.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Microbiologia Agropecuária - FCAV

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Biological processes are complex and possess emergent properties that can not be explained or predict by reductionism methods. To overcome the limitations of reductionism, researchers have been used a group of methods known as systems biology, a new interdisciplinary eld of study aiming to understand the non-linear interactions among components embedded in biological processes. These interactions can be represented by a mathematical object called graph or network, where the elements are represented by nodes and the interactions by edges that link pair of nodes. The networks can be classi- ed according to their topologies: if node degrees follow a Poisson distribution in a given network, i.e. most nodes have approximately the same number of links, this is a random network; if node degrees follow a power-law distribution in a given network, i.e. small number of high-degree nodes and high number of low-degree nodes, this is a scale-free network. Moreover, networks can be classi ed as hierarchical or non-hierarchical. In this study, we analised Escherichia coli and Saccharomyces cerevisiae integrated molecular networks, which have protein-protein interaction, metabolic and transcriptional regulation interactions. By using computational methods, such as MathematicaR , and data collected from public databases, we calculated four topological parameters: the degree distribution P(k), the clustering coe cient C(k), the closeness centrality CC(k) and the betweenness centrality CB(k). P(k) is a function that calculates the total number of nodes with k degree connection and is used to classify the network as random or scale-free. C(k) shows if a network is hierarchical, i.e. if the clusterization coe cient depends on node degree. CC(k) is an indicator of how much a node it is in the lesse way among others some nodes of the network and the CB(k) is a pointer of how a particular node is among several ...(Complete abstract click electronic access below)

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The comet assay is a method of DNA damage analysis widely used to quantify oxidative damage, crosslinks of DNA, apoptosis and genotoxicity of chemicals substances as chemical, pharmaceuticals, agrochemicals products, among others. This technique is suitable to detect DNA strand breaks, alkali-labile sites and incomplete excision repair sites and is based on the migration of DNA fragments by microeletroforesis, DNA migrates for the anode forming a “tail”, and the formed image has the appearance of a comet. The slides can be stained with fluorescence or silver, having differences in the microscopy type used for the analysis and the possibility of storage of the slides, moreover, the first one is a stained-method with more difficulties of accomplishment. The image analysis can be performed by a visual way, however, there is a disadvantage as the subjectivity on the results, that can be minimized by an automated method of digital analysis. This process was studied in this report with the aim to perceive the validation of the digital analysis turning it a quantitative method with larger reproductibility, minimizing the variability and imprecision due to the subjective analysis. For this validation we selected 50 comets photographed in a standardized way and printed, afterwards, pictures were submitted to three experienced appraisers, who quantified them manually. Later, the images were processed by free software ImageJ 1.38x, printed and quantified manually by the same appraisers. The intraclass correlation was higher to comet measures after image processing. Following, an algorithm of automated digital analysis from the measures of the comet was developed; the values obtained were compared with those 12 estimated manually after the processing resulting high correlation among the measures. The use of image analysis systems increases ...(Complete abstract click electronic access below)

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The reducionism method has helped in the clari cation of functioning of many biological process. However, such process are extremely complex and have emergent properties that can not be explained or even predicted by reducionism methods. To overcome these limits, researchers have been used a set of methods known as systems biology, a new area of biology aiming to understand the interactions between the multiple components of biological processes. These interactions can be represented by a mathematical object called graph or network, where the interacting elements are represented by a vertex and the interactions by edges that connect a pair of vertexes. Into graphs it is possible to nd subgraphs, occurring in complex networks at numbers that are signi cantly higher than those in randomized networks, they are de ned as motifs. As motifs in biological networks may represent the structural units of biological processess, their detection is important. Therefore, the aim of this present work was detect, count and classify motifs present in biological integrated networks of bacteria Escherichia coli and yeast Saccharomyces cere- visiae. For this purpose, we implemented codes in MathematicaR and Python environments for detecting, counting and classifying motifs in these networks. The composition and types of motifs detected in these integrated networks indicate that such networks are organized in three main bridged modules composed by motifs in which edges are all the same type. The connecting bridges are composed by motifs in which the types of edges are diferent

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To understand how biological phenomena emerge, the nonlinear interactions among the components envolved in these and the correspondent connected elements, like genes, proteins, etc., can be represented by a mathematical object called graph or network, where interacting elements are represented by edges connecting pairs of nodes. The analysis of various graph-related properties of biological networks has revealed many clues about biological processes. Among these properties, the community structure, i.e. groups of nodes densely connected among themselves, but sparsely connected to other groups, are important for identifying separable functional modules within biological systems for the comprehension of the high-level organization of the cell. Communities' detection can be performed by many algorithms, but most of them are based on the density of interactions among nodes of the same community. So far, the detection and analysis of network communities in biological networks have only been pursued for networks composed by one type of interaction (e.g. protein-protein interactions or metabolic interactions). Since a real biological network is simultaneously composed by protein-protein, metabolic and transcriptional regulatory interactions, it would be interesting to investigate how communities are organized in this type of network. For this purpose, we detected the communities in an integrated biological network of the Escherichia coli and Saccharomyces cerevisiae by using the Clique Percolation Method and we veri ed, by calculating the frequency of each type of interaction and its related entropy, if components of communities... (Complete abstract click electronic access below)