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Genes (Basel) ; 11(7)2020 07 21.
Artigo em Inglês | MEDLINE | ID: mdl-32708319


Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E Δ H S C compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E Δ H S C mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E Δ H S C mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches.

Antígenos de Superfície/imunologia , Infecções por Coronavirus/genética , Infecções por Coronavirus/imunologia , Proteínas Ligadas por GPI/imunologia , Redes Reguladoras de Genes , Interações Hospedeiro-Patógeno/imunologia , Animais , Antígenos de Superfície/genética , Biologia Computacional/métodos , Proteínas Ligadas por GPI/genética , Regulação da Expressão Gênica , Interações Hospedeiro-Patógeno/genética , Camundongos Knockout , Vírus da Hepatite Murina
Comput Math Methods Med ; 2018: 9674108, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30013615


In the last few years, gene networks have become one of most important tools to model biological processes. Among other utilities, these networks visually show biological relationships between genes. However, due to the large amount of the currently generated genetic data, their size has grown to the point of being unmanageable. To solve this problem, it is possible to use computational approaches, such as heuristics-based methods, to analyze and optimize gene network's structure by pruning irrelevant relationships. In this paper we present a new method, called GeSOp, to optimize large gene network structures. The method is able to perform a considerably prune of the irrelevant relationships comprising the input network. To do so, the method is based on a greedy heuristic to obtain the most relevant subnetwork. The performance of our method was tested by means of two experiments on gene networks obtained from different organisms. The first experiment shows how GeSOp is able not only to carry out a significant reduction in the size of the network, but also to maintain the biological information ratio. In the second experiment, the ability to improve the biological indicators of the network is checked. Hence, the results presented show that GeSOp is a reliable method to optimize and improve the structure of large gene networks.

Algoritmos , Biologia Computacional , Redes Reguladoras de Genes
Int J Data Min Bioinform ; 5(5): 558-73, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-22145534


The great amount of biological information provides scientists with an incomparable framework for testing the results of new algorithms. Several tools have been developed for analysing gene-enrichment and most of them are Gene Ontology-based tools. We developed a Kyoto Encyclopedia of Genes and Genomes (Kegg)-based tool that provides a friendly graphical environment for analysing gene-enrichment. The tool integrates two statistical corrections and simultaneously analysing the information about many groups of genes in both visual and textual manner. We tested the usefulness of our approach on a previous analysis (Huttenshower et al.). Furthermore, our tool is freely available (

Proteínas/genética , Software , Animais , Bases de Dados Factuais , Expressão Gênica , Genes , Genoma , Redes e Vias Metabólicas/genética , Proteínas/metabolismo
Bioinformatics ; 27(19): 2738-45, 2011 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-21824973


MOTIVATION: Binary datasets represent a compact and simple way to store data about the relationships between a group of objects and their possible properties. In the last few years, different biclustering algorithms have been specially developed to be applied to binary datasets. Several approaches based on matrix factorization, suffix trees or divide-and-conquer techniques have been proposed to extract useful biclusters from binary data, and these approaches provide information about the distribution of patterns and intrinsic correlations. RESULTS: A novel approach to extracting biclusters from binary datasets, BiBit, is introduced here. The results obtained from different experiments with synthetic data reveal the excellent performance and the robustness of BiBit to density and size of input data. Also, BiBit is applied to a central nervous system embryonic tumor gene expression dataset to test the quality of the results. A novel gene expression preprocessing methodology, based on expression level layers, and the selective search performed by BiBit, based on a very fast bit-pattern processing technique, provide very satisfactory results in quality and computational cost. The power of biclustering in finding genes involved simultaneously in different cancer processes is also shown. Finally, a comparison with Bimax, one of the most cited binary biclustering algorithms, shows that BiBit is faster while providing essentially the same results. AVAILABILITY: The source and binary codes, the datasets used in the experiments and the results can be found at: CONTACT: SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Algoritmos , Neoplasias do Sistema Nervoso Central/genética , Biologia Computacional/métodos , Mineração de Dados/métodos , Neoplasias do Sistema Nervoso Central/embriologia , Análise por Conglomerados , Bases de Dados Factuais , Expressão Gênica , Humanos , Armazenamento e Recuperação da Informação , Análise de Sequência com Séries de Oligonucleotídeos , Software
Brief Bioinform ; 11(2): 210-24, 2010 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-19815645


Establishing an association between variables is always of interest in genomic studies. Generation of DNA microarray gene expression data introduces a variety of data analysis issues not encountered in traditional molecular biology or medicine. Frequent pattern mining (FPM) has been applied successfully in business and scientific data for discovering interesting association patterns, and is becoming a promising strategy in microarray gene expression analysis. We review the most relevant FPM strategies, as well as surrounding main issues when devising efficient and practical methods for gene association analysis (GAA). We observed that, so far, scalability achieved by efficient methods does not imply biological soundness of the discovered association patterns, and vice versa. Ideally, GAA should employ a balanced mining model taking into account best practices employed by methods reviewed in this survey. Integrative approaches, in which biological knowledge plays an important role within the mining process, are becoming more reliable.

Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Análise de Sequência com Séries de Oligonucleotídeos , Reconhecimento Automatizado de Padrão/métodos , Análise de Sequência de DNA , Algoritmos , Expressão Gênica , Redes Reguladoras de Genes