共查询到20条相似文献,搜索用时 15 毫秒
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Background
Existing clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions.Results
We applied our method to several sets of co-expressed genes and were able to define subsets with enrichment in particular biological processes and specific upstream regulatory motifs.Conclusions
These results show the potential of our technique for functional prediction and regulatory motif identification from microarray data.5.
Biochemical and cytogenetic experiments have led to the hypothesis that eukaryotic chromatin is organized into a series of distinct domains that are functionally independent. Two expectations of this hypothesis are: (i) adjacent genes are more frequently co-expressed than is expected by chance; and (ii) co-expressed neighbouring genes are often functionally related. Here we report that over 10% of Arabidopsis thaliana genes are within large, co-expressed chromosomal regions. Two per cent (497/22,520) of genes are highly co-expressed (r > 0.7), about five times the number expected by chance. These genes fall into 226 groups distributed across the genome, and each group typically contains two to three genes. Among the highly co-expressed groups, 40% (91/226) have genes with high amino acid sequence similarity. Nonetheless, duplicate genes alone do not explain the observed levels of co-expression. Co-expressed, non-homologous genes are transcribed in parallel, share functions, and lie close together more frequently than expected. Our results show that the A. thaliana genome contains domains of gene expression. Small domains have highly co-expressed genes that often share functional and sequence similarity and are probably co-regulated by nearby regulatory sequences. Genes within large, significantly correlated groups are typically co-regulated at a low level, suggesting the presence of large chromosomal domains. 相似文献
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Hao Li Constance L Wood Yushu Liu Thomas V Getchell Marilyn L Getchell Arnold J Stromberg 《BMC bioinformatics》2006,7(1):245
Background
In gene networks, the timing of significant changes in the expression level of each gene may be the most critical information in time course expression profiles. With the same timing of the initial change, genes which share similar patterns of expression for any number of sampling intervals from the beginning should be considered co-expressed at certain level(s) in the gene networks. In addition, multiple testing problems are complicated in experiments with multi-level treatments when thousands of genes are involved. 相似文献7.
Background
Many different cluster methods are frequently used in gene expression data analysis to find groups of co-expressed genes. However, cluster algorithms with the ability to visualize the resulting clusters are usually preferred. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. 相似文献8.
Jeff W Chou Tong Zhou William K Kaufmann Richard S Paules Pierre R Bushel 《BMC bioinformatics》2007,8(1):427
Background
A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions. 相似文献9.
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Ana Moreno Bofarull Antón Arias Royo Manuel Hernández Fernández Edgardo Ortiz-Jaureguizar Jorge Morales 《BMC evolutionary biology》2008,8(1):97
Background
This paper tests Vrba's resource-use hypothesis, which predicts that generalist species have lower specialization and extinction rates than specialists, using the 879 species of South American mammals. We tested several predictions about this hypothesis using the biomic specialization index (BSI) for each species, which is based on its geographical range within different climate-zones. The four predictions tested are: (1) there is a high frequency of species restricted to a single biome, which henceforth are referred to as stenobiomic species, (2) certain clades are more stenobiomic than others, (3) there is a higher proportion of biomic specialists in biomes that underwent through major expansion-contraction alternation due to the glacial-interglacial cycles, (4) certain combinations of inhabited biomes occur more frequently among species than do others. 相似文献12.
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Carlos Quijano Pavel Tomancak Jesus Lopez-Marti Mikita Suyama Peer Bork Marco Milan David Torrents Miguel Manzanares 《Genome biology》2009,9(12):R176
Background
The physical organization and chromosomal localization of genes within genomes is known to play an important role in their function. Most genes arise by duplication and move along the genome by random shuffling of DNA segments. Higher order structuring of the genome occurs in eukaryotes, where groups of physically linked genes are co-expressed. However, the contribution of gene duplication to gene order has not been analyzed in detail, as it is believed that co-expression due to recent duplicates would obscure other domains of co-expression. 相似文献17.
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Michael Watson 《BMC bioinformatics》2006,7(1):509
Background
Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed. 相似文献19.
Background
Alternative splicing is an efficient mechanism for increasing the variety of functions fulfilled by proteins in a living cell. It has been previously demonstrated that alternatively spliced regions often comprise functionally important and conserved sequence motifs. The objective of this work was to test the hypothesis that alternative splicing is correlated with contact regions of protein-protein interactions. 相似文献20.