Dataset Information

Domain-oriented functional analysis based on expression profiling.

ABSTRACT: BACKGROUND:Co-regulation of genes may imply involvement in similar biological processes or related function. Many clusters of co-regulated genes have been identified using microarray experiments. In this study, we examined co-regulated gene families using large-scale cDNA microarray experiments on the human transcriptome. RESULTS:We present a simple model, which, for each probe pair, distills expression changes into binary digits and summarizes the expression of multiple members of a gene family as the Family Regulation Ratio. The set of Family Regulation Ratios for each protein family across multiple experiments is called a Family Regulation Profile. We analyzed these Family Regulation Profiles using Pearson Correlation Coefficients and derived a network diagram portraying relationships between the Family Regulation Profiles of gene families that are well represented on the microarrays. Our strategy was cross-validated with two randomly chosen data subsets and was proven to be a reliable approach. CONCLUSION:This work will help us to understand and identify the functional relationships between gene families and the regulatory pathways in which each family is involved. Concepts presented here may be useful for objective clustering of protein functions and deriving a comprehensive protein interaction map. Functional genomic approaches such as this may also be applicable to the elucidation of complex genetic regulatory networks.

SUBMITTER: Ding W

PROVIDER: S-EPMC137579 | biostudies-literature | 2002 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Domain-oriented functional analysis based on expression profiling.

Ding Wei W Wang Luquan L Qiu Ping P Kostich Mitchel M Greene Jonathan J Hernandez Marco M

BMC genomics 20021031 1

<h4>Background</h4>Co-regulation of genes may imply involvement in similar biological processes or related function. Many clusters of co-regulated genes have been identified using microarray experiments. In this study, we examined co-regulated gene families using large-scale cDNA microarray experiments on the human transcriptome.<h4>Results</h4>We present a simple model, which, for each probe pair, distills expression changes into binary digits and summarizes the expression of multiple members o ...[more]

PMID: 12456268

Similar Datasets

Project description:The aim of the current study was to investigate the pathogenesis of pituitary adenoma through screening of the differentially-expressed genes (DEGs) and proteins in normal pituitary and pituitary adenoma tissues, and analyzing the interactions among them. Following the acquisition of gene expression profiling data from a public functional genomics data repository, Gene Expression Omnibus, DEGs were screened in normal pituitary and pituitary adenoma tissues. Upregulated and downregulated DEGs were further identified through gene ontology functional enrichment analysis. Subsequently, the DEGs were mapped to the Search Tool for the Retrieval of Interacting Genes database, and the protein-protein interaction (PPI) networks of the upregulated and downregulated DEGs were constructed. Finally, the functional modules of the PPI network of the downregulated DEGs were analyzed. In total, 211 upregulated and 413 downregulated DEGs were screened between the normal pituitary and pituitary adenoma samples. Downregulated DEGs were associated with certain functions, including the immune response, hormone regulation and cell proliferation. Upregulated genes were associated with cation transport functions. Five modules were acquired from the PPI network of the downregulated DEGs. Transcription factors, including signal transducer and activator of transcription 3 (STAT3), interleukin 6 (IL-6), B-cell lymphoma 6 protein, early growth response 1, POU1F1, jun B proto-oncogene and FOS were the core nodes in the functional modules. In summary, the DEGs and proteins were identified through screening gene expression profiling and PPI networks. The results of the present study indicated that low expression levels of hormone- and immune-related genes facilitated the occurrence of pituitary adenoma. Low expression levels of IL-6 and STAT3 were significant in the dysimmunity of pituitary adenoma. Furthermore, the low expression level of POU1F1 contributed to the reduction in pituitary hormone secretion.

Dataset Information

Domain-oriented functional analysis based on expression profiling.

Publications

Domain-oriented functional analysis based on expression profiling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure