Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feat...Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feature to improve the accuracy and efficiency when they are used for pattern recognition and clustering analysis. A Poisson Model-based Kernel (PMK) was proposed based on the Poisson distribution of the SAGE data. Kernel Principle Component Analysis (KPCA) with PMK was proposed and used in feature-extract analysis of mouse retinal SAGE data. The computa-tional results show that this algorithm can extract feature effectively and reduce dimensions of SAGE data.展开更多
This study examined the gene expression patterns of peripheral blood mononuclear cells （PBMCs） in patients with systemic lupus erythematosus （SLE） by using serial analysis of gene expression （SAGE） technology. F...This study examined the gene expression patterns of peripheral blood mononuclear cells （PBMCs） in patients with systemic lupus erythematosus （SLE） by using serial analysis of gene expression （SAGE） technology. Following the construction of serial analysis of gene expression （SAGE） library of PBMCs collected from 3 cases of familial SLE patients, a large scale of tag Sequencing was performed. The data extracted from sequencing files was analyzed with SAGE 2000 V 4.5 software. The top 30 expressed genes of SLE patients were uploaded to http：//david.niaid.nih.gov/david/ease.htm and the functional classification of genes was obtained. The differences among those expressed gene were analyzed by Chi-square tests. The results showed that a total of 1286 unique SAGE tags were identified from 1814 individual SAGE tags. Among the 1286 unique tags, 86.8% had single copy, and only 0.2% tags had more than 20 copies. And 68.4% of the tags matched known expressed sequences, 41.1% of which matched more than one known expressed sequence. About 31.6% of the tags had no match and could represent potentially novel genes. Approximately one third of the top 30 genes were ribosomal protein, and the rest were genes related to metabolism or with unknown functions. Eight tags were found to express differentially in SAGE library of SLE patients. This study draws a profile of gene expression patterns of PBMCs in patients with SLE. Comparison of SAGE database from PBMCs between normal individuals and SLE patients will help us to better understand the pathogenesis of SLE.展开更多
Objective: To monitor the systemic gene expression profile in a murine model of lipopolysaccharide-induced acute lung injury. Methods: Acute lung injury was induced by intratracheal injection of lipopolysaccharide in ...Objective: To monitor the systemic gene expression profile in a murine model of lipopolysaccharide-induced acute lung injury. Methods: Acute lung injury was induced by intratracheal injection of lipopolysaccharide in 3 mice. Another 3 normal mice receiving same volume of normal saline were taken as the controls. The comprehensive gene expression profile was monitored by the recently modified long serial analysis of gene expression. Results: A total of 24 670 tags representing 12 168 transcripts in the control mice and 26 378 tags representing 13 397 transcripts in the mice with lung injury were identified respectively. There were 11 transcripts increasing and 7 transcripts decreasing more than 10 folds in the lipopolysaccharide-treated mice. The most overexpressed genes in the mice with lung injury included serum amyloid A3, metallothionein 2, lipocalin 2, cyclin-dependent kinase inhibitor 1A, lactate dehydrogenase 1, melatonin receptor, S100 calcium-binding protein A9, natriuretic peptide precursor, etc. Mitogen activated protein kinase 3, serum albumin, complement component 1 inhibitor, and ATP synthase were underexpressed in the lung injury mice. Conclusions: Serial analysis of gene expression provides a molecular characteristic of acute lung injury.展开更多
基金Supported by the National Natural Science Foundation of China (No. 50877004)
文摘Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feature to improve the accuracy and efficiency when they are used for pattern recognition and clustering analysis. A Poisson Model-based Kernel (PMK) was proposed based on the Poisson distribution of the SAGE data. Kernel Principle Component Analysis (KPCA) with PMK was proposed and used in feature-extract analysis of mouse retinal SAGE data. The computa-tional results show that this algorithm can extract feature effectively and reduce dimensions of SAGE data.
基金This project was supported by a grant form the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry (No [2002]247)
文摘This study examined the gene expression patterns of peripheral blood mononuclear cells （PBMCs） in patients with systemic lupus erythematosus （SLE） by using serial analysis of gene expression （SAGE） technology. Following the construction of serial analysis of gene expression （SAGE） library of PBMCs collected from 3 cases of familial SLE patients, a large scale of tag Sequencing was performed. The data extracted from sequencing files was analyzed with SAGE 2000 V 4.5 software. The top 30 expressed genes of SLE patients were uploaded to http：//david.niaid.nih.gov/david/ease.htm and the functional classification of genes was obtained. The differences among those expressed gene were analyzed by Chi-square tests. The results showed that a total of 1286 unique SAGE tags were identified from 1814 individual SAGE tags. Among the 1286 unique tags, 86.8% had single copy, and only 0.2% tags had more than 20 copies. And 68.4% of the tags matched known expressed sequences, 41.1% of which matched more than one known expressed sequence. About 31.6% of the tags had no match and could represent potentially novel genes. Approximately one third of the top 30 genes were ribosomal protein, and the rest were genes related to metabolism or with unknown functions. Eight tags were found to express differentially in SAGE library of SLE patients. This study draws a profile of gene expression patterns of PBMCs in patients with SLE. Comparison of SAGE database from PBMCs between normal individuals and SLE patients will help us to better understand the pathogenesis of SLE.
文摘Objective: To monitor the systemic gene expression profile in a murine model of lipopolysaccharide-induced acute lung injury. Methods: Acute lung injury was induced by intratracheal injection of lipopolysaccharide in 3 mice. Another 3 normal mice receiving same volume of normal saline were taken as the controls. The comprehensive gene expression profile was monitored by the recently modified long serial analysis of gene expression. Results: A total of 24 670 tags representing 12 168 transcripts in the control mice and 26 378 tags representing 13 397 transcripts in the mice with lung injury were identified respectively. There were 11 transcripts increasing and 7 transcripts decreasing more than 10 folds in the lipopolysaccharide-treated mice. The most overexpressed genes in the mice with lung injury included serum amyloid A3, metallothionein 2, lipocalin 2, cyclin-dependent kinase inhibitor 1A, lactate dehydrogenase 1, melatonin receptor, S100 calcium-binding protein A9, natriuretic peptide precursor, etc. Mitogen activated protein kinase 3, serum albumin, complement component 1 inhibitor, and ATP synthase were underexpressed in the lung injury mice. Conclusions: Serial analysis of gene expression provides a molecular characteristic of acute lung injury.
