• Title, Summary, Keyword: gene expression

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Analysis and Subclass Classification of Microarray Gene Expression Data Using Computational Biology (전산생물학을 이용한 마이크로어레이의 유전자 발현 데이터 분석 및 유형 분류 기법)

  • Yoo, Chang-Kyoo;Lee, Min-Young;Kim, Young-Hwang;Lee, In-Beum
    • Journal of Institute of Control, Robotics and Systems
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    • v.11 no.10
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    • pp.830-836
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    • 2005
  • Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

NGSEA: Network-Based Gene Set Enrichment Analysis for Interpreting Gene Expression Phenotypes with Functional Gene Sets

  • Han, Heonjong;Lee, Sangyoung;Lee, Insuk
    • Molecules and Cells
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    • v.42 no.8
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    • pp.579-588
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    • 2019
  • Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

Expression of B Cell Activating Factor Pathway Genes in Mouse Mammary Gland

  • Choi, S.;Jung, D.J.;Bong, J.J.;Baik, M.
    • Asian-Australasian Journal of Animal Sciences
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    • v.20 no.2
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    • pp.153-159
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    • 2007
  • In our previous study, overexpression of extracellular proteinase inhibitor (Expi) gene accelerated apoptosis of mammary epithelial cells, and induced expression of B cell activating factor (BAFF) gene. In this study, we found induction of BAFF-receptor (BAFF-R) gene expression in the Expi-transfected cells. A proliferation-inducing ligand (APRIL) gene is another TNF family member and the closest known relative of BAFF. We found induction of APRIL gene expression in the Expi-overexpressed apoptotic cells. NF-${\kappa}$B gene was also induced in the Expi-overexpressed cells. Expression patterns of BAFF and APRIL pathway-related genes were examined in in vivo mouse mammary gland at various reproductive stages. Expression levels of BAFF gene were very low at early pregnancy, increased from mid-pregnancy, and peaked at lactation, and thereafter decreased at involution stages of mammary gland. Expression of BAFF-R gene was highly induced in involution stages compared to lactation stages. Thus, expression patterns of BAFF-R gene were correlated to apoptotic status of mammary gland: active apoptosis of mammary epithelial cells occurs at involution stage of mammary gland. Expression levels of NF-${\kappa}$B gene were higher in involution stages compared to lactation stages. We analyzed mRNA levels of bcl-2 family genes from different stages of mammary development. Bcl-2 gene expression was relatively constant during lactation and involution stages. There was a slight increase in bcl-xL gene expression in involution stages compared to lactation state. Bax gene expression was highly induced in involution stage. Our results suggest that signaling pathways activated by both BAFF and ARRIL in mammary gland point towards NF-${\kappa}$B activation which causes upregulation of bax.

Analysis of the Caenorhabditis elegans dlk-1 Gene Expression

  • Lee, Bum-Noh;Cho, Nam-Jeong
    • Animal cells and systems
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    • v.9 no.3
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    • pp.107-111
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    • 2005
  • C. elegans DLK-1 has been reported to play an important role in synaptogenesis by shaping the structure of presynaptic terminal. In this study, we investigated the expression pattern and regulation of the dlk-1 gene in C. elegans. To determine the expression pattern, we made a dlk-1::gfp fusion construct, named pPDdg1, which consisted of -2.2 kb 5' upstream region, the first exon, the first intron, and a part of the second exon of the dlk-1 gene. By microinjecting this construct into the worm, we observed that the DLK-1::GFP was expressed mainly in neurons. We next examined the regulatory elements of gene expression by deletion analysis of pPDdg1. Removal of a large portion of the 5' upstream region (${\Delta}-361$ to -2246) of the gene had little effect on the expression pattern, whereas deletion of the first intron led to elimination of the DLK-1::GFP expression in most of the neurons. Our results suggest that the first intron of the C. elegans dlk-1 gene contains the regulatory element critical for gene expression.

Gene Set and Pathway Analysis of Microarray Data (프마이크로어레이 데이터의 유전자 집합 및 대사 경로 분석)

  • Kim Seon-Young
    • KOGO NEWS
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    • v.6 no.1
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    • pp.29-33
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    • 2006
  • Gene set analysis is a new concept and method. to analyze and interpret microarray gene expression data and tries to extract biological meaning from gene expression data at gene set level rather than at gene level. Compared with methods which select a few tens or hundreds of genes before gene ontology and pathway analysis, gene set analysis identifies important gene ontology terms and pathways more consistently and performs well even in gene expression data sets with minimal or moderate gene expression changes. Moreover, gene set analysis is useful for comparing multiple gene expression data sets dealing with similar biological questions. This review briefly summarizes the rationale behind the gene set analysis and introduces several algorithms and tools now available for gene set analysis.

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