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Beyond Bioinformatics | |
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Title
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Extract-SAGE: An integrated platform for cross-analysis and GA-based selection of SAGE data
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Authors
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Cheng-Hong Yang1, Tsung-Mu Shih1, Yu-Chen Hung2, Hsueh-Wei Chang2,3,4,*, Li-Yeh Chuang5 | ||||||||||||||||||||||||||||
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Affiliation
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1Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Taiwan; 2Graduate Institute of Natural Products, College of Pharmacy, Kaohsiung Medical University, Taiwan; 3Center of Excellence for Environmental Medicine, Kaohsiung Medical University, Taiwan; 4Faculty of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Taiwan; 5Department of Chemical Engineering, I-Shou University, Taiwan
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changhw@kmu.edu.tw; * Corresponding authors
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Article Type
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Software
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Date
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received January 19, 2009; accepted February 06, 2009; published February 27, 2009
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Abstract |
Serial analysis of gene expression (SAGE) is a powerful quantification technique for gene expression data. The huge amount of tag data in SAGE libraries of samples is difficult to analyze with current SAGE analysis tools. Data is often not provided in a biologically significant way for cross-analysis and -comparison, thus limiting its application. Hence, an integrated software platform that can perform such a complex task is required. Here, we implement set theory for cross-analyzing gene expression data among different SAGE libraries of tissue sources; up- or down-regulated tissue-specific tags can be identified computationally. Extract-SAGE employs a genetic algorithm (GA) to reduce the number of genes among the SAGE libraries. Its representative tag mining will facilitate the discovery of the candidate genes with discriminating gene expression.
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Keywords |
SAGE; genetic algorithm; set theory; software
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| Availability |
This software and user manual are freely available at ftp://sage@bio.kuas.edu.tw/Extract-SAGE.zip
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Citation |
Yang et al., Bioinformation 3(7): 291-292 (2009)
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Edited by |
P. Kangueane
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ISSN |
0973-2063
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Publisher |
Biomedical Informatics Publishing Group
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Copyright |
Publisher
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Copyright Transfer Agreement
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The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.
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License
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This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
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