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      TiSGeD: a database for tissue-specific genes

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      Author(s): 1 , 1 , 1 , 1 , 2 , *
      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: Oxford University Press

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          Abstract

          Summary: The tissue-specific genes are a group of genes whose function and expression are preferred in one or several tissues/cell types. Identification of these genes helps better understanding of tissue–gene relationship, etiology and discovery of novel tissue-specific drug targets. In this study, a statistical method is introduced to detect tissue-specific genes from more than 123 125 gene expression profiles over 107 human tissues, 67 mouse tissues and 30 rat tissues. As a result, a novel subject-specialized repository, namely the tissue-specific genes database (TiSGeD), is developed to represent the analyzed results. Auxiliary information of tissue-specific genes was also collected from biomedical literatures.

          Availability: http://bioinf.xmu.edu.cn/databases/TiSGeD/index.html

          Contact: appo@ 123456bioinf.xmu.edu.cn ; zhiliang.ji@ 123456gmail.com

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          ArrayExpress update—from an archive of functional genomics experiments to the atlas of gene expression

          Helen Parkinson, Misha Kapushesky, Nikolay Kolesnikov … (2009)
          ArrayExpress http://www.ebi.ac.uk/arrayexpress consists of three components: the ArrayExpress Repository—a public archive of functional genomics experiments and supporting data, the ArrayExpress Warehouse—a database of gene expression profiles and other bio-measurements and the ArrayExpress Atlas—a new summary database and meta-analytical tool of ranked gene expression across multiple experiments and different biological conditions. The Repository contains data from over 6000 experiments comprising approximately 200 000 assays, and the database doubles in size every 15 months. The majority of the data are array based, but other data types are included, most recently—ultra high-throughput sequencing transcriptomics and epigenetic data. The Warehouse and Atlas allow users to query for differentially expressed genes by gene names and properties, experimental conditions and sample properties, or a combination of both. In this update, we describe the ArrayExpress developments over the last two years.
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            TiGER: A database for tissue-specific gene expression and regulation

            Xiong Liu, Xueping Yu, Donald Zack … (2008)
            Background Understanding how genes are expressed and regulated in different tissues is a fundamental and challenging question. However, most of currently available biological databases do not focus on tissue-specific gene regulation. Results The recent development of computational methods for tissue-specific combinational gene regulation, based on transcription factor binding sites, enables us to perform a large-scale analysis of tissue-specific gene regulation in human tissues. The results are stored in a web database called TiGER (Tissue-specific Gene Expression and Regulation). The database contains three types of data including tissue-specific gene expression profiles, combinatorial gene regulations, and cis-regulatory module (CRM) detections. At present the database contains expression profiles for 19,526 UniGene genes, combinatorial regulations for 7,341 transcription factor pairs and 6,232 putative CRMs for 2,130 RefSeq genes. Conclusion We have developed and made publicly available a database, TiGER, which summarizes and provides large scale data sets for tissue-specific gene expression and regulation in a variety of human tissues. This resource is available at [1].
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              From patterns to pathways: gene expression data analysis comes of age.

              Donna K. Slonim (2002)
              Many different biological questions are routinely studied using transcriptional profiling on microarrays. A wide range of approaches are available for gleaning insights from the data obtained from such experiments. The appropriate choice of data-analysis technique depends both on the data and on the goals of the experiment. This review summarizes some of the common themes in microarray data analysis, including detection of differential expression, clustering, and predicting sample characteristics. Several approaches to each problem, and their relative merits, are discussed and key areas for additional research highlighted.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 1 May 2010
                Publication date (Electronic): 11 March 2010
                Publication date PMC-release: 11 March 2010
                Volume: 26
                Issue: 9
                Pages: 1273-1275
                Affiliations
                1 Key Laboratory for Cell Biology and Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences and 2 The Key Laboratory for Chemical Biology of Fujian Province, School of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, P. R. China
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: Jonathan Wren

                Article
                Publisher ID: btq109
                DOI: 10.1093/bioinformatics/btq109
                PMC ID: 2859128
                PubMed ID: 20223836
                SO-VID: 00043d03-2245-45dc-b346-de1a95a18aab
                Copyright © © The Author(s) 2010. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 3 December 2009
                Date revision received : 18 February 2010
                Date accepted : 7 March 2010
                Categories
                Subject: Applications Note
                Subject: Databases and Ontologies

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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