National Tsing Hua University Institutional Repository:Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 54367/62174 (87%)
Visitors : 14660063      Online Users : 79
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTHU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    NTHUR > College of Electrical Engineering and Computer Science > Department of Electrical Engineering > EE Journal / Magazine Articles  >  Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data

    Please use this identifier to cite or link to this item:

    Title: Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data
    Authors: Wei-Sheng Wu
    Wen-Hsiung Li
    Bor-Sen Chen
    Teacher: 陳博現
    Date: 2007
    Publisher: BioMed Central
    Relation: BMC BIOINFORMATICS   Volume: 8 Article Number: 188   Published: JUN 8 2007
    Keywords: transcription factors
    Temporal Relationship Identification Algorithm
    Abstract: Background
    ChIP-chip data, which indicate binding of transcription factors (TFs) to DNA regions in vivo, are widely used to reconstruct transcriptional regulatory networks. However, the binding of a TF to a gene does not necessarily imply regulation. Thus, it is important to develop methods to identify regulatory targets of TFs from ChIP-chip data.
    We developed a method, called Temporal Relationship Identification Algorithm (TRIA), which uses gene expression data to identify a TF's regulatory targets among its binding targets inferred from ChIP-chip data. We applied TRIA to yeast cell cycle microarray data and identified many plausible regulatory targets of cell cycle TFs. We validated our predictions by checking the enrichments for functional annotation and known cell cycle genes. Moreover, we showed that TRIA performs better than two published methods (MA-Network and MFA). It is known that co-regulated genes may not be co-expressed. TRIA has the ability to identify subsets of highly co-expressed genes among the regulatory targets of a TF. Different functional roles are found for different subsets, indicating the diverse functions a TF could have. Finally, for a control, we showed that TRIA also performs well for cell-cycle irrelevant TFs.
    Finding the regulatory targets of TFs is important for understanding how cells change their transcription program to adapt to environmental stimuli. Our algorithm TRIA is helpful for achieving this purpose.
    Relation Link:
    Appears in Collections:[Department of Electrical Engineering] EE Journal / Magazine Articles
    [Institute of Communications Engineering] COM Journal / Magazine Articles

    Files in This Item:

    File Description SizeFormat
    2030106010078.pdf435KbAdobe PDF833View/Open


    SFX Query


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback