Learning transcriptional regulation on a genome scale: A theoretical analysis based on gene expression data

    Research output: Contribution to journalArticle

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    Abstract

    The recent advent of high-throughput microarray data has enabled the global analysis of the transcriptome, driving the development and application of computational approaches to study transcriptional regulation on the genome scale, by reconstructing in silico the regulatory interactions of the gene network. Although there are many in-depth reviews of such 'reverse-engineering' methodologies, most have focused on the practical aspect of data mining, and few on the biological problem and the biological relevance of the methodology. Therefore, in this review, from a biological perspective, we used a set of yeast microarray data as a working example, to evaluate the fundamental assumptions implicit in associating transcription factor (TF)-target gene expression levels and estimating TFs' activity, and further explore cooperative models. Finally we confirm that the detailed transcription mechanism is overly-complex for expression data alone to reveal, nevertheless, future network reconstruction studies could benefit from the incorporation of context-specific information, the modeling of multiple layers of regulation (e.g. micro-RNA), or the development of approaches for context-dependent analysis, to uncover the mechanisms of gene regulation.

    Original languageEnglish (US)
    Article numberbbr029
    Pages (from-to)150-161
    Number of pages12
    JournalBriefings in Bioinformatics
    Volume13
    Issue number2
    DOIs
    StatePublished - Mar 2012

    Profile

    Data Mining
    Gene Regulatory Networks
    Gene Expression Profiling
    MicroRNAs
    Computer Simulation
    Transcription Factors
    Yeasts
    Learning
    Genome
    Gene Expression
    Genes
    Gene expression
    Microarrays
    Transcription factors
    Reverse engineering
    Transcription
    RNA
    Yeast
    Data mining
    Throughput

    Keywords

    • Gene expression
    • Network reconstruction
    • Transcription factors
    • Transcriptional regulation

    ASJC Scopus subject areas

    • Molecular Biology
    • Information Systems

    Cite this

    Learning transcriptional regulation on a genome scale : A theoretical analysis based on gene expression data. / Wu, Ming; Chan, Christina.

    In: Briefings in Bioinformatics, Vol. 13, No. 2, bbr029, 03.2012, p. 150-161.

    Research output: Contribution to journalArticle

    Wu, Ming; Chan, Christina / Learning transcriptional regulation on a genome scale : A theoretical analysis based on gene expression data.

    In: Briefings in Bioinformatics, Vol. 13, No. 2, bbr029, 03.2012, p. 150-161.

    Research output: Contribution to journalArticle

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