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    Quantitative analysis of the Drosophila segmentation regulatory network using pattern generating potentials

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    Name:
    Quantitative analysis of the ...
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    Genre
    Journal Article
    Date
    2010-08-01
    Author
    Kazemian, M
    Blatti, C
    Richards, A
    McCutchan, M
    Wakabayashi-Ito, N
    Hammonds, AS
    Celniker, SE
    Kumar, S
    Wolfe, SA
    Brodsky, MH
    Sinha, S
    Show allShow less
    Subject
    Animals
    Binding Sites
    Body Patterning
    Computational Biology
    Drosophila
    Enhancer Elements, Genetic
    Gene Expression Regulation, Developmental
    Gene Regulatory Networks
    Insect Proteins
    Models, Genetic
    Protein Binding
    Software
    Transcription Factors
    Show allShow less
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/5537
    
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    DOI
    10.1371/journal.pbio.1000456
    Abstract
    Cis-regulatory modules that drive precise spatial-temporal patterns of gene expression are central to the process of metazoan development. We describe a new computational strategy to annotate genomic sequences based on their "pattern generating potential" and to produce quantitative descriptions of transcriptional regulatory networks at the level of individual protein-module interactions. We use this approach to convert the qualitative understanding of interactions that regulate Drosophila segmentation into a network model in which a confidence value is associated with each transcription factor-module interaction. Sequence information from multiple Drosophila species is integrated with transcription factor binding specificities to determine conserved binding site frequencies across the genome. These binding site profiles are combined with transcription factor expression information to create a model to predict module activity patterns. This model is used to scan genomic sequences for the potential to generate all or part of the expression pattern of a nearby gene, obtained from available gene expression databases. Interactions between individual transcription factors and modules are inferred by a statistical method to quantify a factor's contribution to the module's pattern generating potential. We use these pattern generating potentials to systematically describe the location and function of known and novel cis-regulatory modules in the segmentation network, identifying many examples of modules predicted to have overlapping expression activities. Surprisingly, conserved transcription factor binding site frequencies were as effective as experimental measurements of occupancy in predicting module expression patterns or factor-module interactions. Thus, unlike previous module prediction methods, this method predicts not only the location of modules but also their spatial activity pattern and the factors that directly determine this pattern. As databases of transcription factor specificities and in vivo gene expression patterns grow, analysis of pattern generating potentials provides a general method to decode transcriptional regulatory sequences and networks. © 2010 Kazemian et al.
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    PLoS Biology
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    http://dx.doi.org/10.34944/dspace/5519
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