Quantitative analysis of the Drosophila segmentation regulatory network using pattern generating potentials.
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.
Kazemian
Majid
M
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America.
Blatti
Charles
C
Richards
Adam
A
McCutchan
Michael
M
Wakabayashi-Ito
Noriko
N
Hammonds
Ann S
AS
Celniker
Susan E
SE
Kumar
Sudhir
S
Wolfe
Scot A
SA
Brodsky
Michael H
MH
Sinha
Saurabh
S
D000818:Animals; D001665:Binding Sites; D019521:Body Patterning / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D004330:Drosophila / Q000196:embryology* / Q000235:genetics / Q000378:metabolism; D004742:Enhancer Elements, Genetic; D018507:Gene Expression Regulation, Developmental; D053263:Gene Regulatory Networks; D019476:Insect Proteins / Q000235:genetics / Q000378:metabolism; D008957:Models, Genetic; D011485:Protein Binding; D012984:Software; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
D019476:Insect Proteins; D014157:Transcription Factors
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