Quantitative Analysis of the Drosophila Segmentation Regulatory Network Using Pattern Generating Potentials

Kazemian, Majid; Blatti, Charles; Richards, Adam; McCutchan, Michael; Wakabayashi-Ito, Noriko; Hammonds, Ann S.; Celniker, Susan E.; Kumar, Sudhir; Wolfe, Scot A.; Brodsky, Michael H.; Sinha, Saurabh

doi:10.1371/journal.pbio.1000456

**Quantitative Analysis of the Drosophila Segmentation Regulatory Network Using Pattern Generating Potentials**

Journal Article · Tue Aug 17 00:00:00 EDT 2010 · PLoS Biology (Online)

DOI:https://doi.org/10.1371/journal.pbio.1000456· OSTI ID:1627163

Kazemian, Majid ^[1]; Blatti, Charles ^[2]; Richards, Adam ^[3]; McCutchan, Michael ^[4]; Wakabayashi-Ito, Noriko ^[3]; Hammonds, Ann S. ^[5]; Celniker, Susan E. ^[5]; Kumar, Sudhir ^[4]; Wolfe, Scot A. ^[3]; Brodsky, Michael H. ^[3]; Sinha, Saurabh ^[6]

University of Illinois at Urbana-Champaign, IL (United States); DOE/OSTI
University of Illinois at Urbana-Champaign, IL (United States)
University of Massachusetts Medical School, Worcester, MA (United States)
Arizona State University, Tempe, AZ (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Univ. of Illinois at Urbana-Champaign, IL (United States). Dept. of Computer Science; Univ. of Illinois at Urbana-Champaign, IL (United States). Inst. of Genomic Biology

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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Institutes of Health; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1627163

Journal Information:: PLoS Biology (Online), Journal Name: PLoS Biology (Online) Journal Issue: 8 Vol. 8; ISSN 1545-7885

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
drosophila melanogaster
embryonic pattern formation
gene expression
gene regulation
invertebrate genomics
network motifs
sequence motif analysis
transcriptional control