Show simple item record

dc.creatorKazemian, M
dc.creatorBlatti, C
dc.creatorRichards, A
dc.creatorMcCutchan, M
dc.creatorWakabayashi-Ito, N
dc.creatorHammonds, AS
dc.creatorCelniker, SE
dc.creatorKumar, S
dc.creatorWolfe, SA
dc.creatorBrodsky, MH
dc.creatorSinha, S
dc.date.accessioned2021-02-01T00:02:13Z
dc.date.available2021-02-01T00:02:13Z
dc.date.issued2010-08-01
dc.identifier.issn1544-9173
dc.identifier.issn1545-7885
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5519
dc.identifier.other20808951 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5537
dc.description.abstractCis-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.
dc.format.extent51-52
dc.language.isoen
dc.relation.haspartPLoS Biology
dc.relation.isreferencedbyPublic Library of Science (PLoS)
dc.rightsCC BY
dc.subjectAnimals
dc.subjectBinding Sites
dc.subjectBody Patterning
dc.subjectComputational Biology
dc.subjectDrosophila
dc.subjectEnhancer Elements, Genetic
dc.subjectGene Expression Regulation, Developmental
dc.subjectGene Regulatory Networks
dc.subjectInsect Proteins
dc.subjectModels, Genetic
dc.subjectProtein Binding
dc.subjectSoftware
dc.subjectTranscription Factors
dc.titleQuantitative analysis of the Drosophila segmentation regulatory network using pattern generating potentials
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1371/journal.pbio.1000456
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidKumar, Sudhir|0000-0002-9918-8212
dc.date.updated2021-02-01T00:02:09Z
refterms.dateFOA2021-02-01T00:02:14Z


Files in this item

Thumbnail
Name:
Quantitative analysis of the ...
Size:
1.390Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record