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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
https://doi.org/10.1007/82_201...
Part of book or chapter of book . 2012 . Peer-reviewed
License: Springer Nature TDM
Data sources: Crossref
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ChIP-Seq and the Complexity of Bacterial Transcriptional Regulation

Authors: James, Galagan; Anna, Lyubetskaya; Antonio, Gomes;

ChIP-Seq and the Complexity of Bacterial Transcriptional Regulation

Abstract

Transcription factors (TFs) play a central role in regulating gene expression in all bacteria. Yet, until recently, studies of TF binding were limited to a small number of factors at a few genomic locations. Chromatin immunoprecipitation followed by sequencing enables mapping of binding sites for TFs in a global and high-throughput fashion. The NIAID funded TB systems biology project http://www.broadinstitute.org/annotation/tbsysbio/home.html aims to map the binding sites for every transcription factor in the genome of Mycobacterium tuberculosis (MTB), the causative agent of human TB. ChIP-Seq data already released through TBDB.org have provided new insight into the mechanisms of TB pathogenesis. But in addition, data from MTB are beginning to challenge many simplifying assumptions associated with gene regulation in all bacteria. In this chapter, we review the global aspects of TF binding in MTB and discuss the implications of these data for our understanding of bacterial gene regulation. We begin by reviewing the canonical model of bacterial transcriptional regulation using the lac operon as the standard paradigm. We then review the use of ChIP-Seq to map the binding sites of DNA-binding proteins and the application of this method to mapping TF binding sites in MTB. Finally, we discuss two aspects of the binding discovered by ChIP-Seq that were unexpected given the canonical model: the substantial binding outside the proximal promoter region and the large number of weak binding sites.

Related Organizations
Keywords

Chromatin Immunoprecipitation, Binding Sites, Lac Operon, Transcription, Genetic, Gene Expression Regulation, Bacterial, Mycobacterium tuberculosis, Sequence Analysis, Transcription Factors

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    42
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
42
Top 10%
Top 10%
Top 10%
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