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A generic approach to identify Transcription Factor-specific operator motifs; Inferences for LacI-family mediated regulation in Lactobacillus plantarum WCFS1

Overview of attention for article published in BMC Genomics, March 2008
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Title
A generic approach to identify Transcription Factor-specific operator motifs; Inferences for LacI-family mediated regulation in Lactobacillus plantarum WCFS1
Published in
BMC Genomics, March 2008
DOI 10.1186/1471-2164-9-145
Pubmed ID
Authors

Christof Francke, Robert Kerkhoven, Michiel Wels, Roland J Siezen

Abstract

A key problem in the sequence-based reconstruction of regulatory networks in bacteria is the lack of specificity in operator predictions. The problem is especially prominent in the identification of transcription factor (TF) specific binding sites. More in particular, homologous TFs are abundant and, as they are structurally very similar, it proves difficult to distinguish the related operators by automated means. This also holds for the LacI-family, a family of TFs that is well-studied and has many members that fulfill crucial roles in the control of carbohydrate catabolism in bacteria including catabolite repression. To overcome the specificity problem, a comprehensive footprinting approach was formulated to identify TF-specific operator motifs and was applied to the LacI-family of TFs in the model gram positive organism, Lactobacillus plantarum WCFS1. The main premise behind the approach is that only orthologous sequences that share orthologous genomic context will share equivalent regulatory sites.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Spain 1 2%
Kazakhstan 1 2%
Unknown 48 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 24%
Student > Ph. D. Student 9 18%
Student > Bachelor 8 16%
Student > Master 6 12%
Professor > Associate Professor 4 8%
Other 9 18%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 53%
Biochemistry, Genetics and Molecular Biology 10 20%
Engineering 4 8%
Nursing and Health Professions 1 2%
Mathematics 1 2%
Other 2 4%
Unknown 6 12%