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Analysis of promoter regions of co-expressed genes identified by microarray analysis

Veerla, Srinivas LU and Höglund, Mattias LU (2006) In BMC Bioinformatics 7.
Abstract
Background: The use of global gene expression profiling to identify sets of genes with similar expression patterns is rapidly becoming a widespread approach for understanding biological processes. A logical and systematic approach to study co-expressed genes is to analyze their promoter sequences to identify transcription factors that may be involved in establishing specific profiles and that may be experimentally investigated. Results: We introduce promoter clustering i.e. grouping of promoters with respect to their high scoring motif content, and show that this approach greatly enhances the identification of common and significant transcription factor binding sites (TFBS) in co-expressed genes. We apply this method to two different... (More)
Background: The use of global gene expression profiling to identify sets of genes with similar expression patterns is rapidly becoming a widespread approach for understanding biological processes. A logical and systematic approach to study co-expressed genes is to analyze their promoter sequences to identify transcription factors that may be involved in establishing specific profiles and that may be experimentally investigated. Results: We introduce promoter clustering i.e. grouping of promoters with respect to their high scoring motif content, and show that this approach greatly enhances the identification of common and significant transcription factor binding sites (TFBS) in co-expressed genes. We apply this method to two different dataset, one consisting of micro array data from 108 leukemias (AMLs) and a second from a time series experiment, and show that biologically relevant promoter patterns may be obtained using phylogenetic foot-printing methodology. In addition, we also found that 15% of the analyzed promoter regions contained transcription factors start sites for additional genes transcribed in the opposite direction. Conclusion: Promoter clustering based on global promoter features greatly improve the identification of shared TFBS in co-expressed genes. We believe that the outlined approach may be a useful first step to identify transcription factors that contribute to specific features of gene expression profiles. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
BMC Bioinformatics
volume
7
publisher
BioMed Central
external identifiers
  • pmid:16916454
  • wos:000240417200001
  • scopus:33748351992
ISSN
1471-2105
DOI
10.1186/1471-2105-7-384
language
English
LU publication?
yes
id
b30785d8-602a-4f62-970e-2d5f908cd17e (old id 394266)
date added to LUP
2007-10-20 15:29:34
date last changed
2018-07-08 04:00:33
@article{b30785d8-602a-4f62-970e-2d5f908cd17e,
  abstract     = {Background: The use of global gene expression profiling to identify sets of genes with similar expression patterns is rapidly becoming a widespread approach for understanding biological processes. A logical and systematic approach to study co-expressed genes is to analyze their promoter sequences to identify transcription factors that may be involved in establishing specific profiles and that may be experimentally investigated. Results: We introduce promoter clustering i.e. grouping of promoters with respect to their high scoring motif content, and show that this approach greatly enhances the identification of common and significant transcription factor binding sites (TFBS) in co-expressed genes. We apply this method to two different dataset, one consisting of micro array data from 108 leukemias (AMLs) and a second from a time series experiment, and show that biologically relevant promoter patterns may be obtained using phylogenetic foot-printing methodology. In addition, we also found that 15% of the analyzed promoter regions contained transcription factors start sites for additional genes transcribed in the opposite direction. Conclusion: Promoter clustering based on global promoter features greatly improve the identification of shared TFBS in co-expressed genes. We believe that the outlined approach may be a useful first step to identify transcription factors that contribute to specific features of gene expression profiles.},
  author       = {Veerla, Srinivas and Höglund, Mattias},
  issn         = {1471-2105},
  language     = {eng},
  publisher    = {BioMed Central},
  series       = {BMC Bioinformatics},
  title        = {Analysis of promoter regions of co-expressed genes identified by microarray analysis},
  url          = {http://dx.doi.org/10.1186/1471-2105-7-384},
  volume       = {7},
  year         = {2006},
}