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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/394266
- author
- Veerla, Srinivas
LU
and Höglund, Mattias
LU
- organization
- publishing date
- 2006
- type
- Contribution to journal
- publication status
- published
- subject
- in
- BMC Bioinformatics
- volume
- 7
- publisher
- BioMed Central (BMC)
- external identifiers
-
- pmid:16916454
- wos:000240417200001
- scopus:33748351992
- pmid:16916454
- 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
- 2016-04-01 16:54:12
- date last changed
- 2025-04-04 14:34:23
@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 (BMC)}},
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}},
doi = {{10.1186/1471-2105-7-384}},
volume = {{7}},
year = {{2006}},
}