Analysis of promoter regions of co-expressed genes identified by microarray analysis
<p>Abstract</p> <p>Background</p> <p>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-ex...
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| Format: | Article |
| Language: | English |
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BMC
2006-08-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/7/384 |
| _version_ | 1811250897118822400 |
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| author | Höglund Mattias Veerla Srinivas |
| author_facet | Höglund Mattias Veerla Srinivas |
| author_sort | Höglund Mattias |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> |
| first_indexed | 2024-04-12T16:11:37Z |
| format | Article |
| id | doaj.art-33785aa405134e009651ee2c752b80bd |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T16:11:37Z |
| publishDate | 2006-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-33785aa405134e009651ee2c752b80bd2022-12-22T03:25:52ZengBMCBMC Bioinformatics1471-21052006-08-017138410.1186/1471-2105-7-384Analysis of promoter regions of co-expressed genes identified by microarray analysisHöglund MattiasVeerla Srinivas<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p>http://www.biomedcentral.com/1471-2105/7/384 |
| spellingShingle | Höglund Mattias Veerla Srinivas Analysis of promoter regions of co-expressed genes identified by microarray analysis BMC Bioinformatics |
| title | Analysis of promoter regions of co-expressed genes identified by microarray analysis |
| title_full | Analysis of promoter regions of co-expressed genes identified by microarray analysis |
| title_fullStr | Analysis of promoter regions of co-expressed genes identified by microarray analysis |
| title_full_unstemmed | Analysis of promoter regions of co-expressed genes identified by microarray analysis |
| title_short | Analysis of promoter regions of co-expressed genes identified by microarray analysis |
| title_sort | analysis of promoter regions of co expressed genes identified by microarray analysis |
| url | http://www.biomedcentral.com/1471-2105/7/384 |
| work_keys_str_mv | AT hoglundmattias analysisofpromoterregionsofcoexpressedgenesidentifiedbymicroarrayanalysis AT veerlasrinivas analysisofpromoterregionsofcoexpressedgenesidentifiedbymicroarrayanalysis |