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Comparing expression profiles of genes with similar promoter regions
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Bioinformatics, 18, 2002, 12, S. 1576-1584 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Park, Peter J. Butte, Atul J. Kohane, Isaac S. Park, Peter J. Butte, Atul J. Kohane, Isaac S. |
|---|---|
| author |
Park, Peter J. Butte, Atul J. Kohane, Isaac S. |
| spellingShingle |
Park, Peter J. Butte, Atul J. Kohane, Isaac S. Bioinformatics Comparing expression profiles of genes with similar promoter regions Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
park, peter j. |
| spelling |
Park, Peter J. Butte, Atul J. Kohane, Isaac S. 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/18.12.1576 <jats:title>Abstract</jats:title> <jats:p>Motivation: Gene regulatory elements are often predicted by seeking common sequences in the promoter regions of genes that are clustered together based on their expression profiles. We consider the problem in the opposite direction: we seek to find the genes that have similar promoter regions and determine the extent to which these genes have similar expression profiles.</jats:p> <jats:p>Results: We use the data sets from experiments on Saccharomyces cerevisiae. Our similarity measure for the promoter regions is based on the set of common mapped or putative transcription factor binding sites and other regulatory elements in the upstream region of the genes, as contained in the Saccharomyces cerevisiae Promoter Database. We pair up the genes with high similarity scores and compare their expression levels in time-course experiment data. We find that genes with similar promoter regions on the average have significantly higher correlation, but it can vary widely depending on the genes. This confirms that the presence of similar regulatory elements often does not correspond to similarity in expression profiles and indicates that finding transcription factor binding sites or other regulatory elements starting with the expression patterns may be limited in many cases. Regardless of the correlation, the degree to which the profiles agree under different experimental conditions can be examined to derive hypotheses concerning the role of common regulatory elements. Overall, we find that considering the relationship between the promoter regions and the expression profiles starting with the regulatory elements is a difficult but useful process that can provide valuable insights.</jats:p> <jats:p>Contact: peter_park@harvard.edu</jats:p> <jats:p>* To whom correspondence should be addressed.</jats:p> Comparing expression profiles of genes with similar promoter regions Bioinformatics |
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Comparing expression profiles of genes with similar promoter regions |
| title_unstemmed |
Comparing expression profiles of genes with similar promoter regions |
| title_full |
Comparing expression profiles of genes with similar promoter regions |
| title_fullStr |
Comparing expression profiles of genes with similar promoter regions |
| title_full_unstemmed |
Comparing expression profiles of genes with similar promoter regions |
| title_short |
Comparing expression profiles of genes with similar promoter regions |
| title_sort |
comparing expression profiles of genes with similar promoter regions |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/18.12.1576 |
| publishDate |
2002 |
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1576-1584 |
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<jats:title>Abstract</jats:title>
<jats:p>Motivation: Gene regulatory elements are often predicted by seeking common sequences in the promoter regions of genes that are clustered together based on their expression profiles. We consider the problem in the opposite direction: we seek to find the genes that have similar promoter regions and determine the extent to which these genes have similar expression profiles.</jats:p>
<jats:p>Results: We use the data sets from experiments on Saccharomyces cerevisiae. Our similarity measure for the promoter regions is based on the set of common mapped or putative transcription factor binding sites and other regulatory elements in the upstream region of the genes, as contained in the Saccharomyces cerevisiae Promoter Database. We pair up the genes with high similarity scores and compare their expression levels in time-course experiment data. We find that genes with similar promoter regions on the average have significantly higher correlation, but it can vary widely depending on the genes. This confirms that the presence of similar regulatory elements often does not correspond to similarity in expression profiles and indicates that finding transcription factor binding sites or other regulatory elements starting with the expression patterns may be limited in many cases. Regardless of the correlation, the degree to which the profiles agree under different experimental conditions can be examined to derive hypotheses concerning the role of common regulatory elements. Overall, we find that considering the relationship between the promoter regions and the expression profiles starting with the regulatory elements is a difficult but useful process that can provide valuable insights.</jats:p>
<jats:p>Contact: peter_park@harvard.edu</jats:p>
<jats:p>* To whom correspondence should be addressed.</jats:p> |
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| author | Park, Peter J., Butte, Atul J., Kohane, Isaac S. |
| author_facet | Park, Peter J., Butte, Atul J., Kohane, Isaac S., Park, Peter J., Butte, Atul J., Kohane, Isaac S. |
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| description | <jats:title>Abstract</jats:title> <jats:p>Motivation: Gene regulatory elements are often predicted by seeking common sequences in the promoter regions of genes that are clustered together based on their expression profiles. We consider the problem in the opposite direction: we seek to find the genes that have similar promoter regions and determine the extent to which these genes have similar expression profiles.</jats:p> <jats:p>Results: We use the data sets from experiments on Saccharomyces cerevisiae. Our similarity measure for the promoter regions is based on the set of common mapped or putative transcription factor binding sites and other regulatory elements in the upstream region of the genes, as contained in the Saccharomyces cerevisiae Promoter Database. We pair up the genes with high similarity scores and compare their expression levels in time-course experiment data. We find that genes with similar promoter regions on the average have significantly higher correlation, but it can vary widely depending on the genes. This confirms that the presence of similar regulatory elements often does not correspond to similarity in expression profiles and indicates that finding transcription factor binding sites or other regulatory elements starting with the expression patterns may be limited in many cases. Regardless of the correlation, the degree to which the profiles agree under different experimental conditions can be examined to derive hypotheses concerning the role of common regulatory elements. Overall, we find that considering the relationship between the promoter regions and the expression profiles starting with the regulatory elements is a difficult but useful process that can provide valuable insights.</jats:p> <jats:p>Contact: peter_park@harvard.edu</jats:p> <jats:p>* To whom correspondence should be addressed.</jats:p> |
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| spelling | Park, Peter J. Butte, Atul J. Kohane, Isaac S. 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/18.12.1576 <jats:title>Abstract</jats:title> <jats:p>Motivation: Gene regulatory elements are often predicted by seeking common sequences in the promoter regions of genes that are clustered together based on their expression profiles. We consider the problem in the opposite direction: we seek to find the genes that have similar promoter regions and determine the extent to which these genes have similar expression profiles.</jats:p> <jats:p>Results: We use the data sets from experiments on Saccharomyces cerevisiae. Our similarity measure for the promoter regions is based on the set of common mapped or putative transcription factor binding sites and other regulatory elements in the upstream region of the genes, as contained in the Saccharomyces cerevisiae Promoter Database. We pair up the genes with high similarity scores and compare their expression levels in time-course experiment data. We find that genes with similar promoter regions on the average have significantly higher correlation, but it can vary widely depending on the genes. This confirms that the presence of similar regulatory elements often does not correspond to similarity in expression profiles and indicates that finding transcription factor binding sites or other regulatory elements starting with the expression patterns may be limited in many cases. Regardless of the correlation, the degree to which the profiles agree under different experimental conditions can be examined to derive hypotheses concerning the role of common regulatory elements. Overall, we find that considering the relationship between the promoter regions and the expression profiles starting with the regulatory elements is a difficult but useful process that can provide valuable insights.</jats:p> <jats:p>Contact: peter_park@harvard.edu</jats:p> <jats:p>* To whom correspondence should be addressed.</jats:p> Comparing expression profiles of genes with similar promoter regions Bioinformatics |
| spellingShingle | Park, Peter J., Butte, Atul J., Kohane, Isaac S., Bioinformatics, Comparing expression profiles of genes with similar promoter regions, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | Comparing expression profiles of genes with similar promoter regions |
| title_full | Comparing expression profiles of genes with similar promoter regions |
| title_fullStr | Comparing expression profiles of genes with similar promoter regions |
| title_full_unstemmed | Comparing expression profiles of genes with similar promoter regions |
| title_short | Comparing expression profiles of genes with similar promoter regions |
| title_sort | comparing expression profiles of genes with similar promoter regions |
| title_unstemmed | Comparing expression profiles of genes with similar promoter regions |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/18.12.1576 |