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TransfactomeDB: a resource for exploring the nucleotide sequence specificity and condition-specific regulatory activity of trans-acting factors.

Foat BC, Tepper RG, Bussemaker HJ - Nucleic Acids Res. (2007)

Bottom Line: Accurate and comprehensive information about the nucleotide sequence specificity of trans-acting factors (TFs) is essential for computational and experimental analyses of gene regulatory networks.We present the Yeast Transfactome Database, a repository of sequence specificity models and condition-specific regulatory activities for a large number of DNA- and RNA-binding proteins in Saccharomyces cerevisiae.The sequence specificities in TransfactomeDB, represented as position-specific affinity matrices (PSAMs), are directly estimated from genomewide measurements of TF-binding using our previously published MatrixREDUCE algorithm, which is based on a biophysical model.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Columbia University, New York, New York 10027, USA.

ABSTRACT
Accurate and comprehensive information about the nucleotide sequence specificity of trans-acting factors (TFs) is essential for computational and experimental analyses of gene regulatory networks. We present the Yeast Transfactome Database, a repository of sequence specificity models and condition-specific regulatory activities for a large number of DNA- and RNA-binding proteins in Saccharomyces cerevisiae. The sequence specificities in TransfactomeDB, represented as position-specific affinity matrices (PSAMs), are directly estimated from genomewide measurements of TF-binding using our previously published MatrixREDUCE algorithm, which is based on a biophysical model. For each mRNA expression profile in the NCBI Gene Expression Omnibus, we used sequence-based regression analysis to estimate the post-translational regulatory activity of each TF for which a PSAM is available. The trans-factor activity profiles across multiple experiments available in TransfactomeDB allow the user to explore potential regulatory roles of hundreds of TFs in any of thousands of microarray experiments. Our resource is freely available at http://bussemakerlab.org/TransfactomeDB/

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Comparison with weight matrices from MacIsaac et al. (15) and TRANSFAC. Each weight matrix from MacIsaac et al. (15) or TRANSFAC (5) was converted into a pseudo-PSAM (see Methods). The correlation between the total affinity of each promoter region predicted by the pseudo-PSAM and the fold-enrichment in the ChIP-chip experiment was then computed. These Pearson r values were then compared with the Pearson r values achieved by PSAMs optimized for the same ChIP-chip data by MatrixREDUCE. In all but nine instances, the correlations were better for PSAMs fit by MatrixREDUCE than for pseudo-PSAMs. In those cases where the pseudo-PSAM had a higher correlation, MatrixREDUCE could still improve the fit of the pseudo-PSAM (green lines).
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Figure 3: Comparison with weight matrices from MacIsaac et al. (15) and TRANSFAC. Each weight matrix from MacIsaac et al. (15) or TRANSFAC (5) was converted into a pseudo-PSAM (see Methods). The correlation between the total affinity of each promoter region predicted by the pseudo-PSAM and the fold-enrichment in the ChIP-chip experiment was then computed. These Pearson r values were then compared with the Pearson r values achieved by PSAMs optimized for the same ChIP-chip data by MatrixREDUCE. In all but nine instances, the correlations were better for PSAMs fit by MatrixREDUCE than for pseudo-PSAMs. In those cases where the pseudo-PSAM had a higher correlation, MatrixREDUCE could still improve the fit of the pseudo-PSAM (green lines).

Mentions: Since MatrixREDUCE is based on a biophysical model of DBP–DNA interactions, and since PSAMs are derived by a direct fit of the model to a particular dataset, a PSAM should always do at least as well at explaining genomewide occupancy measurements as a ‘pseudo-PSAM’ (see Methods section and Supplementary Data) derived from the PWM for the same DBP. We tested this assertion by converting all PWMs from TRANSFAC (5) and MacIsaac et al. (15) to pseudo-PSAMs and comparing to the PSAMs inferred by MatrixREDUCE (Figure 3). In 471 of 480 comparisons, the latter better explained the data, as expected. In the nine cases where the pseudo-PSAM performed better than the true PSAM, an even better fit to the data was achieved by allowing MatrixREDUCE to improve the pseudo-PSAM through the PSAM fitting procedure. Those few cases where the the pseudo-PSAMs performed better were likely due to MatrixREDUCE settling on a suboptimal local minimum.Figure 3.


TransfactomeDB: a resource for exploring the nucleotide sequence specificity and condition-specific regulatory activity of trans-acting factors.

Foat BC, Tepper RG, Bussemaker HJ - Nucleic Acids Res. (2007)

Comparison with weight matrices from MacIsaac et al. (15) and TRANSFAC. Each weight matrix from MacIsaac et al. (15) or TRANSFAC (5) was converted into a pseudo-PSAM (see Methods). The correlation between the total affinity of each promoter region predicted by the pseudo-PSAM and the fold-enrichment in the ChIP-chip experiment was then computed. These Pearson r values were then compared with the Pearson r values achieved by PSAMs optimized for the same ChIP-chip data by MatrixREDUCE. In all but nine instances, the correlations were better for PSAMs fit by MatrixREDUCE than for pseudo-PSAMs. In those cases where the pseudo-PSAM had a higher correlation, MatrixREDUCE could still improve the fit of the pseudo-PSAM (green lines).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2238954&req=5

Figure 3: Comparison with weight matrices from MacIsaac et al. (15) and TRANSFAC. Each weight matrix from MacIsaac et al. (15) or TRANSFAC (5) was converted into a pseudo-PSAM (see Methods). The correlation between the total affinity of each promoter region predicted by the pseudo-PSAM and the fold-enrichment in the ChIP-chip experiment was then computed. These Pearson r values were then compared with the Pearson r values achieved by PSAMs optimized for the same ChIP-chip data by MatrixREDUCE. In all but nine instances, the correlations were better for PSAMs fit by MatrixREDUCE than for pseudo-PSAMs. In those cases where the pseudo-PSAM had a higher correlation, MatrixREDUCE could still improve the fit of the pseudo-PSAM (green lines).
Mentions: Since MatrixREDUCE is based on a biophysical model of DBP–DNA interactions, and since PSAMs are derived by a direct fit of the model to a particular dataset, a PSAM should always do at least as well at explaining genomewide occupancy measurements as a ‘pseudo-PSAM’ (see Methods section and Supplementary Data) derived from the PWM for the same DBP. We tested this assertion by converting all PWMs from TRANSFAC (5) and MacIsaac et al. (15) to pseudo-PSAMs and comparing to the PSAMs inferred by MatrixREDUCE (Figure 3). In 471 of 480 comparisons, the latter better explained the data, as expected. In the nine cases where the pseudo-PSAM performed better than the true PSAM, an even better fit to the data was achieved by allowing MatrixREDUCE to improve the pseudo-PSAM through the PSAM fitting procedure. Those few cases where the the pseudo-PSAMs performed better were likely due to MatrixREDUCE settling on a suboptimal local minimum.Figure 3.

Bottom Line: Accurate and comprehensive information about the nucleotide sequence specificity of trans-acting factors (TFs) is essential for computational and experimental analyses of gene regulatory networks.We present the Yeast Transfactome Database, a repository of sequence specificity models and condition-specific regulatory activities for a large number of DNA- and RNA-binding proteins in Saccharomyces cerevisiae.The sequence specificities in TransfactomeDB, represented as position-specific affinity matrices (PSAMs), are directly estimated from genomewide measurements of TF-binding using our previously published MatrixREDUCE algorithm, which is based on a biophysical model.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Columbia University, New York, New York 10027, USA.

ABSTRACT
Accurate and comprehensive information about the nucleotide sequence specificity of trans-acting factors (TFs) is essential for computational and experimental analyses of gene regulatory networks. We present the Yeast Transfactome Database, a repository of sequence specificity models and condition-specific regulatory activities for a large number of DNA- and RNA-binding proteins in Saccharomyces cerevisiae. The sequence specificities in TransfactomeDB, represented as position-specific affinity matrices (PSAMs), are directly estimated from genomewide measurements of TF-binding using our previously published MatrixREDUCE algorithm, which is based on a biophysical model. For each mRNA expression profile in the NCBI Gene Expression Omnibus, we used sequence-based regression analysis to estimate the post-translational regulatory activity of each TF for which a PSAM is available. The trans-factor activity profiles across multiple experiments available in TransfactomeDB allow the user to explore potential regulatory roles of hundreds of TFs in any of thousands of microarray experiments. Our resource is freely available at http://bussemakerlab.org/TransfactomeDB/

Show MeSH
Related in: MedlinePlus