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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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Determining the parameters of the empirical P-value calculation for MatrixREDUCE quality of fit. Shown in black are the value of /r/, the absolute value of the Pearson correlation for randomized data at N = 6 505 genes and a range of PSAM widths Lw. The red line shows the result of a linear fit to the data, which gives rise to the results shown in Equation 1.
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Figure 4: Determining the parameters of the empirical P-value calculation for MatrixREDUCE quality of fit. Shown in black are the value of /r/, the absolute value of the Pearson correlation for randomized data at N = 6 505 genes and a range of PSAM widths Lw. The red line shows the result of a linear fit to the data, which gives rise to the results shown in Equation 1.

Mentions: Combining the observations from the above trials and performing linear regression on PSAM width Lw (Figure 4), the estimator of the mean of /r/ under the distribution as a function of Lw and the number of genes N is given by: (1)while the standard deviation is given by: (2)Thus, a (pseudo-) t-value corresponding to the Pearson correlation r for a MatrixREDUCE optimized PSAM is: (3)Since N > 1000 for all of the data analysed here, the corresponding P-value can be well estimated using a standard normal distribution. We used the empirical /r/ distribution to calculate t-values and P-values for every PSAM in the database. Only those PSAMs with P-values better than 10− 3 were included.Figure 4.


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)

Determining the parameters of the empirical P-value calculation for MatrixREDUCE quality of fit. Shown in black are the value of /r/, the absolute value of the Pearson correlation for randomized data at N = 6 505 genes and a range of PSAM widths Lw. The red line shows the result of a linear fit to the data, which gives rise to the results shown in Equation 1.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 4: Determining the parameters of the empirical P-value calculation for MatrixREDUCE quality of fit. Shown in black are the value of /r/, the absolute value of the Pearson correlation for randomized data at N = 6 505 genes and a range of PSAM widths Lw. The red line shows the result of a linear fit to the data, which gives rise to the results shown in Equation 1.
Mentions: Combining the observations from the above trials and performing linear regression on PSAM width Lw (Figure 4), the estimator of the mean of /r/ under the distribution as a function of Lw and the number of genes N is given by: (1)while the standard deviation is given by: (2)Thus, a (pseudo-) t-value corresponding to the Pearson correlation r for a MatrixREDUCE optimized PSAM is: (3)Since N > 1000 for all of the data analysed here, the corresponding P-value can be well estimated using a standard normal distribution. We used the empirical /r/ distribution to calculate t-values and P-values for every PSAM in the database. Only those PSAMs with P-values better than 10− 3 were included.Figure 4.

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