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Machine learning for regulatory analysis and transcription factor target prediction in yeast.

Holloway DT, Kon M, Delisi C - Syst Synth Biol (2007)

Bottom Line: For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1.Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies.Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data.

View Article: PubMed Central - PubMed

Affiliation: Molecular Biology Cell Biology and Biochemistry, Boston University, Boston, MA, 02215, USA, dth128@bu.edu.

ABSTRACT
High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps-the identity and location of regulatory binding sites within genomes. Still, the full identification of sites, even in lower eukaryotes, remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a set of 104 Saccharomyces cerevisiae regulators indicates that our SVM-based target classification is more sensitive (73 vs. 20%) when specificity and positive predictive value are the same. We have applied our SVM classifier for each transcriptional regulator to all promoters in the yeast genome to obtain thousands of new targets, which are currently being analyzed and refined to limit the risk of classifier over-fitting. For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1. For both transcription factors SVM predictions match well with the known biology of control mechanisms, and possible new roles for these factors are suggested, such as a function for Rap1 in regulating fermentative growth. We also examine the promoter melting temperature curves for the targets of YJR060W, and show that targets of this TF have potentially unique physical properties which distinguish them from other genes. The SVM output automatically provides the means to rank dataset features to identify important biological elements. We use this property to rank classifying k-mers, thereby reconstructing known binding sites for several TFs, and to rank expression experiments, determining the conditions under which Fhl1, the factor responsible for expression of ribosomal protein genes, is active. We can see that targets of Fhl1 are differentially expressed in the chosen conditions as compared to the expression of average and negative set genes. SVM-based classifiers provide a robust framework for analysis of regulatory networks. Processing of classifier outputs can provide high quality predictions and biological insight into functions of particular transcription factors. Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies. Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data.

No MeSH data available.


Related in: MedlinePlus

Rap1 and glycolytic/TCA cycle reaction. Glycolysis leading to acetate and ethanol are shown. The gray box on the left contains a pathway overview of glycolysis, fermentation and the TCA cycle, where red connections are known and yellow are predicted. Rap1 can be seen to regulate key control points in glycolysis and the TCA cycle. Pathway map generated taken from the Pathway Tool Omics Viewer at SGD (Christie et al. 2004)
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Fig10: Rap1 and glycolytic/TCA cycle reaction. Glycolysis leading to acetate and ethanol are shown. The gray box on the left contains a pathway overview of glycolysis, fermentation and the TCA cycle, where red connections are known and yellow are predicted. Rap1 can be seen to regulate key control points in glycolysis and the TCA cycle. Pathway map generated taken from the Pathway Tool Omics Viewer at SGD (Christie et al. 2004)

Mentions: The Rap1 DNA binding factor is a widely known regulator in the cell cycle, acting as a repressor or activator depending on its context. Rap1 is also a key element in the structure of yeast telomeres, where it plays a role in telomere silencing (Pina et al. 2003). In a seemingly contradictory role, Rap1 has also been shown to regulate several glycolytic enzymes, as shown in Fig. 10. The specificity of this glycolytic regulation is dependent on a second factor, Gcr2, which binds to the Rap1/Gcr1 complex but does not contact DNA directly (Deminoff and Santangelo 2001). New predictions by SVM in the pathways of sugar metabolism show good correspondence with expectations for Rap1 (Fig. 10). Most interestingly, the new predictions include both isoforms of the enzyme phosphofructokinase. This step, where fructose-6-phosphase is converted into fructose-1,6-bisphosphate, is the crucial step in sugar breakdown where most metabolic flux through the pathway is controlled (Zubay 1996).Fig. 10


Machine learning for regulatory analysis and transcription factor target prediction in yeast.

Holloway DT, Kon M, Delisi C - Syst Synth Biol (2007)

Rap1 and glycolytic/TCA cycle reaction. Glycolysis leading to acetate and ethanol are shown. The gray box on the left contains a pathway overview of glycolysis, fermentation and the TCA cycle, where red connections are known and yellow are predicted. Rap1 can be seen to regulate key control points in glycolysis and the TCA cycle. Pathway map generated taken from the Pathway Tool Omics Viewer at SGD (Christie et al. 2004)
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2533145&req=5

Fig10: Rap1 and glycolytic/TCA cycle reaction. Glycolysis leading to acetate and ethanol are shown. The gray box on the left contains a pathway overview of glycolysis, fermentation and the TCA cycle, where red connections are known and yellow are predicted. Rap1 can be seen to regulate key control points in glycolysis and the TCA cycle. Pathway map generated taken from the Pathway Tool Omics Viewer at SGD (Christie et al. 2004)
Mentions: The Rap1 DNA binding factor is a widely known regulator in the cell cycle, acting as a repressor or activator depending on its context. Rap1 is also a key element in the structure of yeast telomeres, where it plays a role in telomere silencing (Pina et al. 2003). In a seemingly contradictory role, Rap1 has also been shown to regulate several glycolytic enzymes, as shown in Fig. 10. The specificity of this glycolytic regulation is dependent on a second factor, Gcr2, which binds to the Rap1/Gcr1 complex but does not contact DNA directly (Deminoff and Santangelo 2001). New predictions by SVM in the pathways of sugar metabolism show good correspondence with expectations for Rap1 (Fig. 10). Most interestingly, the new predictions include both isoforms of the enzyme phosphofructokinase. This step, where fructose-6-phosphase is converted into fructose-1,6-bisphosphate, is the crucial step in sugar breakdown where most metabolic flux through the pathway is controlled (Zubay 1996).Fig. 10

Bottom Line: For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1.Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies.Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data.

View Article: PubMed Central - PubMed

Affiliation: Molecular Biology Cell Biology and Biochemistry, Boston University, Boston, MA, 02215, USA, dth128@bu.edu.

ABSTRACT
High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps-the identity and location of regulatory binding sites within genomes. Still, the full identification of sites, even in lower eukaryotes, remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a set of 104 Saccharomyces cerevisiae regulators indicates that our SVM-based target classification is more sensitive (73 vs. 20%) when specificity and positive predictive value are the same. We have applied our SVM classifier for each transcriptional regulator to all promoters in the yeast genome to obtain thousands of new targets, which are currently being analyzed and refined to limit the risk of classifier over-fitting. For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1. For both transcription factors SVM predictions match well with the known biology of control mechanisms, and possible new roles for these factors are suggested, such as a function for Rap1 in regulating fermentative growth. We also examine the promoter melting temperature curves for the targets of YJR060W, and show that targets of this TF have potentially unique physical properties which distinguish them from other genes. The SVM output automatically provides the means to rank dataset features to identify important biological elements. We use this property to rank classifying k-mers, thereby reconstructing known binding sites for several TFs, and to rank expression experiments, determining the conditions under which Fhl1, the factor responsible for expression of ribosomal protein genes, is active. We can see that targets of Fhl1 are differentially expressed in the chosen conditions as compared to the expression of average and negative set genes. SVM-based classifiers provide a robust framework for analysis of regulatory networks. Processing of classifier outputs can provide high quality predictions and biological insight into functions of particular transcription factors. Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies. Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data.

No MeSH data available.


Related in: MedlinePlus