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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

GCN4 and amino acid biosynthesis. Predictions by SVM match well with the known biology of Gcn4 control mechanisms. Pathway map generated taken from the Pathway Tool Omics Viewer at SGD (Christie et al. 2004)
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Fig9: GCN4 and amino acid biosynthesis. Predictions by SVM match well with the known biology of Gcn4 control mechanisms. Pathway map generated taken from the Pathway Tool Omics Viewer at SGD (Christie et al. 2004)

Mentions: Gcn4 is a transcription factor in yeast known to control genes in the amino acid biosynthetic pathway (Hinnebusch 1992), and SVM predictions match well with the known biology of Gcn4 control mechanisms. The final classifier for this TF has an F1 score of 0.89, sensitivity of 0.86, and PPV of 0.92. This TF is a master regulator which has known targets in at least 12 amino acid biosynthetic pathways and has been shown by gene expression to induce at least 1/10th of the yeast genome (Hinnebusch and Natarajan 2002). Figure 9 highlights some known targets of Gcn4 in methionine/threonine biosynthesis in the aspartate family pathway. Branch-points from this pathway can ultimately lead to the amino acids methionine, threonine, lysine, and isoleucine. This group is of particular interest to humans since they are essential and not synthesized in the human metabolism. Gcn4 is known to regulate the genes Hom3, Thr1 and Thr4 leading to threonine, lysine, and isoleucine. However, predictions by SVM indicate it also directly targets committed steps of methionine biosynthesis by binding Met2, Met17, and Met6, which are interesting targets for further study.Fig. 9


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

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

GCN4 and amino acid biosynthesis. Predictions by SVM match well with the known biology of Gcn4 control mechanisms. 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

Fig9: GCN4 and amino acid biosynthesis. Predictions by SVM match well with the known biology of Gcn4 control mechanisms. Pathway map generated taken from the Pathway Tool Omics Viewer at SGD (Christie et al. 2004)
Mentions: Gcn4 is a transcription factor in yeast known to control genes in the amino acid biosynthetic pathway (Hinnebusch 1992), and SVM predictions match well with the known biology of Gcn4 control mechanisms. The final classifier for this TF has an F1 score of 0.89, sensitivity of 0.86, and PPV of 0.92. This TF is a master regulator which has known targets in at least 12 amino acid biosynthetic pathways and has been shown by gene expression to induce at least 1/10th of the yeast genome (Hinnebusch and Natarajan 2002). Figure 9 highlights some known targets of Gcn4 in methionine/threonine biosynthesis in the aspartate family pathway. Branch-points from this pathway can ultimately lead to the amino acids methionine, threonine, lysine, and isoleucine. This group is of particular interest to humans since they are essential and not synthesized in the human metabolism. Gcn4 is known to regulate the genes Hom3, Thr1 and Thr4 leading to threonine, lysine, and isoleucine. However, predictions by SVM indicate it also directly targets committed steps of methionine biosynthesis by binding Met2, Met17, and Met6, which are interesting targets for further study.Fig. 9

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