ArticleProceedings of the National Academy of Sciences of the United States of America2005
Statistical methods for identifying yeast cell cycle transcription factors.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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47 citing papers in PubMed.
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- Bayesian regularization for a nonstationary Gaussian linear mixed effects model.Statistics in medicine · 2022Article
- Crystal structure of the complex of DNA with the C-terminal domain of TYE7 from Saccharomyces cerevisiae.Acta crystallographica. Section F, Structural biology communications · 2021Article
- Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer.Nucleic acids research · 2017Article
- A novel statistical approach for identification of the master regulator transcription factor.BMC bioinformatics · 2017Article
- A Clb/Cdk1-mediated regulation of Fkh2 synchronizesNPJ systems biology and applications · 2017Article
- Article
- YCRD: Yeast Combinatorial Regulation Database.PloS one · 2016Article
- Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets.PloS one · 2016Article
- CoopTFD: a repository for predicted yeast cooperative transcription factor pairs.Database : the journal of biological databases and curation · 2016Article
- Integrative Analysis Reveals Regulatory Programs in Endometriosis.Reproductive sciences (Thousand Oaks, Calif.) · 2015Article
- Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast.BMC genomics · 2015Article
- PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast.BMC bioinformatics · 2015Article
- Periodic mRNA synthesis and degradation co-operate during cell cycle gene expression.Molecular systems biology · 2014Article
- Identifying cooperative transcription factors in yeast using multiple data sources.BMC systems biology · 2014Article
- A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms.BMC systems biology · 2014Article
- DPRP: a database of phenotype-specific regulatory programs derived from transcription factor binding data.Nucleic acids research · 2014Article
- Comparison of targeted maximum likelihood and shrinkage estimators of parameters in gene networks.Statistical applications in genetics and molecular biology · 2012Article
- Understanding transcriptional regulation by integrative analysis of transcription factor binding data.Genome research · 2012Article
- m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence.Genome biology · 2012Article
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3 authors.
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Abstract
Knowing transcription factors (TFs) involved in the yeast cell cycle is helpful for understanding the regulation of yeast cell cycle genes. We therefore developed two methods for predicting (i) individual cell cycle TFs and (ii) synergistic TF pairs. The essential idea is that genes regulated by a cell cycle TF should have higher (lower, if it is a repressor) expression levels than genes not regulated by it during one or more phases of the cell cycle. This idea can also be used to identify synergistic interactions of TFs. Applying our methods to chromatin immunoprecipitation data and microarray data, we predict 50 cell cycle TFs and 80 synergistic TF pairs, including most known cell cycle TFs and synergistic TF pairs. Using these and published results, we describe the behaviors of 50 known or inferred cell cycle TFs in each cell cycle phase in terms of activation/repression and potential positive/negative interactions between TFs. In addition to the cell cycle, our methods are also applicable to other functions.
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