ArticleNucleic acids research2006
Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae.
Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
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- Modeling co-occupancy of transcription factors using chromatin features.Nucleic acids research · 2016Article
- Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets.PloS one · 2016Article
- Regulatory Cross-Talks and Cascades in Rice Hormone Biosynthesis Pathways Contribute to Stress Signaling.Frontiers in plant science · 2016Article
- YCRD: Yeast Combinatorial Regulation Database.PloS one · 2016Article
- CoopTFD: a repository for predicted yeast cooperative transcription factor pairs.Database : the journal of biological databases and curation · 2016Article
- cDREM: inferring dynamic combinatorial gene regulation.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Predicted trans-acting siRNAs in the human brain.International journal of molecular sciences · 2015Article
- PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast.BMC bioinformatics · 2015Article
- Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast.BMC genomics · 2015Article
- Deciphering Cis-Regulatory Element Mediated Combinatorial Regulation in Rice under Blast Infected Condition.PloS one · 2015Article
- Automated genomic context analysis and experimental validation platform for discovery of prokaryote transcriptional regulator functions.BMC genomics · 2014Article
- Profiling the transcription factor regulatory networks of human cell types.Nucleic acids research · 2014Article
- CCAT: Combinatorial Code Analysis Tool for transcriptional regulation.Nucleic acids research · 2014Article
- A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms.BMC systems biology · 2014Article
- Identifying cooperative transcription factors in yeast using multiple data sources.BMC systems biology · 2014Article
- Statistical significance of combinatorial regulations.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
- Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices.Nucleic acids research · 2012Article
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6 authors.
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Abstract
Combinatorial regulation by transcription factor complexes is an important feature of eukaryotic gene regulation. Here, we propose a new method for identification of interactions between transcription factors (TFs) that relies on the relationship of their binding sites, and we test it using Saccharomyces cerevisiae as a model system. The algorithm predicts interacting TF pairs based on the co-occurrence of their binding motifs and the distance between the motifs in promoter sequences. This allows investigation of interactions between TFs without known binding motifs or expression data. With this approach, 300 significant interactions involving 77 TFs were identified. These included more than 70% of the known protein-protein interactions. Approximately half of the detected interacting motif pairs showed strong preferences for particular distances and orientations in the promoter sequences. These one dimensional features may reflect constraints on allowable spatial arrangements for protein-protein interactions. Evidence for biological relevance of the observed characteristic distances is provided by the finding that target genes with the same characteristic distances show significantly higher co-expression than those without preferred distances. Furthermore, the observed interactions were dynamic: most of the TF pairs were not constitutively active, but rather showed variable activity depending on the physiological condition of the cells. Interestingly, some TF pairs active in multiple conditions showed preferences for different distances and orientations depending on the condition. Our prediction and characterization of TF interactions may help to understand the transcriptional regulatory networks in eukaryotic systems.
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