ArticleBMC bioinformatics2007
Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 36 citations in OpenAlex.
- An integrative method to decode regulatory logics in gene transcription.Nature communications · 2017Article
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- Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast.BMC genomics · 2015Article
- Functional redundancy of transcription factors explains why most binding targets of a transcription factor are not affected when the transcription factor is knocked out.BMC systems biology · 2015Article
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- Shrinkage regression-based methods for microarray missing value imputation.BMC systems biology · 2013Article
- Missing value imputation for microarray data: a comprehensive comparison study and a web tool.BMC systems biology · 2013Article
- Identification of infection- and defense-related genes via a dynamic host-pathogen interaction network using a Candida albicans-zebrafish infection model.Journal of innate immunity · 2013Article
- Identifying biologically interpretable transcription factor knockout targets by jointly analyzing the transcription factor knockout microarray and the ChIP-chip data.BMC systems biology · 2012Article
- Different Functional Gene Clusters in Yeast have Different Spatial Distributions of the Transcription Factor Binding Sites.Bioinformatics and biology insights · 2011Article
- Identifying a Transcription Factor's Regulatory Targets from its Binding Targets.Gene regulation and systems biology · 2010Article
- Integrated cellular network of transcription regulations and protein-protein interactions.BMC systems biology · 2010Article
- Global screening of potential Candida albicans biofilm-related transcription factors via network comparison.BMC bioinformatics · 2010Article
- Deciphering the transcriptional circuitry of microRNA genes expressed during human monocytic differentiation.BMC genomics · 2009Article
- Systematic identification of yeast cell cycle transcription factors using multiple data sources.BMC bioinformatics · 2008Article
- Identifying gene regulatory modules of heat shock response in yeast.BMC genomics · 2008Article
- Reconstructing a network of stress-response regulators via dynamic system modeling of gene regulation.Gene regulation and systems biology · 2008Article
- Computational reconstruction of transcriptional regulatory modules of the yeast cell cycle.BMC bioinformatics · 2006Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChIP-chip data, which indicate binding of transcription factors (TFs) to DNA regions in vivo, are widely used to reconstruct transcriptional regulatory networks. However, the binding of a TF to a gene does not necessarily imply regulation. Thus, it is important to develop methods to identify regulatory targets of TFs from ChIP-chip data.
resultsWe developed a method, called Temporal Relationship Identification Algorithm (TRIA), which uses gene expression data to identify a TF's regulatory targets among its binding targets inferred from ChIP-chip data. We applied TRIA to yeast cell cycle microarray data and identified many plausible regulatory targets of cell cycle TFs. We validated our predictions by checking the enrichments for functional annotation and known cell cycle genes. Moreover, we showed that TRIA performs better than two published methods (MA-Network and MFA). It is known that co-regulated genes may not be co-expressed. TRIA has the ability to identify subsets of highly co-expressed genes among the regulatory targets of a TF. Different functional roles are found for different subsets, indicating the diverse functions a TF could have. Finally, for a control, we showed that TRIA also performs well for cell-cycle irrelevant TFs.
conclusionFinding the regulatory targets of TFs is important for understanding how cells change their transcription program to adapt to environmental stimuli. Our algorithm TRIA is helpful for achieving this purpose.
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