ArticleBMC bioinformatics2006
Genome-wide prediction of transcriptional regulatory elements of human promoters using gene expression and promoter analysis data.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 29 citations in OpenAlex.
- Genome-Wide Computational Identification of Biologically Significant Cis-Regulatory Elements and Associated Transcription Factors from Rice.Plants (Basel, Switzerland) · 2019Article
- Development of a novel prediction method of cis-elements to hypothesize collaborative functions of cis-element pairs in iron-deficient rice.Rice (New York, N.Y.) · 2013Article
- A computational profiling of changes in gene expression and transcription factors induced by vFLIP K13 in primary effusion lymphoma.PloS one · 2012Article
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- Functional analysis: evaluation of response intensities--tailoring ANOVA for lists of expression subsets.BMC bioinformatics · 2010Article
- Gene set-based module discovery decodes cis-regulatory codes governing diverse gene expression across human multiple tissues.PloS one · 2010Article
- A computational evaluation of over-representation of regulatory motifs in the promoter regions of differentially expressed genes.BMC bioinformatics · 2010Article
- Genome-wide transcription factor binding site/promoter databases for the analysis of gene sets and co-occurrence of transcription factor binding motifs.BMC genomics · 2010Article
- GSMA: Gene Set Matrix Analysis, An Automated Method for Rapid Hypothesis Testing of Gene Expression Data.Bioinformatics and biology insights · 2009Article
- Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.Plant molecular biology · 2009Article
- Accuracy and application of the motif expression decomposition method in dissecting transcriptional regulation.Nucleic acids research · 2008Article
- A gene sets approach for identifying prognostic gene signatures for outcome prediction.BMC genomics · 2008Article
- Global gene expression analysis and regulation of the principal genes expressed in bovine placenta in relation to the transcription factor AP-2 family.Reproductive biology and endocrinology : RB&E · 2007Article
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2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundA complete understanding of the regulatory mechanisms of gene expression is the next important issue of genomics. Many bioinformaticians have developed methods and algorithms for predicting transcriptional regulatory mechanisms from sequence, gene expression, and binding data. However, most of these studies involved the use of yeast which has much simpler regulatory networks than human and has many genome wide binding data and gene expression data under diverse conditions. Studies of genome wide transcriptional networks of human genomes currently lag behind those of yeast.
resultsWe report herein a new method that combines gene expression data analysis with promoter analysis to infer transcriptional regulatory elements of human genes. The Z scores from the application of gene set analysis with gene sets of transcription factor binding sites (TFBSs) were successfully used to represent the activity of TFBSs in a given microarray data set. A significant correlation between the Z scores of gene sets of TFBSs and individual genes across multiple conditions permitted successful identification of many known human transcriptional regulatory elements of genes as well as the prediction of numerous putative TFBSs of many genes which will constitute a good starting point for further experiments. Using Z scores of gene sets of TFBSs produced better predictions than the use of mRNA levels of a transcription factor itself, suggesting that the Z scores of gene sets of TFBSs better represent diverse mechanisms for changing the activity of transcription factors in the cell. In addition, cis-regulatory modules, combinations of co-acting TFBSs, were readily identified by our analysis.
conclusionBy a strategic combination of gene set level analysis of gene expression data sets and promoter analysis, we were able to identify and predict many transcriptional regulatory elements of human genes. We conclude that this approach will aid in decoding some of the important transcriptional regulatory elements of human genes.
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