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Title 

Genome-wide prediction of transcriptional regulatory elements of human promoters using gene expression and promoter analysis data

Authors 

Seon-Young KimYong Sung Kim

Publisher 

BioMed Central

Issue Date 

2006

Citation 

BMC Bioinformatics, vol. 7, no. 0, pp. 330-330

Keywords 

correlation analysisdata analysisgene activitygene expressionhumanhuman genomepredictionpromoter region

Abstract 

Background: A 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. Results: We 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. Conclusion: By 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.

ISSN 

1471-2105

Link 

http://dx.doi.org/10.1186/1471-2105-7-330

Appears in Collections

1. Journal Articles > Journal Articles

Registered Date

2019-05-02


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