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Genome-wide prediction and analysis of human chromatin boundary elements.

ABSTRACT: Boundary elements partition eukaryotic chromatin into active and repressive domains, and can also block regulatory interactions between domains. Boundary elements act via diverse mechanisms making accurate feature-based computational predictions difficult. Therefore, we developed an unbiased algorithm that predicts the locations of human boundary elements based on the genomic distributions of chromatin and transcriptional states, as opposed to any intrinsic characteristics that they may possess. Application of our algorithm to ChIP-seq data for histone modifications and RNA Pol II-binding data in human CD4(+) T cells resulted in the prediction of 2542 putative chromatin boundary elements genome wide. Predicted boundary elements display two distinct features: first, position-specific open chromatin and histone acetylation that is coincident with the recruitment of sequence-specific DNA-binding factors such as CTCF, EVI1 and YYI, and second, a directional and gradual increase in histone lysine methylation across predicted boundaries coincident with a gain of expression of non-coding RNAs, including examples of boundaries encoded by tRNA and other non-coding RNA genes. Accordingly, a number of the predicted human boundaries may function via the synergistic action of sequence-specific recruitment of transcription factors leading to non-coding RNA transcriptional interference and the blocking of facultative heterochromatin propagation by transcription-associated chromatin remodeling complexes.

SUBMITTER: Wang J

PROVIDER: S-EPMC3258141 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Genome-wide prediction and analysis of human chromatin boundary elements.

Wang Jianrong J Lunyak Victoria V VV Jordan I King IK

Nucleic acids research 20110919 2

Boundary elements partition eukaryotic chromatin into active and repressive domains, and can also block regulatory interactions between domains. Boundary elements act via diverse mechanisms making accurate feature-based computational predictions difficult. Therefore, we developed an unbiased algorithm that predicts the locations of human boundary elements based on the genomic distributions of chromatin and transcriptional states, as opposed to any intrinsic characteristics that they may possess. ...[more]

PMID: 21930510

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Project description: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.

Dataset Information

Genome-wide prediction and analysis of human chromatin boundary elements.

Publications

Genome-wide prediction and analysis of human chromatin boundary elements.

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