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A co-localization model of paired ChIP-seq data using a large ENCODE data set enables comparison of multiple samples.

ABSTRACT: Deep sequencing approaches, such as chromatin immunoprecipitation by sequencing (ChIP-seq), have been successful in detecting transcription factor-binding sites and histone modification in the whole genome. An approach for comparing two different ChIP-seq data would be beneficial for predicting unknown functions of a factor. We propose a model to represent co-localization of two different ChIP-seq data. We showed that a meaningful overlapping signal and a meaningless background signal can be separated by this model. We applied this model to compare ChIP-seq data of RNA polymerase II C-terminal domain (CTD) serine 2 phosphorylation with a large amount of peak-called data, including ChIP-seq and other deep sequencing data in the Encyclopedia of DNA Elements (ENCODE) project, and then extracted factors that were related to RNA polymerase II CTD serine 2 in HeLa cells. We further analyzed RNA polymerase II CTD serine 7 phosphorylation, of which their function is still unclear in HeLa cells. Our results were characterized by the similarity of localization for transcription factor/histone modification in the ENCODE data set, and this suggests that our model is appropriate for understanding ChIP-seq data for factors where their function is unknown.

SUBMITTER: Maehara K

PROVIDER: S-EPMC3592427 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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A co-localization model of paired ChIP-seq data using a large ENCODE data set enables comparison of multiple samples.

Maehara Kazumitsu K Odawara Jun J Harada Akihito A Yoshimi Tomohiko T Nagao Koji K Obuse Chikashi C Akashi Koichi K Tachibana Taro T Sakata Toshio T Ohkawa Yasuyuki Y

Nucleic acids research 20121103 1

Deep sequencing approaches, such as chromatin immunoprecipitation by sequencing (ChIP-seq), have been successful in detecting transcription factor-binding sites and histone modification in the whole genome. An approach for comparing two different ChIP-seq data would be beneficial for predicting unknown functions of a factor. We propose a model to represent co-localization of two different ChIP-seq data. We showed that a meaningful overlapping signal and a meaningless background signal can be sep ...[more]

PMID: 23125363

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Project description:BackgroundMotif enrichment analysis of transcription factor ChIP-seq data can help identify transcription factors that cooperate or compete. Previously, little attention has been given to comparative motif enrichment analysis of pairs of ChIP-seq experiments, where the binding of the same transcription factor is assayed under different conditions. Such comparative analysis could potentially identify the distinct regulatory partners/competitors of the assayed transcription factor under different conditions or at different stages of development.ResultsWe describe a new methodology for identifying sequence motifs that are differentially enriched in one set of DNA or RNA sequences relative to another set, and apply it to paired ChIP-seq experiments. We show that, using paired ChIP-seq data for a single transcription factor, differential motif enrichment analysis identifies all the known key transcription factors involved in the transformation of non-cancerous immortalized breast cells (MCF10A-ER-Src cells) into cancer stem cells whereas non-differential motif enrichment analysis does not. We also show that differential motif enrichment analysis identifies regulatory motifs that are significantly enriched at constrained locations within the bound promoters, and that these motifs are not identified by non-differential motif enrichment analysis. Our methodology differs from other approaches in that it leverages both comparative enrichment and positional enrichment of motifs in ChIP-seq peak regions or in the promoters of genes bound by the transcription factor.ConclusionsWe show that differential motif enrichment analysis of paired ChIP-seq experiments offers biological insights not available from non-differential analysis. In contrast to previous approaches, our method detects motifs that are enriched in a constrained region in one set of sequences, but not enriched in the same region in the comparative set. We have enhanced the web-based CentriMo algorithm to allow it to perform the constrained differential motif enrichment analysis described in this paper, and CentriMo's on-line interface (http://meme.ebi.edu.au) provides dozens of databases of DNA- and RNA-binding motifs from a full range of organisms. All data and output files presented here are available at http://research.imb.uq.edu.au/t.bailey/supplementary\_data/Lesluyes2014.

Dataset Information

A co-localization model of paired ChIP-seq data using a large ENCODE data set enables comparison of multiple samples.

Publications

A co-localization model of paired ChIP-seq data using a large ENCODE data set enables comparison of multiple samples.

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