Dataset Information

ChIP-seq and ChIP-exo profiling of Pol II, H2A.Z, and H3K4me3 in human K562 cells.

ABSTRACT: The human K562 chronic myeloid leukemia cell line has long served as an experimental paradigm for functional genomic studies. To systematically and functionally annotate the human genome, the ENCODE consortium generated hundreds of functional genomic data sets, such as chromatin immunoprecipitation coupled to sequencing (ChIP-seq). While ChIP-seq analyses have provided tremendous insights into gene regulation, spatiotemporal insights were limited by a resolution of several hundred base pairs. ChIP-exonuclease (ChIP-exo) is a refined version of ChIP-seq that overcomes this limitation by providing higher precision mapping of protein-DNA interactions. To study the interplay of transcription initiation and chromatin, we profiled the genome-wide locations for RNA polymerase II (Pol II), the histone variant H2A.Z, and the histone modification H3K4me3 using ChIP-seq and ChIP-exo. In this Data Descriptor, we present detailed information on parallel experimental design, data generation, quality control analysis, and data validation. We discuss how these data lay the foundation for future analysis to understand the relationship between the occupancy of Pol II and nucleosome positions at near base pair resolution.

SUBMITTER: Mchaourab ZF

PROVIDER: S-EPMC5839155 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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ChIP-seq and ChIP-exo profiling of Pol II, H2A.Z, and H3K4me3 in human K562 cells.

Mchaourab Zenab F ZF Perreault Andrea A AA Venters Bryan J BJ

Scientific data 20180306

The human K562 chronic myeloid leukemia cell line has long served as an experimental paradigm for functional genomic studies. To systematically and functionally annotate the human genome, the ENCODE consortium generated hundreds of functional genomic data sets, such as chromatin immunoprecipitation coupled to sequencing (ChIP-seq). While ChIP-seq analyses have provided tremendous insights into gene regulation, spatiotemporal insights were limited by a resolution of several hundred base pairs. Ch ...[more]

PMID: 29509191

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Project description:Use of alternative gene promoters that drive widespread cell-type, tissue-type or developmental gene regulation in mammalian genomes is a common phenomenon. Chromatin immunoprecipitation methods coupled with DNA microarray (ChIP-chip) or massive parallel sequencing (ChIP-seq) are enabling genome-wide identification of active promoters in different cellular conditions using antibodies against Pol-II. However, these methods produce enrichment not only near the gene promoters but also inside the genes and other genomic regions due to the non-specificity of the antibodies used in ChIP. Further, the use of these methods is limited by their high cost and strong dependence on cellular type and context.We trained and tested different state-of-art ensemble and meta classification methods for identification of Pol-II enriched promoter and Pol-II enriched non-promoter sequences, each of length 500 bp. The classification models were trained and tested on a bench-mark dataset, using a set of 39 different feature variables that are based on chromatin modification signatures and various DNA sequence features. The best performing model was applied on seven published ChIP-seq Pol-II datasets to provide genome wide annotation of mouse gene promoters.We present a novel algorithm based on supervised learning methods to discriminate promoter associated Pol-II enrichment from enrichment elsewhere in the genome in ChIP-chip/seq profiles. We accumulated a dataset of 11,773 promoter and 46,167 non-promoter sequences, each of length 500 bp, generated from RNA Pol-II ChIP-seq data of five tissues (Brain, Kidney, Liver, Lung and Spleen). We evaluated the classification models in building the best predictor and found that Bagging and Random Forest based approaches give the best accuracy. We implemented the algorithm on seven different published ChIP-seq datasets to provide a comprehensive set of promoter annotations for both protein-coding and non-coding genes in the mouse genome. The resulting annotations contain 13,413 (4,747) protein-coding (non-coding) genes with single promoters and 9,929 (1,858) protein-coding (non-coding) genes with two or more alternative promoters, and a significant number of unassigned novel promoters.Our new algorithm can successfully predict the promoters from the genome wide profile of Pol-II bound regions. In addition, our algorithm performs significantly better than existing promoter prediction methods and can be applied for genome-wide predictions of Pol-II promoters.

Dataset Information

ChIP-seq and ChIP-exo profiling of Pol II, H2A.Z, and H3K4me3 in human K562 cells.

Publications

ChIP-seq and ChIP-exo profiling of Pol II, H2A.Z, and H3K4me3 in human K562 cells.

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