Dataset Information

Analysis of paired end Pol II ChIP-seq and short capped RNA-seq in MCF-7 cells.

ABSTRACT: While a role of promoter-proximal RNA Polymerase II (Pol II) pausing in regulation of eukaryotic gene expression is implied, the mechanisms and dynamics of this process are poorly understood. We performed genome-wide analysis of short capped RNAs (scRNAs) and Pol II chromatin immunoprecipitation sequencing (ChIP-seq) in human breast cancer MCF-7 cells to better understand Pol II pausing (Samarakkody, A., Abbas, A., Scheidegger, A., Warns, J., Nnoli, O., Jokinen, B., Zarns, K., Kubat, B., Dhasarathy, A. and Nechaev, S. (2015) RNA polymerase II pausing can be retained or acquired during activation of genes involved in the epithelial to mesenchymal transition. Nucleic Acids Res43, 3938-3949). The data are available at the NCBI Gene Expression Omnibus under accession number GSE67041. For both ChIP and scRNA samples, we used paired end sequencing on the Illumina MiSeq instrument. For ChIP-seq, the use of paired end sequencing allowed us to avoid ambiguities in center-read definition. For scRNA seq, this allowed us to identify both the 5'-end and the 3'-end in the same run that represent, respectively, the transcription start sites and the locations of Pol II pausing. The sharpening of Pol II ChIP-seqmetagene profiles when aligned against 5'-ends of scRNAs indicates that these RNAs can be used to define the start sites for the majority of mRNA transcription events.

SUBMITTER: Scheidegger A

PROVIDER: S-EPMC4516138 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Analysis of paired end Pol II ChIP-seq and short capped RNA-seq in MCF-7 cells.

Scheidegger Adam A Burkholder Adam A Abbas Ata A Zarns Kris K Samarakkody Ann A Nechaev Sergei S

Genomics data 20150901

While a role of promoter-proximal RNA Polymerase II (Pol II) pausing in regulation of eukaryotic gene expression is implied, the mechanisms and dynamics of this process are poorly understood. We performed genome-wide analysis of short capped RNAs (scRNAs) and Pol II chromatin immunoprecipitation sequencing (ChIP-seq) in human breast cancer MCF-7 cells to better understand Pol II pausing (Samarakkody, A., Abbas, A., Scheidegger, A., Warns, J., Nnoli, O., Jokinen, B., Zarns, K., Kubat, B., Dhasara ...[more]

PMID: 26229744

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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

Analysis of paired end Pol II ChIP-seq and short capped RNA-seq in MCF-7 cells.

Publications

Analysis of paired end Pol II ChIP-seq and short capped RNA-seq in MCF-7 cells.

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