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Genome-wide mapping of HATs and HDACs in human CD4+ T cells

GSE15735 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 41 samples Submitted 2009/08/20 Platform GPL570Platform GPL9052
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 41 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
41 samples
Data you can get
A processed expression table (series matrix) and the array files, from NCBI.
Summary (written by the submitters)
Histone acetyltransferases (HATs) and deacetylases (HDACs) function antagonistically to control histone acetylation. As acetylation is a histone mark for active transcription, HATs have been associated with active and HDACs with inactive genes. We describe here genome-wide mapping of HATs and HDACs binding on chromatin and find that both are found at active genes with acetylated histones.
Read the full summary ↓
Our data provide evidence that HATs and HDACs are both targeted to transcribed regions of active genes by phosphorylated RNA Pol II. Furthermore, the majority of HDACs in the human genome function to reset chromatin by removing acetylation at active genes. Inactive genes that are primed by MLL-mediated histone H3K4 methylation are subject to a dynamic cycle of acetylation and deacetylation by transient HAT/HDAC binding, preventing Pol II from binding to these genes but poising them for future activation. Silent genes without any H3K4 methylation signal show no evidence of being bound by HDACs.
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE15735_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA129023 and SRA study SRP002112. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 41 more — browse all 41 samples with per-sample file links →

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