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Global Mapping of Histone H3 K4 and K27 Trimethylation: Lineage Fate Determination of Differentiating CD4+ T Cells

GSE14254 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2009/01/21 Platform GPL9185
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 12 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
Mus musculus (mouse)
Samples
12 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Multipotential naïve CD4+ T cells differentiate into distinct lineages including Th1, Th2, Th17, and inducible T regulatory (iTreg) cells. The remarkable diversity of CD4+ T cells begs the question whether the observed changes reflect terminal differentiation with heritable epigenetic modifications or plasticity in T cell responses. We generated genome-wide histone H3 lysine 4 (H3K4) and lysine 27 (H3K27) trimethylation maps in naïve, Th1, Th2, Th17, iTreg, and natural (n)Treg cells.
Read the full summary ↓
We find that while modifications of signature cytokine genes (Ifng, Il4, and Il17) partially conform to the expectation of lineage commitment, critical transcription factors such as Tbx21 exhibit a broad spectrum of epigenetic states, consistent with our demonstration of T-bet and IFN-γ induction in nTreg cells. Our data suggest an epigenetic mechanism underlying the specificity and plasticity of effector and regulatory T cells and also provide a framework for understanding complexity of CD4+ T helper cell differentiation.
This dataset
Open GSE14254 in finder → Find more mouse methylation datasets →
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.

Series matrix ↓
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 GSE14254_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA111207 and SRA study SRP000706. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 19144320) ↗
Samples in this study

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+ 12 more — browse all 12 samples with per-sample file links →

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