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c-Maf-dependent regulatory T cells mediate immunological tolerance to a gut pathobiont

GSE108184 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2017/12/19 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 12 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
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)
Both microbial and host genetic factors contribute to the pathogenesis of autoimmune disease. Accumulating evidence suggests that microbial species that potentiate chronic inflammation, as in inflammatory bowel disease (IBD), often also colonize healthy individuals. These microbes, including the Helicobacter species, have the propensity to induce pathogenic T cells and are collectively referred to as pathobionts.
Read the full summary ↓
However, an understanding of how such T cells are constrained in healthy individuals is lacking. For this study we wanted to compare the transcriptomes of H. hepaticus-specific T effector cells from mice that were IL-10-deficient to those that were Maf-deficient in the Treg compartment to determine if they share an inflammatory signature. We also compared Maf -sufficient and -deficient polyclonal iTreg to determine molecular targets of the TF c-Maf.
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 GSE108184_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA422768 and SRA study SRP126932. Searching any of these in the dataset finder brings you back here.

Samples in this study

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

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