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Loop Stacking Attenuates Chromatin Boundaries in TCR Loci During Thymocyte Development

GSE265888 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 92 samples Submitted 2024/04/27 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 92 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
92 samples: 32 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
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)
Topologically Associating Domains (TADs) partition the genome into self-interacting regions through boundaries. Chromatin configuration plays a crucial role in TCR gene rearrangements, thereby affecting repertoire diversity. To assess the dynamics of chromatin boundaries within TCR genes, we conducted Hi-C assays on double-negative (DN) and double-positive (DP) cells from RAG-deficient mice.
Read the full summary ↓
A chromatin boundary at the 3' end of the Tcrb locus observed in DN cells dissipated in DP cells, thereby altering interactions between the main Vβ cluster and the recombination center. Additionally, the INTs boundary in the Tcra-Tcrd locus disappeared in DP cells, leading to enhanced interactions between the proximal Vα region and recombination center at the Jα segments. Subsequent analysis revealed that the disappearance of boundaries was not attributable to diminished CTCF binding but rather heightened activity within these regions. This study illuminates the developmental dynamics of TAD boundaries in TCR genes, enriching our understanding of their evolving nature.
This dataset
Open GSE265888 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE265888_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 PRJNA1104639 and SRA study SRP504139. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
32 conditions, mostly in triplicate
EACBEwt-Rag1ko-DN-DpnII-HiC ×3 EACBEko-Rag1ko-DN-DpnII-HiC ×3 EACBEwt-Rag1ko-DN-Trbv12-2-3C-HTGTS ×3 EACBEwt-Rag1ko-DN-Eb-3C-HTGTS ×3 EACBEwt-Rag1ko-DN-Ea-3C-HTGTS ×3 EACBEko-Rag1ko-DN-Ea-3C-HTGTS ×3 EACBEwt-Rag1ko-DN-TEA-3C-HTGTS ×3 EACBEwt-Rag1ko-DN-Trdv5-3C-HTGTS ×3 +31 more

Supports a between-group comparison across 85 samples.

How we read this: 32 replicated groups read from 92 sample titles; they account for 85 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 52 more — browse all 92 samples with per-sample file links →

Datasets on a similar topic

Search all mouse ChIP / ATAC / CUT&Tag datasets in GEO →

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