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SWI/SNF-dependent genes are defined by their chromatin landscape [ChIP-seq_2]

GSE249211 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2024/03/08 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 24 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
24 samples: 24 samples, each with a distinct title — no replicated groupsGroups 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.
Paper
SWI/SNF-dependent genes are defined by their chromatin landscapeCell reports · 2024
Summary (written by the submitters)
The SWI/SNF complex was the first chromatin remodeling machinery discovered. Although it has been extensively investigated, numerous aspects of its regulation and activity are still poorly understood, especially in higher eukaryotes. In mammals, there is not a single SWI/SNF complex (also called BRM or BRG1 associated factors, BAF, complex) but rather a polymorphic family of complexes, with three main subtypes called canonical BAF (cBAF), polybromo-associated BAF (PBAF), and non-canonical BAF (ncBAF), with relatively different specificities.
Read the full summary ↓
The enzymatic motors of the complexes are two mutually exclusive ATPases of the SNF2 family called BRAHMA (BRM, also called SMARCA2) and BRAHMA RELATED GENE 1 (BRG1, also called SMARCA4). Recently, an specific inhibitor of BRM and BRG1 ATPase activity, called BRM014, has been developed. We have extensively investigated the effect of BRM014 on the transcriptome and the chromatin landscape of non-tumoral normal murine mammary (NMuMG) epithelial cells.
Published in
SWI/SNF-dependent genes are defined by their chromatin landscape
Basurto-Cayuela L, Guerrero-Martínez JA, Gómez-Marín E et al. · Cell reports 2024 · PMID 38427563 · doi:10.1016/j.celrep.2024.113855
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 GSE249211_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 PRJNA1047897 and SRA study SRP475449. Searching any of these in the dataset finder brings you back here.

Study design
24 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 24 sample titles: 24 distinct titles with little repetition. 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
Datasets on a similar topic

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