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Chrom-seq Identifies RNAs at Chromatin Marks [Chrom_and_RNA_seq]

GSE252807 Mus musculus (mouse) RNA-seq (gene expression) 47 samples Submitted 2024/07/31 Platform GPL24676Platform GPL24247Platform GPL20795
At a glance

RNA-seq (gene expression) · mouse · 47 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
47 samples: 16 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.
Paper
Chrom-seq identifies RNAs at chromatin marksScience advances · 2024
Summary (written by the submitters)
Chromatin marks are associated with transcriptional regulatory activities. Here, We developed a method termed Chrom-seq to efficiently capture RNAs associated with various chromatin marks in living cells. Chrom-seq jointly applies highly specific chromatin-mark reader with APEX2 which catalyzes the oxidation of biotin-aniline to label the adjacent RNAs for isolation by streptavidin-coated beads.
Read the full summary ↓
Using the readers of mCBX7/dPC, mCBX1 and mTAF3, we detected RNA species significantly associated with H3K27me3, H3K9me3 and H3K4me3, respectively. Chrom-seq provides an antibody-free approach to systematically map RNAs at chromatin marks with potential regulatory roles in different epigenetic events.
Published in
Chrom-seq identifies RNAs at chromatin marks
Fan L, Sun W, Lyu Y et al. · Science advances 2024 · PMID 39083617 · doi:10.1126/sciadv.adn1397
This dataset
Open GSE252807 in finder → Find more RNA-seq 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 GSE252807_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: 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 PRJNA1062749 and SRA study SRP482799. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39083617) ↗
Study design
16 conditions, mostly in triplicate
HEK293T_Chromseq_dPC ×3 HEK293T_Chromseq_CBX7 ×3 HEK293T_Chromseq_TAF3Control ×3 HEK293T_Chromseq_TAF3 ×3 HEK293T_RNAseq_Control ×3 HEK293T_RNAseq_CBX7 ×3 HCT116_Chromseq_Control ×3 HCT116_Chromseq_TAF3 ×3 +8 more

Supports a between-group comparison across 47 samples.

How we read this: 16 replicated groups read from 47 sample titles; they account for 47 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

+ 7 more — browse all 47 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.