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Cohesin acts as a transcriptional gatekeeper by restraining pause–release to promote processive elongation [RNA-seq]

GSE300577 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/07/07 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 18 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
Homo sapiens (human)
Samples
18 samples: 6 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)
Cohesin organizes 3D chromatin architecture, including promoter–enhancer loops, yet its loss has surprisingly modest effects on steady-state gene expression. We address this paradox by demonstrating that cohesin acts at different stages of transcription in human cells. First, it promotes Pol II promoter recruitment by facilitating enhancer–promoter communication that maintains active promoter chromatin states.
Read the full summary ↓
Second, it delays pause release by transiently associating with the transcriptional machinery during the pause–release transition. Kinetic modelling suggests that reduced Pol II recruitment and enhanced pause release have compensatory effects, contributing to minimal changes in steady-state gene expression across genes upon cohesin loss. In contrast, cohesin depletion impairs robust transcriptional induction in response to external stimuli. Moreover, cohesin ensures sufficient pausing duration as a quality-control-like step to promote elongation complex assembly and transcription processivity. Here, we show that cohesin regulates multiple transcriptional steps, offering mechanistic insight into cohesin-related diseases, including cancers and cohesinopathies.
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 GSE300577_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 PRJNA1281417 and SRA study SRP594173. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in triplicate
RNAseq, ctrl, ×3 RNAseq, aux, ×3 RNAseq, XRN2, KD, ×3 RNAseq, aux, XRN2, KD, ×3 RNAseq, SMC1, ctrl, ×3 RNAseq, SMC1, aux, ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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
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