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Comparative cofactor screens reveal the influence of transactivation domains and core promoters on the mechanisms of transcription.

GSE198944 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 165 samples Submitted 2024/03/01 Platform GPL18573
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 165 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
Homo sapiens (human)
Samples
165 samples: 160 distinct titles, 2 of them repeatedGroups 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
Comparative cofactor screens show the influence of transactivation domains and core promoters on the mechanisms of transcriptionNature genetics · 2024
Summary (written by the submitters)
Eukaryotic transcription factors (TFs) activate gene expression by recruiting cofactors to promoters. However, the relationships between TFs, promoters and their associated cofactors remain poorly understood. Here, we combine GAL4-transactivation assays with comparative CRISPR-Cas9 screens to identify the cofactors required by nine different TFs in human cells.
Read the full summary ↓
Using this dataset, we associate key TFs with their cofactors, classify cofactors as ubiquitous or specific, discover novel transcriptional co-dependencies and demonstrate that certain TFs use the tail 2 and kinase submodules of Mediator to potentiate transcriptional elongation. By employing a reductionistic and comparative approach, we demonstrate that TFs do not display discrete mechanisms of activation. Instead, each TF is dependent on a unique combination of cofactors, which influences distinct steps in the transcriptional process. We also extend our screens to nine different core promoters to explore how core promoter elements influence cofactor dependence. Our data suggest that different classes of promoter are constrained by either initiation or pause release, which influences their dynamic range of gene expression and compatibility with specific cofactors. Overall, our comparative cofactor screens uncover the interplay between TFs, cofactors and core promoters and reveal general principles by which they influence transcription.
Published in
Comparative cofactor screens show the influence of transactivation domains and core promoters on the mechanisms of transcription
Bell CC, Balic JJ, Talarmain L et al. · Nature genetics 2024 · PMID 38769457 · doi:10.1038/s41588-024-01749-z
This dataset
Open GSE198944 in finder → Find more human 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 GSE198944_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 PRJNA817542 and SRA study SRP364491. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38769457) ↗
Study design
160 distinct titles, 2 of them repeated
MED14-dTAG ×4 MED14-DMSO ×3

How we read this: Read from 165 sample titles: 160 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

+ 125 more — browse all 165 samples with per-sample file links →

Datasets on a similar topic

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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.