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Comparative cofactor screens reveal the influence of transactivation domains and core promoters on the mechanisms of transcription [ChIP-seq]

GSE198940 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 13 samples Submitted 2024/03/01 Platform GPL18573
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 13 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
13 samples
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)
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.
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 GSE198940_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 PRJNA817545 and SRA study SRP364808. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 13 more — browse all 13 samples with per-sample file links →

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