"Reading the Switches" — Integrating ChIP-seq with Gene Expression

 Gene expression is the readout. But what controls the switches?

If you work on transcription factors, chromatin remodelling, or gene regulation, you've probably done — or dreamed of doing — a ChIP-seq experiment. ChIP-seq tells you where proteins bind on the genome: where your transcription factor of interest sits, which promoters are decorated with active histone marks, where the cell has placed its regulatory bets. When you combine that information with expression data from the same system, you start to see causality rather than just correlation.

R2 makes that integration surprisingly accessible, even if ChIP-seq data processing is not your area.

The platform hosts a growing collection of pre-processed ChIP-seq datasets, covering transcription factor binding, histone modifications and chromatin accessibility across a range of cell lines and tumour types. You don't need to run a single alignment or peak-calling pipeline. The processed data is already there, waiting to be interrogated alongside the expression datasets you already know.

In practice, the integration works through the genome browser. When you navigate to a gene of interest, you can load ChIP-seq tracks for the same or related cell types alongside your expression data. Suddenly you're not just looking at whether a gene is expressed — you're looking at whether its promoter carries an H3K4me3 mark (active transcription), whether an upstream enhancer is decorated with H3K27ac (active enhancer), and whether a transcription factor you care about has a binding peak sitting right on that enhancer.

One of the most compelling features here is the ability to investigate super-enhancers — unusually large and active regulatory regions that are often hijacked in cancer to drive the expression of oncogenes. R2 lets you overlay super-enhancer calls from published datasets onto your genomic view, and ask whether your gene of interest sits within one of these powerful regulatory hubs. In many paediatric cancers, the answer is revealing: genes that look like ordinary oncogenes turn out to be controlled by enormous super-enhancers that make them extraordinarily sensitive to certain therapeutic interventions.

You can also work in the other direction: starting from a list of ChIP-seq peaks and asking which genes they regulate, then connecting those genes to expression patterns in patient cohorts. The chromatin opens a window into the why behind the expression patterns you've been studying all along.

This is Part of an ongoing series on the R2 Genomics Analysis and Visualization Platform, developed at Amsterdam UMC. All analyses can be freely performed at r2.amc.nl. Full tutorials at r2-tutorials.readthedocs.io.