GEO series

RNA-dependent ChIP-Seq (rChIP-Seq) of MYC and TBP in MCF-7 cells

GSE252695 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 10 samples Submitted 2025/05/08 Platform GPL18573

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 10 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: 10 samples: 5 conditions, mostly in duplicateGroups 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: Integrative characterization of MYC RNA-binding functionCell genomics · 2025

Summary (written by the submitters)

MYC binds to thousands of gene regulatory elements in the genome such as promoters and enhancers and exerts an amplification effect on the expression of most genes, regardless of the E-box motif existence. In this study, we discovered that MYC is an RNA-binding protein with a high affinity to guanosine-rich RNAs. RNAs binding to MYC enhance its chromatin occupancy.

Read the full summary ↓

This process helps on MYC-mediated regulation of gene expression. Mechanistically, Lys355, Arg356, and Arg357 in the basic region of MYC are essential for RNA binding. Alanine mutation of these three amino acids abolishes MYC RNA-binding function but causes minimal damage to MYC/MAX DNA-binding capacity. Only loss of MYC RNA-binding function, while maintaining DNA-binding function, significantly decreases MYC chromatin occupancy in vivo. Our study reveals a new dimension to MYC-mediated gene regulation by demonstrating that RNA-binding activity plays an important role in MYC’s function.

Published in

Integrative characterization of MYC RNA-binding function

Li S, Wang Z, Wang X et al. · Cell genomics 2025 · PMID 40378850 · doi:10.1016/j.xgen.2025.100878

This dataset

Open GSE252695 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE252695_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 PRJNA1062070 and SRA study SRP482330. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40378850) ↗

Study design

5 conditions, mostly in duplicate

ChIP-Seq, MCF-7, no RNase, input, ×2 ChIP-Seq, MCF-7, MYC, no RNase, IP, ×2 ChIP-Seq, MCF-7, MYC, RNase, IP, ×2 ChIP-Seq, MCF-7, TBP, no RNase, IP, ×2 ChIP-Seq, MCF-7, TBP, RNase, IP, ×2

Supports a between-group comparison across 10 samples.

How we read this: 5 replicated groups read from 10 sample titles; they account for 10 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

GSM8005326 ChIP-Seq, MCF-7, no RNase, input, rep1 ChIP-Seq, MCF-7, no RNa…
GSM8005327 ChIP-Seq, MCF-7, no RNase, input, rep2 ChIP-Seq, MCF-7, no RNa…
GSM8005328 ChIP-Seq, MCF-7, MYC, no RNase, IP, rep1 ChIP-Seq, MCF-7, MYC, n…
GSM8005329 ChIP-Seq, MCF-7, MYC, no RNase, IP, rep2 ChIP-Seq, MCF-7, MYC, n…
GSM8005330 ChIP-Seq, MCF-7, MYC, RNase, IP, rep1 ChIP-Seq, MCF-7, MYC, R…
GSM8005331 ChIP-Seq, MCF-7, MYC, RNase, IP, rep2 ChIP-Seq, MCF-7, MYC, R…
GSM8005332 ChIP-Seq, MCF-7, TBP, no RNase, IP, rep1 ChIP-Seq, MCF-7, TBP, n…
GSM8005333 ChIP-Seq, MCF-7, TBP, no RNase, IP, rep2 ChIP-Seq, MCF-7, TBP, n…
GSM8005334 ChIP-Seq, MCF-7, TBP, RNase, IP, rep1 ChIP-Seq, MCF-7, TBP, R…
GSM8005335 ChIP-Seq, MCF-7, TBP, RNase, IP, rep2 ChIP-Seq, MCF-7, TBP, R…

Datasets on a similar topic

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GSE246361 MCF-7 RPMI ERa and FOXA1 ChIP-seq in GDC-9545 and GNE-858 18 samples shares mcf-7, chip-seq
GSE250076 GPS2 and HDAC3 ChIP-Seq and RNA-Seq in GPS2-KO MCF-7 14 samples shares mcf-7, chip-seq
GSE278073 Targeting VRK2 Enhances the Efficacy of Anti-PD-1 Therapy in Hepatocellular Carcinoma by Reducing Direct Phosphorylation and Stability of MYC: CUT-TAG sequencing for MYC in hepatocellular carcinoma cells 16 samples shares myc

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