Functional genomics assays, including ChIP-seq, ATAC-seq, RNA-seq, GRO-seq, and in situ Hi-C, in the context of replicative senescence
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 223 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
- 223 samples: 223 samples, each with a distinct title — no replicated groupsGroups 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
- Ligand-specific regulation of a binary enhancer code dictating cellular senescenceProceedings of the National Academy of Sciences of the United States of America · 2025
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE146585_RAW.tar (it can run to tens of gigabytes).
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 PRJNA610876 and SRA study SRP251890. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 223 sample titles: 223 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.
- GSM4395086 Tom161_BJ_Young_anti-H3K27ac
- GSM4395087 Tom162_BJ_Intermediate_Early_anti-H3K27ac
- GSM4395089 Tom163_BJ_Intermediate_Late_anti-H3K27ac
- GSM4395091 Tom164_BJ_Senescent_anti-H3K27ac
- GSM4395092 Tom165_BJ_Young_anti-H3K79me3
- GSM4395094 Tom166_BJ_Intermediate_Early_anti-H3K79me3
- GSM4395096 Tom167_BJ_Intermediate_Late_anti-H3K79me3
- GSM4395097 Tom168_BJ_Senescent_anti-H3K79me3
- GSM4395099 TS185_BJ_Young_anti-H3K4me2
- GSM4395101 TS186_BJ_Intermediate_Early_anti-H3K4me2
- GSM4395103 TS187_BJ_Intermediate_Late_anti-H3K4me2
- GSM4395104 TS188_BJ_Senescent_anti-H3K4me2
- GSM4395106 TS121_BJ_Young_anti_H2Bub
- GSM4395107 TS122_BJ_Intermediate_Early_anti_H2Bub
- GSM4395109 TS123_BJ_Intermediate_Late_anti_H2Bub
- GSM4395111 TS124_BJ_Senescent_anti_H2Bub
- GSM4395113 TS125_BJ_Young_anti_h3k79me2
- GSM4395114 TS126_BJ_Intermediate_Early_anti_h3k79me2
- GSM4395116 TS127_BJ_Intermediate_Late_anti_h3k79me2
- GSM4395118 TS128_BJ_Senescent_anti_h3k79me2
- GSM4395119 TSB23_BJ_Young_anti-p65
- GSM4395121 TSB24_BJ_Intermediate_Early_anti-p65
- GSM4395123 TSB25_BJ_Intermediate_Late_anti-p65
- GSM4395124 TSB26_BJ_Senescent_anti-p65
- GSM4395447 TSB55_BJ_Young_Dox_HA-JMJD6
- GSM4395448 TSB56_BJ_Senescent_Dox_HA-JMJD6
- GSM4395449 TSB65_BJ_Young_Dox_TY1-JMJD6
- GSM4395450 TSB66_BJ_Senescent_Dox_TY1-JMJD6
- GSM4395451 TSC801_IMR90_GFP_Control_anti-H3K27ac
- GSM4395452 TSC802_IMR90_RAS_OIS_anti-H3K27ac
- GSM4395453 TSC803_BJ_Young_anti-NFI_Rep1
- GSM4395454 TSC804_BJ_Senescent_anti-NFI_Rep1
- GSM4395455 TSB922_BJ_Young_anti-NFI_Rep2
- GSM4395456 TSB923_BJ_Senescent_anti-NFI_Rep2
- GSM4395457 TSC825_BJ_Young_anti-SMAD2/3_Rep1
- GSM4395458 TSC826_Bj_Senescent_anti-SMAD2/3_Rep1
- GSM4395459 TSC805_BJ_Young_anti-SMAD2/3_Rep2
- GSM4395460 TSC806_BJ_Senescent_anti-SMAD2/3_Rep2
- GSM4395461 TSB561_BJ_Young_anti-H4R3me2sym_shNS
- GSM4395462 TSB562_BJ_Young_anti-H4R3me2sym_shJMJD6-51
+ 183 more — browse all 223 samples with per-sample file links →
- GSE310672 Progesterone receptors drive advanced breast cancer phenotypes including circulating tumor- and stem-like cell expansion in the context of ESR1 mutation [RNA-seq] 48 samples shares including, rna-seq, context
- GSE289805 Histone H4 lysine 20 tri-methylation safeguards breast cancer lineage fidelity and unfolded protein response [ChIP-seq/ATAC-seq] 126 samples shares chip-seq, atac-seq
- GSE304612 Inhibition of p300/CREBBP catalytic activity drives context-dependent transcriptional activation in AML. [ChIP-seq] 95 samples shares chip-seq, context
- GSE318861 Quantitative analysis of Tat-dependent and host-driven HIV transcription by ChIP-seq and RNA-seq 52 samples shares chip-seq, rna-seq
- GSE296831 Epigenetic Context Defines the Transcriptional Activity of Canonical and Noncanonical NF-kappaB Signaling in Pancreatic Cancer [ChIP-Seq] 48 samples shares chip-seq, context
- GSE303766 Tumor-associated Schwann cell remodeling under metabolic stress via lactate sensing orchestrates [ChIP-seq, MeRIP-seq, RNA-seq] 36 samples shares chip-seq, rna-seq
- GSE331395 Replicative senescence in neural progenitors induces senescence-associated markers in midbrain organoids 24 samples shares replicative, senescence
- GSE320212 Identifying Senescence and Immune Biomarkers Predictive of Benefit to Combined CDK4/6 and PD-1 Blockade in Dedifferentiated Liposarcoma: Single-Cell RNA-seq of Serial Tumor Biopsies 23 samples shares senescence, rna-seq
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.