Genome-wide CRISPR screen identifies Menin and SUZ12 as regulators of human developmental timing.
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 102 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
- 102 samples: 41 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
- Genome-wide CRISPR screen identifies Menin and SUZ12 as regulators of human developmental timingNature cell biology · 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 GSE279036_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 PRJNA1170206 and SRA study SRP537152. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 102 samples.
How we read this: 41 replicated groups read from 102 sample titles; they account for 102 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.
- GSM8559823 NT_d0_ATAC-seq_rep1 NT_d0_ATAC-seq
- GSM8559824 MEN1_1_d0_ATAC-seq_rep1 MEN1_1_d0_ATAC-seq
- GSM8559825 MEN1_2_d0_ATAC-seq_rep1 MEN1_2_d0_ATAC-seq
- GSM8559826 SUZ12_1_d0_ATAC-seq_rep1 SUZ12_1_d0_ATAC-seq
- GSM8559827 SUZ12_2_d0_ATAC-seq_rep1 SUZ12_2_d0_ATAC-seq
- GSM8559828 NT_d0_ATAC-seq_rep2 NT_d0_ATAC-seq
- GSM8559829 MEN1_1_d0_ATAC-seq_rep2 MEN1_1_d0_ATAC-seq
- GSM8559830 MEN1_2_d0_ATAC-seq_rep2 MEN1_2_d0_ATAC-seq
- GSM8559831 SUZ12_1_d0_ATAC-seq_rep2 SUZ12_1_d0_ATAC-seq
- GSM8559832 SUZ12_2_d0_ATAC-seq_rep2 SUZ12_2_d0_ATAC-seq
- GSM8559833 NT_d0_set1_RNA-seq_rep1 NT_d0_set1_RNA-seq
- GSM8559834 NT_d1_set1_RNA-seq_rep1 NT_d1_set1_RNA-seq
- GSM8559835 NT_d2_set1_RNA-seq_rep1 NT_d2_set1_RNA-seq
- GSM8559836 NT_d3_set1_RNA-seq_rep1 NT_d3_set1_RNA-seq
- GSM8559837 NT_d4_set1_RNA-seq_rep1 NT_d4_set1_RNA-seq
- GSM8559838 NT_d5_set1_RNA-seq_rep1 NT_d5_set1_RNA-seq
- GSM8559839 NT_d6_set1_RNA-seq_rep1 NT_d6_set1_RNA-seq
- GSM8559840 NT_d7_set1_RNA-seq_rep1 NT_d7_set1_RNA-seq
- GSM8559841 NT_d8_set1_RNA-seq_rep1 NT_d8_set1_RNA-seq
- GSM8559842 NT_d9_set1_RNA-seq_rep1 NT_d9_set1_RNA-seq
- GSM8559843 NT_d10_set1_RNA-seq_rep1 NT_d10_set1_RNA-seq
- GSM8559844 SUZ12_d0_set1_RNA-seq_rep1 SUZ12_d0_set1_RNA-seq
- GSM8559845 SUZ12_d4_set1_RNA-seq_rep1 SUZ12_d4_set1_RNA-seq
- GSM8559846 SUZ12_d8_set1_RNA-seq_rep1 SUZ12_d8_set1_RNA-seq
- GSM8559847 NT_d0_set1_RNA-seq_rep2 NT_d0_set1_RNA-seq
- GSM8559848 NT_d1_set1_RNA-seq_rep2 NT_d1_set1_RNA-seq
- GSM8559849 NT_d2_set1_RNA-seq_rep2 NT_d2_set1_RNA-seq
- GSM8559850 NT_d3_set1_RNA-seq_rep2 NT_d3_set1_RNA-seq
- GSM8559851 NT_d4_set1_RNA-seq_rep2 NT_d4_set1_RNA-seq
- GSM8559852 NT_d5_set1_RNA-seq_rep2 NT_d5_set1_RNA-seq
- GSM8559853 NT_d6_set1_RNA-seq_rep2 NT_d6_set1_RNA-seq
- GSM8559854 NT_d7_set1_RNA-seq_rep2 NT_d7_set1_RNA-seq
- GSM8559855 NT_d8_set1_RNA-seq_rep2 NT_d8_set1_RNA-seq
- GSM8559856 NT_d9_set1_RNA-seq_rep2 NT_d9_set1_RNA-seq
- GSM8559857 NT_d10_set1_RNA-seq_rep2 NT_d10_set1_RNA-seq
- GSM8559858 SUZ12_d0_set1_RNA-seq_rep2 SUZ12_d0_set1_RNA-seq
- GSM8559859 SUZ12_d4_set1_RNA-seq_rep2 SUZ12_d4_set1_RNA-seq
- GSM8559860 SUZ12_d8_set1_RNA-seq_rep2 SUZ12_d8_set1_RNA-seq
- GSM8559861 NT_d0_set1_RNA-seq_rep3 NT_d0_set1_RNA-seq
- GSM8559862 NT_d1_set1_RNA-seq_rep3 NT_d1_set1_RNA-seq
+ 62 more — browse all 102 samples with per-sample file links →
- GSE330227 In vivo genome-wide CRISPR screens in human T cells to enhance T cell therapy for solid tumors 30 samples shares crispr, genome-wide, screen
- GSE303100 Dissecting Resistance to CDK4/6 Inhibition in Experimental Glioma via Genome-wide CRISPR-Cas9 Screening 24 samples shares crispr, genome-wide, screen
- GSE336960 Phenotypic CRISPR screening identifies ZBTB10 as a novel regulator of human trophoblast differentiation 22 samples shares crispr, identifies, screen
- GSE310675 CRISPR-Cas9 Screening Identifies Resistance Mechanisms to KRAS Inhibition in Pancreatic Cancer 12 samples shares crispr, identifies, screen
- GSE308629 Pooled CRISPR screens identify genes and non-coding genomic regions that regulate red blood cell density 99 samples shares crispr, screen
- GSE327424 Virus-like particles enable targeted gene engineering and pooled CRISPR screening in primary human myeloid cells 86 samples shares crispr, screen
- GSE309511 Independent cell type-specific expression and distal regulation of the 9p21 locus cell cycle regulators: p14ARF, p16INK4A, p15INK4A, and ANRIL [CRISPR and scRNA-seq] 32 samples shares crispr, regulators
- GSE278910 Pooled CRISPR screens with joint single-nucleus chromatin accessibility and transcriptome profiling [CUT&RUN] 30 samples shares crispr, screen
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.