CRISPR screens uncover CRTC3 as a regulator of IFN-γ induced ferroptosis in hepato-cellular carcinoma
RNA-seq (gene expression) · human · 12 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 12 samples: 3 × CRTC3_WT vs 3 × CRTC3_KO vs 2 × SK-HEP-1_day vs 2 × HepG2_dayGroups 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.
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 GSE206907_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA852587 and SRA study SRP383497. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 10 samples.
How we read this: 4 replicated groups read from 12 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.
- GSM6266713 SK-HEP-1_day0 SK-HEP-1_day
- GSM6266714 SK-HEP-1_day14 SK-HEP-1_day
- GSM6266715 SK-HEP-1_IFN
- GSM6266716 HepG2_day0 HepG2_day
- GSM6266717 HepG2_day14 HepG2_day
- GSM6266718 HepG2_IFN
- GSM6266719 CRTC3_WT rep1 CRTC3_WT
- GSM6266720 CRTC3_WT rep2 CRTC3_WT
- GSM6266721 CRTC3_WT rep3 CRTC3_WT
- GSM6266722 CRTC3_KO rep1 CRTC3_KO
- GSM6266723 CRTC3_KO rep2 CRTC3_KO
- GSM6266724 CRTC3_KO rep3 CRTC3_KO
- GSE245249 CRISPR activation screens identify the SWI-SNF ATPases as suppressors of ferroptosis 65 samples shares crispr, ferroptosis, screens
- GSE288996 Simultaneous single cell CRISPR, RNA, and ATAC-seq enables multiomic CRISPR screens to identify gene regulatory relationships 15 samples shares crispr, regulator, screens
- GSE245239 CRISPR activation screens identify the SWI-SNF ATPases as suppressors of ferroptosis [NGS3752_RNA] 12 samples shares crispr, ferroptosis, screens
- GSE293514 Development of a split-toxin CRISPR screening platform to systematically identify upstream regulators of human myoblast fusion 57 samples shares crispr, regulator
- GSE301119 Integrative analysis of pooled CRISPR screens provide functional insights into AD GWAS risk genes 28 samples shares crispr, screens
- GSE279036 Genome-wide CRISPR screen identifies Menin and SUZ12 as regulators of human developmental timing. 102 samples shares crispr, regulator
- GSE308629 Pooled CRISPR screens identify genes and non-coding genomic regions that regulate red blood cell density 99 samples shares crispr, screens
- GSE224986 Single-cell CRISPRi screens of breast cancer associated enhancers 94 samples shares crispr, screens
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.