Pan-Cancer Analysis Reveals Mutation-Driven TE Dysregulation as a Contributor to Immune Suppression
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 28 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
- 28 samples: 8 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.
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 GSE320398_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 PRJNA1427740 and SRA study SRP678829. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 18 samples.
How we read this: 8 replicated groups read from 28 sample titles; they account for 18 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.
- GSM9538391 NUGC4,ARID1AKO,rep1 NUGC4,ARID1AKO,
- GSM9538392 NUGC4,ARID1AKO,rep2 NUGC4,ARID1AKO,
- GSM9538393 NUGC4,WT,rep1 NUGC4,WT,
- GSM9538394 NUGC4,WT,rep2 NUGC4,WT,
- GSM9538395 NUGC4,WT,rep3 NUGC4,WT,
- GSM9538396 NUGC4,WT,rep4 NUGC4,WT,
- GSM9538397 NUGC4,ARID1A,CRISPRi,vector,rep1 NUGC4,ARID1A,CRISPRi,ve…
- GSM9538398 NUGC4,ARID1A,CRISPRi,vector,rep2 NUGC4,ARID1A,CRISPRi,ve…
- GSM9538399 NUGC4,ARID1A,CRISPRi,sgRNA1,rep1 NUGC4,ARID1A,CRISPRi,sg…
- GSM9538400 NUGC4,ARID1A,CRISPRi,sgRNA1,rep2 NUGC4,ARID1A,CRISPRi,sg…
- GSM9538401 HepG2,CTNNB1,vec,rep1 HepG2,CTNNB1,vec,
- GSM9538402 HepG2,CTNNB1,vec,rep2 HepG2,CTNNB1,vec,
- GSM9538403 HepG2,CTNNB1,OE,rep1 HepG2,CTNNB1,OE,
- GSM9538404 HepG2,CTNNB1,OE,rep2 HepG2,CTNNB1,OE,
- GSM9538423 NUGC4,ATAC,WT
- GSM9538424 NUGC4,ATAC,KO,rep1 NUGC4,ATAC,KO,
- GSM9538425 NUGC4,ATAC,KO,rep2 NUGC4,ATAC,KO,
- GSM9538440 NUGC4,H3K27me3,WT
- GSM9538441 NUGC4,H3K27me3,KO,rep1 NUGC4,H3K27me3,KO,
- GSM9538442 NUGC4,H3K27me3,KO,rep2 NUGC4,H3K27me3,KO,
- GSM9538443 NUGC4,WT,rep1,WGBS
- GSM9538444 NUGC4,WT,rep2,WGBS
- GSM9538445 NUGC4,ARID1AKO,rep1,WGBS
- GSM9538446 NUGC4,ARID1AKO,rep2,WGBS
- GSM9538447 HepG2,CTNNB1,Vec1,WGBS
- GSM9538448 HepG2,CTNNB1,Vec2,WGBS
- GSM9538449 HepG2,CTNNB1,OE,rep1,WGBS
- GSM9538450 HepG2,CTNNB1,OE,rep2,WGBS
- GSE264443 Dynamic gene expression analysis reveals distinct severity phases of immune and cellular dysregulation in COVID-19 189 samples shares dysregulation
- GSE271896 Deep longitudinal immune profiling reveals reprogramming of memory T cells that links to B cell dysregulation with age 868 samples shares dysregulation
- GSE275067 Deep longitudinal immune profiling reveals reprogramming of memory T cells that links to B cell dysregulation with age. 252 samples shares dysregulation
- GSE313660 Single-cell analysis of follicular fluid reveals dysregulation of ovulatory immune function in Polycystic Ovary Syndrome patients undergoing ovarian stimulation 16 samples shares dysregulation
- GSE283203 Multi-omic analysis of chronic myelomonocytic leukemia monocytes reveals metabolic and immune dysregulation leading to altered macrophage polarization [RNAseq] 16 samples shares dysregulation
- GSE298020 PU.1-Activated Genomic Regions Define Low-risk MDS Subsets Characterized by Immune Dysregulation and Disease Progression. 493 samples shares dysregulation
- GSE274284 Multi-omics dissection of human RAG deficiency reveals distinctive patterns of immune dysregulation but a common inflammatory signature 63 samples shares dysregulation
- GSE260603 Dissecting depression symptoms: multi-omics clustering uncovers immune-related subgroups and cell-type specific dysregulation 273 samples shares dysregulation
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.