Epigenetic reprogramming drives cellular and phenotypic plasticity in liposarcoma
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 42 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)
- Topic
- Soft tissue sarcomaThis series is part of our curated briefing on it.
- Samples
- 42 samples: 42 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.
Read the full summary ↓
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 GSE324208_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 PRJNA1367140. 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 42 sample titles: 42 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.
- GSM9571142 scMultiomic RNA-Seq of LPS,1-DD
- GSM9571143 scMultiomic RNA-Seq of LPS,1-WD
- GSM9571144 scMultiomic RNA-Seq of LPS,2-WD-A
- GSM9571145 scMultiomic RNA-Seq of LPS,2-WD-B
- GSM9571146 scMultiomic RNA-Seq of LPS, 3-DD-A
- GSM9571147 scMultiomic RNA-Seq of LPS,3-DD-B
- GSM9571148 scMultiomic RNA-Seq of LPS,4-DD-A
- GSM9571149 scMultiomic RNA-Seq of LPS, 4-DD-B
- GSM9571150 scMultiomic RNA-Seq of LPS, 4-WD
- GSM9571151 scMultiomic RNA-Seq of LPS, 5-WD-A
- GSM9571152 scMultiomic RNA-Seq of LPS, 5-WD-B
- GSM9571153 scMultiomic RNA-Seq of LPS, 6-WD-A
- GSM9571154 scMultiomic RNA-Seq of LPS, 6-WD-B
- GSM9571155 scMultiomic RNA-Seq of LPS, 7-WD-A
- GSM9571156 scMultiomic RNA-Seq of LPS, 7-WD-B
- GSM9571157 scMultiomic RNA-Seq of LPS, 8-DD-8
- GSM9571158 scMultiomic RNA-Seq of LPS, 9-WD-A
- GSM9571159 scMultiomic RNA-Seq of LPS, 9-WD-B
- GSM9571160 scMultiomic RNA-Seq of LPS, 10-DD
- GSM9571161 scMultiomic RNA-Seq of LPS, 11-WD
- GSM9571162 scMultiomic RNA-Seq of LPS, 12-WD
- GSM9571163 scMultiomic ATAC-Seq of LPS,1-DD
- GSM9571164 scMultiomic ATAC-Seq of LPS,1-WD
- GSM9571165 scMultiomic ATAC-Seq of LPS,2-WD-A
- GSM9571166 scMultiomic ATAC-Seq of LPS,2-WD-B
- GSM9571167 scMultiomic ATAC-Seq of LPS, 3-DD-A
- GSM9571168 scMultiomic ATAC-Seq of LPS,3-DD-B
- GSM9571169 scMultiomic ATAC-Seq of LPS, 4-DD-A
- GSM9571170 scMultiomic ATAC-Seq of LPS, 4-DD-B
- GSM9571171 scMultiomic ATAC-Seq of LPS, 4-WD
- GSM9571172 scMultiomic ATAC-Seq of LPS, 5-WD-A
- GSM9571173 scMultiomic ATAC-Seq of LPS, 5-WD-B
- GSM9571174 scMultiomic ATAC-Seq of LPS, 6-WD-A
- GSM9571175 scMultiomic ATAC-Seq of LPS, 6-WD-B
- GSM9571176 scMultiomic ATAC-Seq of LPS, 7-WD-A
- GSM9571177 scMultiomic ATAC-Seq of LPS, 7-WD-B
- GSM9571178 scMultiomic ATAC-Seq of LPS, 8-DD-8
- GSM9571179 scMultiomic ATAC-Seq of LPS, 9-WD-A
- GSM9571180 scMultiomic ATAC-Seq of LPS, 9-WD-B
- GSM9571181 scMultiomic ATAC-Seq of LPS, 10-DD
+ 2 more — browse all 42 samples with per-sample file links →
- GSE286950 PAX3-FOXO1 drives targetable cell state-dependent metabolic vulnerabilities in rhabdomyosarcoma 16 samples same disease briefing · shares drives
- GSE274482 EHMT1 mediates cellular motility in embryonal rhabdomyosarcoma by activating SOX8 expression. 12 samples same disease briefing · shares cellular
- GSE325716 The MEK/MYC/p21 Axis Drives Senescence Bypass and BCL-xL Dependence in Vincristine-Resistant Rhabdomyosarcoma 4 samples same disease briefing · shares drives
- GSE282611 A multitargeting approach rewires cell identity in fusion-negative rhabdomyosarcoma [snRNAseq and scATACseq] 4 samples same disease briefing
- GSE312030 Combined inhibition of HRAS and MEK induces tumor regression and restores myogenic differentiation in HRAS-mutant rhabdomyosarcoma 159 samples same disease briefing
- GSE303028 CDK8 Inhibition Releases the Muscle Differentiation Blockade in Fusion-driven Alveolar Rhabdomyosarcoma 104 samples same disease briefing
- GSE273829 GABA promotes resistance to immunotherapy of patients with TLS-positive tumors [RNA-seq] 92 samples same disease briefing
- GSE274640 MYOD1L122R induces cancer stem cell pathways to impart chemotherapy and radiation-resistance in aggressive rhabdomyosarcoma. 80 samples same disease briefing
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.