EZH2 gain-of-function mutations distort H3K27me3 landscapes and transform transcriptional responses to PRC2 inhibition [RNA-seq]
RNA-seq (gene expression) · mouse · 88 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
- Mus musculus (mouse)
- Samples
- 88 samples: 88 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
- EZH2 mutations in follicular lymphoma distort H3K27me3 profiles and alter transcriptional responses to PRC2 inhibitionNature communications · 2024
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 GSE218715_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 PRJNA905335 and SRA study SRP409735. 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 88 sample titles: 88 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.
- GSM6755421 PRC2 WT iMEFs rep1 [A988T01 RNA-seq]
- GSM6755422 PRC2 WT iMEFs rep2 [A988T02 RNA-seq]
- GSM6755423 Ezh2_KO iMEFs rep1 [A988T03 RNA-seq]
- GSM6755424 Ezh2_KO iMEFs rep2 [A988T04 RNA-seq]
- GSM6755425 H3.3K27M iMEFs rep1 [A988T09 RNA-seq]
- GSM6755426 H3.3K27M iMEFs rep2 [A988T10 RNA-seq]
- GSM6755427 H3.3K27M Ezh2_KO iMEFs rep1 [A988T11 RNA-seq]
- GSM6755428 H3.3K27M Ezh2_KO iMEFs rep2 [A988T12 RNA-seq]
- GSM6755429 Eed_KO iMEFs rep1 [A988T13 RNA-seq]
- GSM6755430 Eed_KO iMEFs rep2 [A988T14 RNA-seq]
- GSM6755431 PRC2 WT iMEFs rep3 [D557C01 RNA-seq]
- GSM6755432 PRC2 WT iMEFs rep4 [D557C02 RNA-seq]
- GSM6755433 Ezh2_KO iMEFs rep3 [D557C03 RNA-seq]
- GSM6755434 Ezh2_KO iMEFs rep4 [D557C04 RNA-seq]
- GSM6755435 Eed_KO iMEFs rep3 [D557C09 RNA-seq]
- GSM6755436 Eed_KO iMEFs rep4 [D557C10 RNA-seq]
- GSM6755437 H3.3K27M iMEFs rep3 [D557C11 RNA-seq]
- GSM6755438 H3.3K27M iMEFs rep4 [D557C12 RNA-seq]
- GSM6755439 PRC2 WT iMEFs rep5 [D729T13 RNA-seq]
- GSM6755440 PRC2 WT iMEFs rep6 [D729T14 RNA-seq]
- GSM6755441 Eed_KO iMEFs rep5 [D729T19 RNA-seq]
- GSM6755442 Eed_KO iMEFs rep6 [D729T20 RNA-seq]
- GSM6755443 Ezh2_Y641F iMEFs rep1 [A988T05 RNA-seq]
- GSM6755444 Ezh2_Y641F iMEFs rep2 [A988T06 RNA-seq]
- GSM6755445 PRC2 WT iMEFs PRC2i ctrl rep1 [D448-D447T100 RNA-seq]
- GSM6755446 PRC2 WT iMEFs PRC2i ctrl rep2 [D448-D447T106 RNA-seq]
- GSM6755447 Ezh2_Y641F iMEFs PRC2i ctrl rep1 [D448-D447T103 RNA-seq]
- GSM6755448 Ezh2_Y641F iMEFs PRC2i ctrl rep2 [D448-D447T109 RNA-seq]
- GSM6755449 Ezh2_Y641F iMEFs rep3 [D557C05 RNA-seq]
- GSM6755450 Ezh2_Y641F iMEFs rep4 [D557C06 RNA-seq]
- GSM6755451 Ezh2_Y641F iMEFs rep5 [D729T17 RNA-seq]
- GSM6755452 Ezh2_Y641F iMEFs rep6 [D729T18 RNA-seq]
- GSM6755453 PRC2 WT iMEFs PRC2i 0.25µM rep1 [D448-D447T101 RNA-seq]
- GSM6755454 PRC2 WT iMEFs PRC2i 2µM rep1 [D448-D447T102 RNA-seq]
- GSM6755455 Ezh2_Y641F iMEFs PRC2i 0.25µM rep1 [D448-D447T104 RNA-seq]
- GSM6755456 Ezh2_Y641F iMEFs PRC2i 2µM rep1 [D448-D447T105 RNA-seq]
- GSM6755457 PRC2 WT iMEFs PRC2i 0.25µM rep2 [D448-D447T107 RNA-seq]
- GSM6755458 PRC2 WT iMEFs PRC2i 2µM rep2 [D448-D447T108 RNA-seq]
- GSM6755459 Ezh2_Y641F iMEFs PRC2i 0.25µM rep2 [D448-D447T110 RNA-seq]
- GSM6755460 Ezh2_Y641F iMEFs PRC2i 2µM rep2 [D448-D447T111 RNA-seq]
+ 48 more — browse all 88 samples with per-sample file links →
- GSE45982 EZH2 is required for germinal center formation and somatic EZH2 mutations promote lymphoid transformation 61 samples shares ezh2, mutations, transform
- GSE294966 ChIP-seq for beta-catenin, EZH2, EED, KMT2B, H3K4me3, H3K27me3 and sequential chipseq 148 samples shares h3k27me3, ezh2
- GSE303043 UHRF1 drives subtype-independent aggressiveness and immune evasion in small cell lung cancer through GATA2-ST6GALNAC5 and PRC2 interactions 30 samples shares prc2
- GSE205245 H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs [ChIP-seq] 105 samples shares h3k27me3
- GSE266956 Increased translation driven by a non-canonical EZH2 cistrome creates a synthetic vulnerability in enzalutamide-resistant prostate cancer 42 samples shares ezh2
- GSE85390 EZH2 is a potential therapeutic target for H3K27M mutant paediatric gliomas 38 samples shares ezh2
- GSE255572 Chromatin remodeler CHD7 regulates neurogenic gene expression via controlling chromatin accessibility and modifications of histone marks H3K27me3 and H3K4me3 34 samples shares h3k27me3
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.