Epigenetic and transcriptional alterations with Kmt2c and Kmt2d knockout
ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 50 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
- Mus musculus (mouse)
- Samples
- 50 samples: 16 conditions, mostly in triplicateGroups 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 GSE264514_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 PRJNA1102881 and SRA study SRP503160. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 48 samples.
How we read this: 16 replicated groups read from 50 sample titles; they account for 48 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.
- GSM8219288 RNAseq_WT_DMSO_1 RNAseq_WT_DMSO
- GSM8219289 RNAseq_WT_DMSO_2 RNAseq_WT_DMSO
- GSM8219290 RNAseq_WT_DMSO_3 RNAseq_WT_DMSO
- GSM8219291 RNAseq_WT_MI503_1 RNAseq_WT_MI503
- GSM8219292 RNAseq_WT_MI503_2 RNAseq_WT_MI503
- GSM8219293 RNAseq_WT_MI503_3 RNAseq_WT_MI503
- GSM8219294 RNAseq_dKO_DMSO_1 RNAseq_dKO_DMSO
- GSM8219295 RNAseq_dKO_DMSO_2 RNAseq_dKO_DMSO
- GSM8219296 RNAseq_dKO_DMSO_3 RNAseq_dKO_DMSO
- GSM8219297 RNAseq_dKO_MI503_1 RNAseq_dKO_MI503
- GSM8219298 RNAseq_dKO_MI503_2 RNAseq_dKO_MI503
- GSM8219299 RNAseq_dKO_MI503_3 RNAseq_dKO_MI503
- GSM8219300 ATACseq_WT_1 ATACseq_WT
- GSM8219301 ATACseq_WT_2 ATACseq_WT
- GSM8219302 ATACseq_WT_3 ATACseq_WT
- GSM8219303 ATACseq_dKO_1 ATACseq_dKO
- GSM8219304 ATACseq_dKO_2 ATACseq_dKO
- GSM8219305 ATACseq_dKO_3 ATACseq_dKO
- GSM8219306 ChIPseq_WT_Input
- GSM8219307 ChIPseq_H3K27ac_WT_1 ChIPseq_H3K27ac_WT
- GSM8219308 ChIPseq_H3K27ac_WT_2 ChIPseq_H3K27ac_WT
- GSM8219309 ChIPseq_H3K27ac_WT_3 ChIPseq_H3K27ac_WT
- GSM8219310 ChIPseq_dKO_Input
- GSM8219311 ChIPseq_H3K27ac_dKO_1 ChIPseq_H3K27ac_dKO
- GSM8219312 ChIPseq_H3K27ac_dKO_2 ChIPseq_H3K27ac_dKO
- GSM8219313 ChIPseq_H3K27ac_dKO_3 ChIPseq_H3K27ac_dKO
- GSM8219314 CutRun_Kmt2a_WT_1 CutRun_Kmt2a_WT
- GSM8219315 CutRun_Kmt2a_WT_2 CutRun_Kmt2a_WT
- GSM8219316 CutRun_Kmt2a_WT_3 CutRun_Kmt2a_WT
- GSM8219317 CutRun_Kmt2a_dKO_1 CutRun_Kmt2a_dKO
- GSM8219318 CutRun_Kmt2a_dKO_2 CutRun_Kmt2a_dKO
- GSM8219319 CutRun_Kmt2a_dKO_3 CutRun_Kmt2a_dKO
- GSM8219320 CutRun_H3K4me1_WT_1 CutRun_H3K4me1_WT
- GSM8219321 CutRun_H3K4me1_WT_2 CutRun_H3K4me1_WT
- GSM8219322 CutRun_H3K4me1_WT_3 CutRun_H3K4me1_WT
- GSM8219323 CutRun_H3K4me2_WT_1 CutRun_H3K4me2_WT
- GSM8219324 CutRun_H3K4me2_WT_2 CutRun_H3K4me2_WT
- GSM8219325 CutRun_H3K4me2_WT_3 CutRun_H3K4me2_WT
- GSM8219326 CutRUn_H3K27me3_WT_1 CutRUn_H3K27me3_WT
- GSM8219327 CutRUn_H3K27me3_WT_2 CutRUn_H3K27me3_WT
+ 10 more — browse all 50 samples with per-sample file links →
- GSE325799 KMT2C and KMT2D amplify GRHL2-driven enhancer activation [CUT&Tag] 80 samples shares kmt2c, kmt2d
- GSE237390 Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 [ChIP-seq] 54 samples shares kmt2c, kmt2d
- GSE289856 Convergence of aging- and rejuvenation-related epigenetic alterations on PRC2 targets 39 samples shares alterations, epigenetic
- GSE247454 Brca1 haploinsufficiency promotes early tumor onset and epigenetic alterations in a mouse model of hereditary breast cancer 38 samples shares alterations, epigenetic
- GSE290849 Acute intervention reveals critical roles of BAF, KMT2D and p300 in MyoD binding on enhancers 143 samples shares kmt2d
- GSE282802 KMT2D controls cerebellar granule cell differentiation by temporally activating neuronal transcriptional factor genes [CUT&RUN] 10 samples shares kmt2d
- GSE243869 KMT2C deficiency enhances HSC mobilization and granulocyte bias in response to G-CSF 78 samples shares kmt2c
- GSE336091 KMT2C catalytic activity regulates enhancer H3K4me1 and synaptic gene expression in cortical excitatory neurons [CUT&Tag] 48 samples shares kmt2c
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.