N6-methyladenosine modulate second cell fate decision by enhancing mRNA stability of pivotal transcription factors during primitive endoderm development [EPSC_RNA-seq]
RNA-seq (gene expression) · mouse · 46 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
- 46 samples: 19 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.
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 GSE282840_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 PRJNA1190591 and SRA study SRP547580. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 46 samples.
How we read this: 19 replicated groups read from 46 sample titles; they account for 46 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.
- GSM8651293 PrEdiff_Virma_WT_rep1 PrEdiff_Virma_WT
- GSM8651294 PrEdiff_Virma_WT_rep2 PrEdiff_Virma_WT
- GSM8651295 PrEdiff_Virma_cKO_rep1 PrEdiff_Virma_cKO
- GSM8651296 PrEdiff_Virma_cKO_rep2 PrEdiff_Virma_cKO
- GSM8651297 PrEdiff_Mettl3_WT_rep1 PrEdiff_Mettl3_WT
- GSM8651298 PrEdiff_Mettl3_WT_rep2 PrEdiff_Mettl3_WT
- GSM8651299 PrEdiff_Mettl3_cKO_rep1 PrEdiff_Mettl3_cKO
- GSM8651300 PrEdiff_Mettl3_cKO_rep2 PrEdiff_Mettl3_cKO
- GSM8651301 PrEdiff_siNC_rep1 PrEdiff_siNC
- GSM8651302 PrEdiff_siNC_rep2 PrEdiff_siNC
- GSM8651303 PrEdiff_siIgf2bp3_rep1 PrEdiff_siIgf2bp3
- GSM8651304 PrEdiff_siIgf2bp3_rep2 PrEdiff_siIgf2bp3
- GSM8676991 PrE-E_rep1 PrE-E
- GSM8676992 PrE-E_rep2 PrE-E
- GSM8676993 EPI-E_rep1 EPI-E
- GSM8676994 EPI-E_rep2 EPI-E
- GSM8676995 embryoid_VE_rep1 embryoid_VE
- GSM8676996 embryoid_VE_rep2 embryoid_VE
- GSM8676997 embryoid_VE_rep3 embryoid_VE
- GSM8676998 embryoid_VE_rep4 embryoid_VE
- GSM8676999 embryoid_VE_rep5 embryoid_VE
- GSM8677000 embryoid_VE_rep6 embryoid_VE
- GSM8677001 embryoid_EPI_rep1 embryoid_EPI
- GSM8677002 embryoid_EPI_rep2 embryoid_EPI
- GSM8677003 embryoid_EPI_rep3 embryoid_EPI
- GSM8677004 embryoid_EPI_rep4 embryoid_EPI
- GSM8677005 embryoid_EPI_rep5 embryoid_EPI
- GSM8677006 embryoid_EPI_rep6 embryoid_EPI
- GSM8677007 EPS_0h_rep1 EPS_0h
- GSM8677008 EPS_0h_rep2 EPS_0h
- GSM8677009 EPS_1h_rep1 EPS_1h
- GSM8677010 EPS_1h_rep2 EPS_1h
- GSM8677011 EPS_2h_rep1 EPS_2h
- GSM8677012 EPS_2h_rep2 EPS_2h
- GSM8677013 EPS_3h_rep1 EPS_3h
- GSM8677014 EPS_3h_rep2 EPS_3h
- GSM8677015 PrEdiff_0h_rep1 PrEdiff_0h
- GSM8677016 PrEdiff_0h_rep2 PrEdiff_0h
- GSM8677017 PrEdiff_1h_rep1 PrEdiff_1h
- GSM8677018 PrEdiff_1h_rep2 PrEdiff_1h
+ 6 more — browse all 46 samples with per-sample file links →
- GSE283382 N6-methyladenosine modulate second cell fate decision by enhancing mRNA stability of pivotal transcription factors during primitive endoderm development [TBLC_RNAseq] 98 samples shares n6-methyladenosine, transcription, enhancing, stability, primitive, modulate, decision
- GSE286046 Asymmetric Histone Inheritance Regulates Differential Transcription Re-initiation and Cell Fate Decisions in Mouse Olfactory Horizontal Basal Cells 96 samples shares transcription, decision
- GSE322779 Transcription factor TFEB antagonizes cardiac hypertrophy and failure by enhancing lysosomal capacity and mitochondrial function 16 samples shares transcription, enhancing
- GSE294772 Single-Cell Epigenomics Uncovers Heterochromatin Instability and Transcription Factor Dysfunction during Mouse Brain Aging [snATAC-Seq] [10X Multiome] 142 samples shares transcription, stability
- GSE284504 Transcriptional profiling of brain from a zQ175 KI mouse model of Huntington's disease 335 samples shares transcription
- GSE229815 Transcriptional profiling of 12 month striatum and cortex from a mouse model of Huntington's disease (Q140) crossed with Msh2 heterozygous or homozygous conditional knockout mice [Msh2_12mon] 138 samples shares transcription
- GSE225576 An atlas of the aging mouse transcriptome and proteome reveals the features of age-related post-transcriptional dysregulation 128 samples shares transcription
- GSE282096 Diminished transcriptional activity and splicing changes drive gene length-biased rewiring in the aging transcriptome (RNA-Seq) 94 samples shares transcription
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.