N6-methyladenosine modulate second cell fate decision by enhancing mRNA stability of pivotal transcription factors during primitive endoderm development
RNA-seq (gene expression) · mouse · 160 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
- 160 samples: 76 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 GSE282841_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 PRJNA1190595 and SRA study SRP547580. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 160 samples.
How we read this: 76 replicated groups read from 160 sample titles; they account for 160 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.
- GSM8645769 TBLC_GLORI_siNC_rep1 TBLC_GLORI_siNC
- GSM8645770 TBLC_GLORI_siNC_rep2 TBLC_GLORI_siNC
- GSM8645771 TBLC_GLORI_siVirma_rep1 TBLC_GLORI_siVirma
- GSM8645772 TBLC_GLORI_siVirma_rep2 TBLC_GLORI_siVirma
- 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
- GSM8661186 TBLC_0h_rep1 TBLC_0h
- GSM8661187 TBLC_0h_rep2 TBLC_0h
- GSM8661188 TBLC_1h_rep1 TBLC_1h
- GSM8661189 TBLC_1h_rep2 TBLC_1h
- GSM8661190 TBLC_2h_rep1 TBLC_2h
- GSM8661191 TBLC_2h_rep2 TBLC_2h
- GSM8661192 TBLC_3h_rep1 TBLC_3h
- GSM8661193 TBLC_3h_rep2 TBLC_3h
- GSM8661194 diffTBLC_0h_rep1 diffTBLC_0h
- GSM8661195 diffTBLC_0h_rep2 diffTBLC_0h
- GSM8661196 diffTBLC_1h_rep1 diffTBLC_1h
- GSM8661197 diffTBLC_1h_rep2 diffTBLC_1h
- GSM8661198 diffTBLC_2h_rep1 diffTBLC_2h
- GSM8661199 diffTBLC_2h_rep2 diffTBLC_2h
- GSM8661200 diffTBLC_3h_rep1 diffTBLC_3h
- GSM8661201 diffTBLC_3h_rep2 diffTBLC_3h
- GSM8661202 diffTBLC_siNC1_0h_rep1 diffTBLC_siNC1_0h
- GSM8661203 diffTBLC_siNC1_0h_rep2 diffTBLC_siNC1_0h
- GSM8661204 diffTBLC_siNC1_1h_rep1 diffTBLC_siNC1_1h
- GSM8661205 diffTBLC_siNC1_1h_rep2 diffTBLC_siNC1_1h
- GSM8661206 diffTBLC_siNC1_2h_rep1 diffTBLC_siNC1_2h
- GSM8661207 diffTBLC_siNC1_2h_rep2 diffTBLC_siNC1_2h
- GSM8661208 diffTBLC_siNC1_3h_rep1 diffTBLC_siNC1_3h
- GSM8661209 diffTBLC_siNC1_3h_rep2 diffTBLC_siNC1_3h
+ 120 more — browse all 160 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, endoderm
- 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
- GSE294521 Transcriptional remodeling by OTX2 directs specification and patterning of mammalian definitive endoderm [CUTnTAG] 34 samples shares transcription, endoderm
- 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
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.