Rational cell fate engineering through chromatin dynamics [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: 16 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.
- Paper
- Engineering mouse cell fate controller by rational designNature communications · 2024
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 GSE243516_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 PRJNA1018838 and SRA study SRP461622. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 32 samples.
How we read this: 16 replicated groups read from 46 sample titles; they account for 32 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.
- GSM7789776 NanogN70+Oct4, Day0, Rep1 NanogN70+Oct4, Day0,
- GSM7789777 NanogN70+Oct4, Day1, Rep1 NanogN70+Oct4, Day1,
- GSM7789778 NanogN70+Oct4, Day3, Rep1 NanogN70+Oct4, Day3,
- GSM7789779 NanogN70+Oct4, Day5, Rep1 NanogN70+Oct4, Day5,
- GSM7789780 NanogN70+Oct4, Day7, Rep1 NanogN70+Oct4, Day7,
- GSM7789781 NanogN70+Oct4, Day8, Rep1
- GSM7789782 NanogN70+Oct4, Day10, Rep1 NanogN70+Oct4, Day10,
- GSM7789783 NanogN70+Oct4, Day12, Rep1 NanogN70+Oct4, Day12,
- GSM7789784 Nanog+Oct4, Day0, Rep1 Nanog+Oct4, Day0,
- GSM7789785 Nanog+Oct4, Day1, Rep1 Nanog+Oct4, Day1,
- GSM7789786 Nanog+Oct4, Day3, Rep1 Nanog+Oct4, Day3,
- GSM7789787 Nanog+Oct4, Day5, Rep1 Nanog+Oct4, Day5,
- GSM7789788 Nanog+Oct4, Day7, Rep1 Nanog+Oct4, Day7,
- GSM7789789 Nanog+Oct4, Day8, Rep1 Nanog+Oct4, Day8,
- GSM7789790 Nanog+Oct4, Day10, Rep1 Nanog+Oct4, Day10,
- GSM7789791 Nanog+Oct4, Day12, Rep1 Nanog+Oct4, Day12,
- GSM7789792 NanogN70+Oct4, Day0, Rep2 NanogN70+Oct4, Day0,
- GSM7789793 NanogN70+Oct4, Day1, Rep2 NanogN70+Oct4, Day1,
- GSM7789794 NanogN70+Oct4, Day3, Rep2 NanogN70+Oct4, Day3,
- GSM7789795 NanogN70+Oct4, Day5, Rep2 NanogN70+Oct4, Day5,
- GSM7789796 NanogN70+Oct4, Day7, Rep2 NanogN70+Oct4, Day7,
- GSM7789797 NanogN70+Oct4, Day10, Rep2 NanogN70+Oct4, Day10,
- GSM7789798 NanogN70+Oct4, Day12, Rep2 NanogN70+Oct4, Day12,
- GSM7789799 Nanog+Oct4, Day0, Rep2 Nanog+Oct4, Day0,
- GSM7789800 Nanog+Oct4, Day1, Rep2 Nanog+Oct4, Day1,
- GSM7789801 Nanog+Oct4, Day3, Rep2 Nanog+Oct4, Day3,
- GSM7789802 Nanog+Oct4, Day5, Rep2 Nanog+Oct4, Day5,
- GSM7789803 Nanog+Oct4, Day7, Rep2 Nanog+Oct4, Day7,
- GSM7789804 Nanog+Oct4, Day8, Rep2 Nanog+Oct4, Day8,
- GSM7789805 Nanog+Oct4, Day10, Rep2 Nanog+Oct4, Day10,
- GSM7789806 Nanog+Oct4, Day12, Rep2 Nanog+Oct4, Day12,
- GSM7789807 NanogN70+Oct4 iPSC2 3#
- GSM7789808 NanogN70+Oct4 iPSC1 4#
- GSM7789809 NanogN70+Oct4 iPSC1 8#
- GSM7789810 NanogN70+Oct4 iPSC1 9#
- GSM7789811 NanogN70+Oct4 iPSC2 1#
- GSM7789812 NanogN70+Oct4 iPSC2 2#
- GSM7789813 Nanog+Oct4 iPSC2 6#
- GSM7789814 Nanog+Oct4 iPSC2 5#
- GSM7789815 Nanog+Oct4 iPSC1 1# Nanog+Oct4 iPSC1 1#
+ 6 more — browse all 46 samples with per-sample file links →
- GSE299114 Endothelial Cells retain inflammatory memory through chromatin remodeling [RNA-Seq 2] 53 samples shares chromatin, rna-seq
- GSE166225 Inference of emergent spatio-temporal processes from single-cell sequencing reveals feedback between de novo DNA methylation and chromatin condensates [RNA-seq] 288 samples shares chromatin, rna-seq
- GSE303460 Spatiotemporal single-cell atlas of suture stem cell dynamics in craniosynostosis [scRNA-Seq] 14 samples shares dynamics, rna-seq
- GSE280373 Spatiotemporal dynamics of the cardioimmune niche during lesion repair [scRNA-seq] 28 samples shares dynamics, rna-seq
- GSE277228 Changing dynamics of the fibroblast differentiation hierarchy during mammary gland morphogenesis and tumorigenesis [scRNA-Seq I] 13 samples shares dynamics, rna-seq
- GSE306116 Caspase-3 Control of RNA Splicing and Mitochondrial Dynamics in Microglia during Parkinson’s Disease [RNA-Seq] 12 samples shares dynamics, rna-seq
- GSE310322 Mapping self-associating chromatin hubs identifies Id proteins as key determinants of exhausted T cell fate 37 samples shares chromatin, fate
- GSE264193 H3K27 dimethylation dynamics reveal stepwise establishment of facultative heterochromatin in early mouse embryos [ChIP-seq] 57 samples shares chromatin, dynamics
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.