Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the transcript life cycle
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: 80 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 GSE207926_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 PRJNA857694 and SRA study SRP385916. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 160 samples.
How we read this: 80 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.
- GSM6323036 3T3_nuclear_WT_0min_rep1 3T3_nuclear_WT_0min
- GSM6323037 3T3_nuclear_WT_15min_rep1 3T3_nuclear_WT_15min
- GSM6323038 3T3_nuclear_WT_30min_rep1 3T3_nuclear_WT_30min
- GSM6323039 3T3_nuclear_WT_60min_rep1 3T3_nuclear_WT_60min
- GSM6323040 3T3_nuclear_WT_120min_rep1 3T3_nuclear_WT_120min
- GSM6323041 3T3_cytoplasm_WT_0min_rep1 3T3_cytoplasm_WT_0min
- GSM6323042 3T3_cytoplasm_WT_15min_rep1 3T3_cytoplasm_WT_15min
- GSM6323043 3T3_cytoplasm_WT_30min_rep1 3T3_cytoplasm_WT_30min
- GSM6323044 3T3_cytoplasm_WT_60min_rep1 3T3_cytoplasm_WT_60min
- GSM6323045 3T3_cytoplasm_WT_120min_rep1 3T3_cytoplasm_WT_120min
- GSM6323046 3T3_polysome_WT_0min_rep1 3T3_polysome_WT_0min
- GSM6323047 3T3_polysome_WT_15min_rep1 3T3_polysome_WT_15min
- GSM6323048 3T3_polysome_WT_30min_rep1 3T3_polysome_WT_30min
- GSM6323049 3T3_polysome_WT_60min_rep1 3T3_polysome_WT_60min
- GSM6323050 3T3_polysome_WT_120min_rep1 3T3_polysome_WT_120min
- GSM6323051 3T3_total_WT_0min_rep1 3T3_total_WT_0min
- GSM6323052 3T3_total_WT_15min_rep1 3T3_total_WT_15min
- GSM6323053 3T3_total_WT_30min_rep1 3T3_total_WT_30min
- GSM6323054 3T3_total_WT_60min_rep1 3T3_total_WT_60min
- GSM6323055 3T3_total_WT_120min_rep1 3T3_total_WT_120min
- GSM6323056 3T3_nuclear_WT_0min_rep2 3T3_nuclear_WT_0min
- GSM6323057 3T3_nuclear_WT_15min_rep2 3T3_nuclear_WT_15min
- GSM6323058 3T3_nuclear_WT_30min_rep2 3T3_nuclear_WT_30min
- GSM6323059 3T3_nuclear_WT_60min_rep2 3T3_nuclear_WT_60min
- GSM6323060 3T3_nuclear_WT_120min_rep2 3T3_nuclear_WT_120min
- GSM6323061 3T3_cytoplasm_WT_0min_rep2 3T3_cytoplasm_WT_0min
- GSM6323062 3T3_cytoplasm_WT_15min_rep2 3T3_cytoplasm_WT_15min
- GSM6323063 3T3_cytoplasm_WT_30min_rep2 3T3_cytoplasm_WT_30min
- GSM6323064 3T3_cytoplasm_WT_60min_rep2 3T3_cytoplasm_WT_60min
- GSM6323065 3T3_cytoplasm_WT_120min_rep2 3T3_cytoplasm_WT_120min
- GSM6323066 3T3_polysome_WT_0min_rep2 3T3_polysome_WT_0min
- GSM6323067 3T3_polysome_WT_15min_rep2 3T3_polysome_WT_15min
- GSM6323068 3T3_polysome_WT_30min_rep2 3T3_polysome_WT_30min
- GSM6323069 3T3_polysome_WT_60min_rep2 3T3_polysome_WT_60min
- GSM6323070 3T3_polysome_WT_120min_rep2 3T3_polysome_WT_120min
- GSM6323071 3T3_total_WT_0min_rep2 3T3_total_WT_0min
- GSM6323072 3T3_total_WT_15min_rep2 3T3_total_WT_15min
- GSM6323073 3T3_total_WT_30min_rep2 3T3_total_WT_30min
- GSM6323074 3T3_total_WT_60min_rep2 3T3_total_WT_60min
- GSM6323075 3T3_total_WT_120min_rep2 3T3_total_WT_120min
+ 120 more — browse all 160 samples with per-sample file links →
- GSE89303 Global transcriptional analysis and genome-wide analysis of chromatin state in extended pluripotent stem cells, primed pluripotent stem cells, and naïve pluripotent stem cells 20 samples shares genome-wide, transcript
- GSE305653 Genome-wide chromatin recording resolves dynamic cell state changes 110 samples shares genome-wide
- GSE35495 Genome-wide analysis of human and mouse macrophages, resting and activated 73 samples shares genome-wide
- GSE317069 Multiscale analysis and functional validation of the cellular and genetic determinants of skeletal disease 47 samples shares determinants
- GSE254764 Genome-wide mapping of native G-loops in living cells 28 samples shares genome-wide
- GSE174543 Genome-wide RNAseq of liver tissue derived from humanized mice fed by CDA-HFD and treated with CLDN1 mAb or vehicle Control 10 samples shares genome-wide
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.