Model integration of circadian and sleep-wake driven contributions to rhythmic gene expression reveals novel regulatory principles
RNA-seq (gene expression) · mouse · 124 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
- 124 samples: 124 samples, each with a distinct title — no replicated groupsGroups 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 GSE262410_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 PRJNA1091667 and SRA study SRP497733. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 124 sample titles: 124 distinct titles with little repetition. 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.
- GSM8164586 RNA_T0_NSD_J70N1_L
- GSM8164587 RNA_T0_NSD_T0N1_L
- GSM8164588 RNA_T0_NSD_T0N8_L
- GSM8164589 RNA_T12_NSD_T12N1_L
- GSM8164590 RNA_T12_NSD_T12N2_L
- GSM8164591 RNA_T12_NSD_T12N4_L
- GSM8164592 RNA_T12_NSD_T12N5_L
- GSM8164593 RNA_T12_NSD_T12N6_L
- GSM8164594 RNA_T18_NSD_T18N2_L
- GSM8164595 RNA_T18_NSD_T18N3_L
- GSM8164596 RNA_T18_NSD_T18N4_L
- GSM8164597 RNA_T18_NSD_T18N8_L
- GSM8164598 RNA_T192_SD_J70S1_L
- GSM8164599 RNA_T192_SD_J70S2_L
- GSM8164600 RNA_T192_SD_J70S3_L
- GSM8164601 RNA_T198_SD_J76S1_L
- GSM8164602 RNA_T198_SD_J76S2_L
- GSM8164603 RNA_T198_SD_J76S3_L
- GSM8164604 RNA_T24_NSD_T0N3_L
- GSM8164605 RNA_T27_SD_T27S2_L
- GSM8164606 RNA_T27_SD_T27S3_L
- GSM8164607 RNA_T27_SD_T27S5_L
- GSM8164608 RNA_T3_NSD_T3N3_L
- GSM8164609 RNA_T3_NSD_T3N4_L
- GSM8164610 RNA_T30_SD_T30S1_L
- GSM8164611 RNA_T30_SD_T30S3_L
- GSM8164612 RNA_T30_SD_T30S4_L
- GSM8164613 RNA_T36_SD_T36S2_L
- GSM8164614 RNA_T36_SD_T36S4_L
- GSM8164615 RNA_T42_SD_T42S2_L
- GSM8164616 RNA_T42_SD_T42S4_L
- GSM8164617 RNA_T48_SD_T48S1_L
- GSM8164618 RNA_T48_SD_T48S3_L
- GSM8164619 RNA_T48_SD_T48S4_L
- GSM8164620 RNA_T54_SD_T54S1_L
- GSM8164621 RNA_T54_SD_T54S2_L
- GSM8164622 RNA_T54_SD_T54S4_L
- GSM8164623 RNA_T6_NSD_J76N1_L
- GSM8164624 RNA_T6_NSD_T6N2_L
- GSM8164625 RNA_T6_NSD_T6N3_L
+ 84 more — browse all 124 samples with per-sample file links →
- GSE256073 De novo formation of cis-regulatory contacts in the absence of NIPBL-driven chromatin loop extrusion 30 samples shares regulatory, driven
- GSE290082 Enhancer-driven gene regulatory networks reveal transcription factors governing T cell adaptation and differentiation in the tumor microenvironment. 25 samples shares regulatory, driven
- GSE267584 Enhancer-driven gene regulatory networks reveal transcription factors governing T cell adaptation and differentiation in the tumor microenvironment. 22 samples shares regulatory, driven
- GSE292261 A dynamic gene regulatory code drives synaptic development of hippocampal granule cells [SS2] 576 samples shares regulatory
- GSE290574 Gene Regulatory Networks Orchestrating Oocyte Fate Bifurcation in Primordial Follicles Revealed by Single-Cell Transcriptomics 386 samples shares regulatory
- GSE228457 IL-7 mediated immunoregulatory feedback loop promotes dampening of immune effector functions by hypoinflammatory monocytes. 305 samples shares regulatory
- GSE217662 Decoding Trp53 regulatory program with knockout cell transcriptomic landscape analyses [scRNA-seq] 247 samples shares regulatory
- GSE274992 Splenic fibroblasts control marginal zone B-cell movement and function via two distinct Notch2-dependent regulatory programs 119 samples shares regulatory
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.