ST105-01: UNBIASED IN-VIVO AND IN-SILICO TL-532 MECANISM OF ACTION BY RNA-SEQUENCING
RNA-seq (gene expression) · mouse · 12 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
- 12 samples: 3 × TL-532_3H_# vs 3 × TL-532_6H_# vs 3 × TL-532_24H_#Groups 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 GSE224041_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 PRJNA929475 and SRA study SRP420030. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 9 samples.
How we read this: 3 replicated groups read from 12 sample titles; they account for 9 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.
- GSM7010189 Mock_3H_#11
- GSM7010190 Mock_6H_#01
- GSM7010191 Mock_24H_#09
- GSM7010192 TL-532_3H_#10 TL-532_3H_#
- GSM7010193 TL-532_3H_#02 TL-532_3H_#
- GSM7010194 TL-532_3H_#20 TL-532_3H_#
- GSM7010195 TL-532_6H_#03 TL-532_6H_#
- GSM7010196 TL-532_6H_#08 TL-532_6H_#
- GSM7010197 TL-532_6H_#14 TL-532_6H_#
- GSM7010198 TL-532_24H_#06 TL-532_24H_#
- GSM7010199 TL-532_24H_#13 TL-532_24H_#
- GSM7010200 TL-532_24H_#05 TL-532_24H_#
- GSE219229 Unbiased profiling of clinical kinase inhibitors’ effects in activated macrophages using chromatin modifications as high-content readouts [RNA-Seq] 180 samples shares unbiased
- GSE263383 Unbiased niche labeling maps immune-excluded niche in bone metastasis 33 samples shares unbiased
- GSE218968 Unbiased profiling of clinical kinase inhibitors’ effects in activated macrophages using chromatin modifications as high-content readouts [Chromatin associated RNA-seq] 27 samples shares unbiased
- GSE296725 Unbiased niche labeling maps immune-excluded niche in bone metastasis 24 samples shares unbiased
- GSE274132 An Unbiased Approach to Identifying Cellular Reprogramming-Inducible Enhancers [RNA-seq] 14 samples shares unbiased
- GSE241403 The endogenous antigen-specific CD8+ T cell repertoire is composed of unbiased and biased clonotypes with differential fate commitment. 9 samples shares unbiased
- GSE274424 An unbiased genomewide screen uncovers 7 genes that drive hematopoietic stem cell fate from mouse embryonic stem cells [scRNA-seq embryoid bodies] 6 samples shares unbiased
- GSE279976 An Unbiased Approach to Identifying Cellular Reprogramming-Inducible Enhancers [RNA-seq] 4 samples shares unbiased
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.