RNA-seq of fetal testes of Sox9-eGFP mice treated with DEHP
RNA-seq (gene expression) · mouse · 16 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
- 16 samples: 5 × GFP-pos_500-DEHP-P vs 4 × GFP-neg_vehicle-N vs 4 × GFP-pos_vehicle-P vs 3 × GFP-pos_20-DEHP-PGroups 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 GSE281155_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 PRJNA1182393 and SRA study SRP543351. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 16 samples.
How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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.
- GSM8611882 GFP-neg_vehicle-N1 GFP-neg_vehicle-N
- GSM8611883 GFP-neg_vehicle-N2 GFP-neg_vehicle-N
- GSM8611884 GFP-neg_vehicle-N3 GFP-neg_vehicle-N
- GSM8611885 GFP-neg_vehicle-N4 GFP-neg_vehicle-N
- GSM8611886 GFP-pos_vehicle-P1 GFP-pos_vehicle-P
- GSM8611887 GFP-pos_vehicle-P2 GFP-pos_vehicle-P
- GSM8611888 GFP-pos_vehicle-P3 GFP-pos_vehicle-P
- GSM8611889 GFP-pos_vehicle-P4 GFP-pos_vehicle-P
- GSM8611890 GFP-pos_20-DEHP-P5 GFP-pos_20-DEHP-P
- GSM8611891 GFP-pos_20-DEHP-P7 GFP-pos_20-DEHP-P
- GSM8611892 GFP-pos_20-DEHP-P8 GFP-pos_20-DEHP-P
- GSM8611893 GFP-pos_500-DEHP-P9 GFP-pos_500-DEHP-P
- GSM8611894 GFP-pos_500-DEHP-P10 GFP-pos_500-DEHP-P
- GSM8611895 GFP-pos_500-DEHP-P11 GFP-pos_500-DEHP-P
- GSM8611896 GFP-pos_500-DEHP-P12 GFP-pos_500-DEHP-P
- GSM8611897 GFP-pos_500-DEHP-P14 GFP-pos_500-DEHP-P
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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.