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Ribosome Profiling of mouse skin squamous tumours

GSE72066 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2016/06/16 Platform GPL9250
At a glance

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
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.
Summary (written by the submitters)
Purpose: Ribosome profiling has revolutionized systems-based analysis and which produces a “global snapshot” of all the ribosomes translationally active in a cell at a particular moment. The goals of this study are to first apply ribosome profiling to in vivo samples for the first time and in particular to stem cells and tumours and second to determine which mRNAs are being actively translated in these particular situations.
Read the full summary ↓
Although much is known about gene expression regulation, little is known about how protein translation regulation can affect stem cell differentiation and tumour progression. Methods: several replicates of ribosome and mRNA profiles of wild-type (WT) and NSun2 -/- mouse skin squamous tumours were generated by deep sequencing, using Illumina HiSeq platform. Results: Our analyses reveal that activation of stress response pathways in vivo drives both a global reduction of protein synthesis and altered translation of specific mRNAs that together promote stem cell functions and tumourigenesis.
This dataset
Open GSE72066 in finder → Find more mouse RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE72066_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA292862 and SRA study SRP062409. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 27306184) ↗
Samples in this study

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+ 12 more — browse all 12 samples with per-sample file links →

Datasets on a similar topic

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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.