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Thiol-linked alkylation for the metabolic sequencing of RNA [Small RNA sequencing in wildtype and Xpo5 knockout mES cells]

GSE99976 Mus musculus (mouse) Non-coding RNA profiling 6 samples Submitted 2017/09/23 Platform GPL17021
At a glance

Non-coding RNA profiling · mouse · 6 samples

What it is
Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism
Mus musculus (mouse)
Samples
6 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)
Gene expression profiling by high-throughput sequencing reveals qualitative and quantitative changes in RNA species at steady-state but obscures the intracellular dynamics of RNA transcription, processing and decay. We developed thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM-seq), an orthogonal chemistry-based epitranscriptomics-sequencing technology that uncovers 4-thiouridine (s4U)-incorporation in RNA species at single-nucleotide resolution.
Read the full summary ↓
In combination with well-established metabolic RNA labeling protocols and coupled to standard, low-input, high-throughput RNA sequencing methods, SLAM-seq enables rapid access to RNA polymerase II-dependent gene expression dynamics in the context of total RNA. When applied to mouse embryonic stem cells, SLAM-seq provides global and transcript-specific insights into pluripotency-associated gene expression. We validated the method by showing that the RNA-polymerase II-dependent transcriptional output scales with Oct4/Sox2/Nanog-defined enhancer activity; and provides quantitative and mechanistic evidence for transcript-specific RNA turnover mediated by post-transcriptional gene regulatory pathways initiated by microRNAs and N6-methyladenosine. SLAM-seq facilitates the dissection of fundamental mechanisms that control gene expression in an accessible, cost-effective, and scalable manner.
This dataset
Open GSE99976 in finder → Find more mouse 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 GSE99976_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 ↗

Also filed as BioProject PRJNA390263 and SRA study SRP109096. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 28945705) ↗
Samples in this study

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+ 6 more — browse all 6 samples with per-sample file links →

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