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TT-Seq captures the human transient transcriptome

GSE75792 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2016/06/03 Platform GPL18460
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 12 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
12 samples: 6 conditions, mostly in duplicateGroups 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.
Summary (written by the submitters)
A large portion of the genome is transcribed but many of the resulting RNAs live only transiently and can generally not be mapped. Here we develop transient transcriptome sequencing (TT-Seq), a protocol that maps transcriptionally active regions in a nearly uniform manner and allows for unbiased monitoring of cellular RNA synthesis activity.
Read the full summary ↓
Application of TT-Seq to human K562 cells recovers stable mRNAs and long intergenic non-coding RNAs, and additionally maps over 10,000 transient RNAs including enhancer RNAs, antisense RNAs, promoter-associated upstream antisense and convergent RNAs. TT-Seq also provides RNA half-lives, and reveals that transient RNAs are short and lack U1 motifs and secondary structure. TT-Seq further uncovers transcription termination sites and reveals a universal DNA motif for RNA polymerase II release.
This dataset
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.

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 GSE75792_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA305363 and SRA study SRP067119. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in duplicate
TT-Seq ×2 4sU-Seq ×2 Total RNA-Frag-Seq ×2 Total RNA-Seq ×2 S2P ChIP ×2 N20 ChIP ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 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.

Samples in this study
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