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Cut-and-Run in situ factor profiling maps DNA binding and 3D contact sites at high resolution

GSE84474 Saccharomyces cerevisiae (yeast) ChIP-seq / ATAC-seq (genome binding and open chromatin) 126 samples Submitted 2016/12/30 Platform GPL16791Platform GPL17342
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · yeast · 126 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
Saccharomyces cerevisiae (yeast)
Samples
126 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)
Unlike Chromatin Immunoprecipitation (ChIP), which fragments and solubilizes total chromatin, Cut-and-Run is performed in situ, allowing for both high-resolution chromatin mapping and probing of the local chromatin environment. When applied to yeast and human nuclei, Cut-and-Run yielded precise transcription factor profiles while avoiding cross-linking and solubilization issues.
Read the full summary ↓
Cut-and-Run is simple to perform and at low temperatures is inherently robust, with extremely low backgrounds that make it especially cost-effective for transcription factor and chromatin profiling. When used in conjunction with native ChIP-seq and applied to human CTCF, Cut-and-Run mapped high-resolution 3D directional interactions. We conclude that Cut- and-Run is a suitable complement or replacement for ChIP-seq that can also provide 3D mapping information.
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 GSE84474_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 PRJNA329356 and SRA study SRP078609. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 126 more — browse all 126 samples with per-sample file links →

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