trausch/atacseq repository overview

⁠ATAC-Seq Pipeline Installation

git clone https://github.com/tobiasrausch/ATACseq.git

cd ATACseq

make all

If one of the above commands fail your operating system probably lacks some build essentials. These are usually pre-installed but if you lack them you need to install these. For instance, for Ubuntu this would require:

apt-get install build-essential g++ git wget unzip

⁠Building promoter regions for QC and downloading motifs

To annotate motifs and estimate TSS enrichments some simple scripts are included in this repository to download these databases.

cd bed/ && Rscript promoter.R && cd ..

cd motif/ && ./downloadMotifs.sh && cd ..

⁠Running the ATAC-Seq analysis pipeline for a single sample

./src/atac.sh <hg19|mm10> <read1.fq.gz> <read2.fq.gz> <genome.fa> <output prefix>

⁠Plotting the key ATAC-Seq Quality Control metrics

The pipeline produces at various steps JSON QC files (*.json.gz). You can upload and interactively browse these files at https://gear.embl.de/alfred/⁠. In addition, the pipeline produces a succinct QC file for each sample. If you have multiple output folders (one for each ATAC-Seq sample) you can simply concatenate the QC metrics of each sample.

head -n 1 ./*/*.key.metrics | grep "TssEnrichment" | uniq > summary.tsv

cat ./*/*.key.metrics | grep -v "TssEnrichment" >> summary.tsv

To plot the distribution for all QC parameters.

Rscript R/metrics.R summary.tsv

⁠ATAC-Seq pipeline output files

The ATAC-Seq pipeline produces various output files.

• Bowtie⁠ BAM alignment files filtered for duplicates and mitochondrial reads.
• Quality control output files from alfred⁠, samtools⁠, FastQC⁠ and cutadapt adapter filter metrics.
• Macs⁠ peak calling files and IDR⁠ filtered peak lists.
• Succinct browser tracks in bedGraph format and IGV's tdf format.
• Footprint track of nucleosome positions and/or transcription factor bound DNA.
• Homer⁠ motif finding results.

⁠Differential peak calling

Merge peaks across samples and create a raw count matrix.

ls ./Sample1/Sample1.peaks ./Sample2/Sample2.peaks ./SampleN/SampleN.peaks > peaks.lst

ls ./Sample1/Sample1.bam ./Sample2/Sample2.bam ./SampleN/SampleN.bam > bams.lst

./src/count.sh hg19 peaks.lst bams.lst <output prefix>

To call differential peaks on a count matrix for TSS peaks, called counts.tss.gz, using DESeq2 we first need to create a file with sample level information (sample.info). For instance, if you have 2 replicates per condition:

echo -e "name\tcondition" > sample.info

zcat counts.tss.gz | head -n 1 | cut -f 5- | tr '\t' '\n' | sed 's/.final$//' | awk '{print $0"\t"int((NR-1)/2);}' >> sample.info

Rscript R/dpeaks.R counts.tss.gz sample.info

⁠Intersecting peaks with annotation tracks

Peaks can of course be intersected with enhancer or conserved element tracks, i.e.:

cd tracks/ && downloadTracks.sh

bedtools intersect -a ./Sample2/Sample2.peaks -b tracks/conserved.bed

⁠Plotting peak density along all chromosomes

There is a basic Rscript available for plotting peak densities.

Rscript R/karyoplot.R input.peaks

⁠ATAC-Seq Data Analysis Tutorial

As part of the Clinical Genomics and NGS course⁠ we put together a ATAC-Seq tutorial in R Statistics⁠.

⁠Citation

Tobias Rausch, Markus Hsi-Yang Fritz, Jan O Korbel, Vladimir Benes.
Alfred: Interactive multi-sample BAM alignment statistics, feature counting and feature annotation for long- and short-read sequencing.⁠
Bioinformatics. 2018 Dec 6.

B Erarslan, JB Kunz, T Rausch, P Richter-Pechanska et al.
Chromatin accessibility landscape of pediatric T‐lymphoblastic leukemia and human T‐cell precursors⁠
EMBO Mol Med (2020)

⁠License

This ATAC-Seq pipeline is distributed under the GPLv3.