ATAC-seq Data Standards and Processing Pipeline
The Assay for Transposase-Accessible Chromatin followed by sequencing (ATAC-seq) experiment provides genome-wide profiles of chromatin accessibility. Briefly, the ATAC-seq method works as follows: loaded transposase inserts sequencing primers into open chromatin sites across the genome, and reads are then sequenced. The ends of the reads mark open chromatin sites.
Updated July 2020
The ATAC-seq pipeline was developed by Anshul Kundaje's lab at Stanford University. Upon revision and full implementation, it will be a part of the ENCODE Uniform Processing Pipelines series. The full ATAC-seq pipeline code is available on Github.
The ENCODE ATAC-seq pipeline is used for quality control and statistical signal processing of short-read sequencing data, producing alignments and measures of enrichment.
Pipeline schematic for replicated data
View the current instances of the pipeline for replicated data
Pipeline schematic for unreplicated data
View the current instances of the pipeline for unreplicated data
Information contained in file
|G-zipped, paired-ended or single-ended ATAC-seq reads
|Reads must meet the criteria outlined under the Uniform Processing Pipeline Restrictions.
Information contained in file
alignments and filtered alignments
Produced by mapping reads to the genome
|Bowtie2 aligner is used to produce raw bam files, followed by various filtering steps (mappability and quality) to produce filtered bams.
|Two versions of nucleotide resolution signal coverage tracks
These signals are the fold enrichment of signal over expected background and a p-value track representing statistical significance
peaks (regions of enrichment)
Punctuate peaks (narrowPeaks) and larger regions of enrichment (gappedPeaks)
Regions of enrichments are called from filtered bams, using the MACS2 peak caller with custom parameters optimal for ATAC-seq data
Merged Peak Sets
|Using the replicates provided where available, three types of “merged” peak sets are produced. In the absence of replicates, the same procedure listed below is used on pseudoreplicates obtained by subsampling reads from a single replicate
|bed and bigBed
|replicated peaks (narrowPeak)
Punctate peaks both on individual replicates/pseudoreplicates and on data pooled across replicates
|From the set of peaks on pooled data, we only retain those that have at least 50% overlap with a peak in both replicates.
|bed and bigBed
|replicated region (gappedPeak)
|Broader regions of enrichment both on individual replicates/pseudoreplicates and on data pooled across replicates
From this set of peaks on pooled data, we only retain those that have at least 50% overlap with a peak in both replicates.
|bed and bigBed
|A higher confidence, reproducible set of peaks
|A statistical procedure called the Irreproducible Discovery Rate (IDR) operates on the replicated peak set and compares consistency of ranks of these peaks in individual replicate/pseudoreplicate peak sets.
Quality control metrics are collected to determine library complexity, signal to noise ratios, fragment length distribution per replicate (where available), and reproducibility.
We recommend using the replicated peak and replicated region sets when one prefers a low false negative rate but potentially higher false positives. We recommend using the IDR peaks when one prefers low false positive rates.
Some may notice that the peaks produced look both like peaks produced from the TF ChIP-seq pipeline as well as the histone ChIP-seq pipeline. This is intentional, as ATAC-seq data looks both like TF data (narrow peaks of signal) as well as histone data (broader regions of openness).
Links and Publications
Uniform Processing Pipeline Restrictions
- The read length prior to any trimming should be a minimum of 45 base pairs.
- Sequencing may be paired- or single-ended, as long as sequencing type is specified and paired sequences are indicated.
- All Illumina platforms are supported for use in the uniform pipeline, though data from different platforms should be processed separately; colorspace (SOLiD) reads are not supported.
- Barcodes, if present in the fastq, must be indicated.
- Library insert size range must be indicated.
- Alignment files are mapped to either the GRCh38 or mm10 sequences.
- Experiments should have two or more biological replicates. Assays performed using EN-TEx samples may be exempted due to limited availability of experimental material, but at least two technical replicates are required.
- Each replicate should have 25 million non-duplicate, non-mitochondrial aligned reads for single-end sequencing and 50 million for paired-ended sequencing (i.e. 25 million fragments, regardless of sequencing run type).
- The alignment rate, or percentage of mapped reads, should be greater than 95%, though values >80% may be acceptable.
- Replicate concordance is measured by calculating IDR values (Irreproducible Discovery Rate). The experiment passes if both rescue and self consistency ratios are less than 2.
- Library complexity is measured using the Non-Redundant Fraction (NRF) and PCR Bottlenecking Coefficients 1 and 2, or PBC1 and PBC2. The preferred values are as follows: NRF>0.9, PBC1>0.9, and PBC2>3.
- Various peak files must meet certain requirements. Please visit the section on output files under the pipeline overview for more information on peak files.
- The number of peaks within a replicated peak file should be >150,000, though values >100,000 may be acceptable.
- The number of peaks within an IDR peak file should be >70,000, though values >50,000 may be acceptable.
- A nucleosome free region (NFR) must be present.
- A mononucleosome peak must be present in the fragment length distribution. These are reads that span a single nucleosome, so they are longer than 147 bp but shorter than 147*2 bp. Good ATAC-seq datasets have reads that span nucleosomes (which allows for calling nucleosome positions in addition to open regions of chromatin).
- The fraction of reads in called peak regions (FRiP score) should be >0.3, though values greater than 0.2 are acceptable. For EN-TEx tissues, FRiP scores will not be enforced as QC metric. TSS enrichment remains in place as a key signal to noise measure.
- Transcription start site (TSS) enrichment values are dependent on the reference files used; cutoff values for high quality data are listed in the table below.
|Resulting Data Status
|hg19 Refseq TSS annotation
|6 - 10
|GRCh38 Refseq TSS annotation
|5 - 7
|mm9 GENCODE TSS annotation
|5 - 7
|mm10 Refseq TSS annotation
- The experiment must pass routine metadata audits in order to be released.