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epigen / atacseq_pipeline

Ultimate ATAC-seq Data Processing & Quantification Workflow. A Snakemake implementation of the BSF's ATAC-seq Data Processing Pipeline extended by downstream quantification and annotation steps using bash and Python.
https://epigen.github.io/atacseq_pipeline/
MIT License
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atac-seq bash bioinformatics biomedical-data-science ngs pipeline python snakemake workflow

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MR.PARETO DOI []() []() GitHub license GitHub Release Snakemake

Ultimate ATAC-seq Data Processing & Quantification Pipeline

A Snakemake 8 workflow implementation of the BSF's ATAC-seq Data Processing Pipeline extended by downstream quantification and annotation steps using Bash and Python.

[!NOTE]
This workflow adheres to the module specifications of MR.PARETO, an effort to augment research by modularizing (biomedical) data science. For more details, instructions, and modules check out the project's repository.

⭐️ Star and share modules you find valuable 📤 - help others discover them, and guide our future work!

[!IMPORTANT]
If you use this workflow in a publication, please don't forget to give credit to the authors by citing it using this DOI 10.5281/zenodo.6323634.

Workflow Rulegraph

🖋️ Authors

💿 Software

This project wouldn't be possible without the following software and their dependencies:

Software Reference (DOI)
bedtools https://doi.org/10.1093/bioinformatics/btq033
Bowtie2 https://doi.org/10.1038/nmeth.1923
deeptools https://doi.org/10.1093/nar/gkw257
ENCODE https://doi.org/10.1038/s41598-019-45839-z
fastp https://doi.org/10.1093/bioinformatics/bty560
HOMER https://doi.org/10.1016/j.molcel.2010.05.004
MACS2 https://doi.org/10.1186/gb-2008-9-9-r137
MultiQC https://doi.org/10.1093/bioinformatics/btw354
pybedtools https://doi.org/10.1093/bioinformatics/btr539
pandas https://doi.org/10.5281/zenodo.3509134
samblaster https://doi.org/10.1093/bioinformatics/btu314
samtools https://doi.org/10.1093/bioinformatics/btp352
Snakemake https://doi.org/10.12688/f1000research.29032.2
UROPA https://doi.org/10.1038/s41598-017-02464-y

🔬 Methods

This is a template for the Methods section of a scientific publication and is intended to serve as a starting point. Only retain paragraphs relevant to your analysis. References [ref] to the respective publications are curated in the software table above. Versions (ver) have to be read out from the respective conda environment specifications (workflow/envs/*.yaml file) or post-execution in the result directory (atacseq_pipeline/envs/*.yaml). Parameters that have to be adapted depending on the data or workflow configurations are denoted in squared brackets e.g., [X].

Processing. Sequencing adapters were removed using the software fastp (ver) [ref]. Bowtie2 (ver) [ref] was used for the alignment of the short reads (representing locations of transposition events) to the [GRCh38 (hg38)/GRCm38 (mm10)] assembly of the [human/mouse] genome using the “--very-sensitive” parameter. PCR duplicates were marked using samblaster (ver) [ref]. Aligned BAM files were then sorted, filtered using ENCODE blacklisted regions [ref], samtools view flags [SAM_flag], and indexed using samtools (ver) [ref]. To detect the open chromatin regions, peak calling was performed using MACS2 (ver) [ref] using the “--nomodel”, “--keep-dup [macs2_keep_dup]” and “--extsize 147” options on each sample. HOMER (ver) [ref] function findMotifs was used for motif enrichment analysis of the detected open chromatin regions. Quality control metrics were aggregated and reported using MultiQC (ver) [ref], [X] sample(s) needed to be removed.

Quantification. A consensus region set, comprising of [X] genomic regions, was generated, by merging the identified peak summits, extended by [slop_extension]bp on both sides using the slop function from bedtools (ver) [ref] and pybedtools (ver) [ref], across all samples while again discarding peaks overlapping blacklisted features as defined by the ENCODE project [ref]. The consensus region set was used to quantify the chromatin accessibility in each sample by summing the number of reads overlapping each consensus region. The consensus region set, and sample-wise quantification of accessibility was performed using bedtools (ver) [ref] and pybedtools (ver) [ref]. Furthermore, the consensus region set was used to quantify the peak support per sample and each region was mapped to its closest TSS according to the HOMER annotation within proximal TSS up and downstream distances [proximal_size_up/down] yielding a gene/TSS-based quantification. Complementary, all promoter regions, defined by the same parameters, were quantified for each sample and aggregated to yield a gene/promoter-based quantification. Finally, all sample-wise enriched known motifs according to HOMER were aggregated.

Annotation. Consensus regions were annotated using annotatePeaks function from HOMER (ver) [ref]. Additionally, we annotated all consensus regions using UROPA (ver) [ref] and genomic features from the [GENCODE vX] basic gene annotation as: “TSS proximal” if the region’s midpoint was within [X] bp upstream or [X] bp downstream from a TSS, or if the region overlapped with a TSS; “gene body” if the region overlapped with a gene; “distal” if the region’s midpoint was within [X] bp of a TSS; and “intergenic” otherwise. For each region, only the closest feature was considered, and the annotations took precedence in the following order: TSS proximal, gene body, distal, and intergenic. Finally, bedtools was employed to quantify nucleotide counts and proportional content per consensus region.

The processing and quantification described here was performed using a publicly available Snakemake [ver] (ref) workflow [10.5281/zenodo.6323634].

🚀 Features

[!IMPORTANT]
Duplciate reads can be filtered during the alignment step by samtools and/or ignored during peak calling by MACS2. The inclusion of duplicates should be intentional, and may lead to a large number of consensus regions. The removal of duplicates should be intentional, might remove real biological signal. The decision depends on your downstream analysis steps e.g., rigorous filtering (e.g., using edgeR::filterByExpr) and/or accounting for PCR bias by normalization conditional on genomic region length and GC content (e.g., CQN) and goals (e.g., differential accessibility analysis). We recommend reading this ChIP-seq tutorial's section on "Removing redundancy".

🛠️ Usage

These steps are the recommended usage for this workflow:

  1. Configure the workflow by pointing to the relevant resources, e.g., downloaded from Zenodo for hg38 or mm10 (see instructions below).
  2. Perform only the processing, by setting the pass_qc annotation for all samples to 0.
  3. Use the generated MultiQC report (result_path/ataceq_pipeline/report/multiqc_report.html) to judge the quality of each sample (see tips in the next section on Quality Control).
  4. Fill out the mandatory quality control column (pass_qc) in the annotation file accordingly (everything >0 will be included in the downstream steps).
  5. Finally, execute the remaining downstream quantification and annotation steps by running the workflow. Thereby only the samples that passed quality control will be included in the consensus region set generation (i.e., the feature space) and all downstream steps.

This workflow is written with Snakemake and its usage is described in the Snakemake Workflow Catalog.

⚙️ Configuration

Detailed specifications can be found here ./config/README.md

📖 Examples

We provide data, annotation and configuration files for two example datasets (hg38 & mm10) in ./test. In both cases the data was generated for test purposes only by downsampling real ATAC-seq samples using samtools.

samtools view -s .0001 real_sample.bam -b > test_sample.bam

The pass_qc attribute is set 0 for all samples, because the downsampled data does not contain any peaks for downstream quantification.

🔍 Quality Control

Below are some guidelines for the manual quality control of each sample, but keep in mind that every experiment/dataset is different.

  1. Reads Mapped ~ $30\cdot 10^{6}$ ($>20\cdot 10^{6}$ at least)
  2. % Aligned >90%
  3. % Mitochondrial <10%
  4. Peaks (depend on reads)
    • FriP (Fraction of reads in Peaks) ~ >20% (can be misleading as 80-90% are also not good)
    • Regulatory regions >10% (as it is roughly 10% of the genome)
    • TSS (Transcription Start Site) normalized coverage ideally > 4 (at least >2)
    • % Duplications “not excessive”
  5. Inspect Genome Browser Tracks using UCSC Genome Browser (online) or IGV (local)
    • Compare all samples to the best, based on above's QC metrics.
    • Check cell type / experiment-specific markers or sex chromosome (X/Y) for accessibility as positive controls.
    • Check e.g., developmental regions for accessibility as negative controls.
  6. Unsupervised Analysis (e.g., PCA or UMAP)
    • Identify outliers/drivers of variation, especially in the control samples and within replicates.

[!IMPORTANT]
Sometimes reads map to Y in females, because X and Y chromosomes both have pseudoautosomal regions (PARs) that are common between the two chromosomes.

My personal QC value scheme to inform downstream analyses (e.g., unsupervised analysis)

Finally, a previous PhD student in our lab, André Rendeiro, wrote about "ATAC-seq sample quality, wet lab troubleshooting and advice".

🔗 Links

📚 Resources

📑 Publications

The following publications successfully used this module for their analyses.

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