ATACseq Analysis Recipe

The ATAC-seq Analysis Recipe takes you from unaligned (raw) BAM files derived from a bulk ATAC-seq to enrichment analysis results of your differentially accessible genomic regions while providing unsupervised analyses and genome browser tracks for quality control.

flowchart LR;
    ngs_fetch-->atacseq_pipeline;
    atacseq_pipeline-->genome_tracks;
    atacseq_pipeline-->spilterlize_integrate;
    spilterlize_integrate-->unsupervised_analysis;
    spilterlize_integrate-->dea_limma;
    dea_limma-->enrichment_analysis;

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Modules

The following Modules are used in this Recipe:

1. (optional) Fetch publicly available bulk ATAC-seq data (coming soon).
2. ATAC-seq pipeline to quantify chromatin accessibility, resulting in count matrices and annotations.
3. Genome Browser Track Visualization for quality control and visual analysis of genomic regions of interest or top hits.
4. Split, Filter, Normalize and Integrate Sequencing Data for downstream analysis.
5. Unsupervised Analysis for quality control and to understand and visualize similarities and variations between samples.
6. Differential Analysis with limma to identify and visualize statistically significant genomic regions that differ between sample groups using linear models.
7. Enrichment Analysis for biomedical interpretation of differential analysis results using prior knowledge.

Strategy

(just notes currently)

• Generate a consensus region set for all (QC'd) samples to cover all potential signals within your feature space e.g., across cell types.
• Subset data for each analysis (e.g., cell type A analysis -> subset to cell type A samples).
• Remove lowly accessible regions in each subset to improve statistical power and reduce statistical estimation issues.
• Perform the desired analysis (e.g., differential accessibility) on the processed subset datasets.
• This approach facilitates integrating common signals across cell types while not losing cell type-specific signals in the respective analyses.

Templates for a Methods section of a scientific publication can be found in each Module's README.

Data

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Code & Configuration

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Results

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