Author: Syed Nurul Hasan, Ph.D. Contact: nayanchoton1789@gmail.com | ORCID: 0000-0002-4564-872X
The last public gap from my Ph.D.'s genome-wide profiling work (ChIP-seq +
RNA-seq + ATAC-seq in Tregs, POSTECH): bulk ATAC-seq specifically, distinct
from the single-cell ATAC-seq already in bioinformatics-portfolio and
scATAC-WNN-multimodal, and from cutandrun-pipeline's CUT&Tag/CUT&RUN.
Bulk ATAC-seq has its own QC conventions — mitochondrial-read filtering,
Tn5 insertion-site shift, fragment-size periodicity, TSS enrichment — none
of which the other repos exercise, so this one builds the real thing rather
than relabeling an adjacent pipeline.
Paired-end FASTQ (osmotic-stress time course, 100k read pairs x4 samples)
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1. bowtie2_index / bowtie2_align (Docker: biocontainers/bowtie2)
--very-sensitive -X 2000 (wide fragment range for nucleosome-spanning fragments)
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2. filter_mito (samtools) -- drop reads mapping to the mitochondrial genome
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3. dedup (samtools fixmate -> markdup -r) -- PCR duplicate removal
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4. atac_shift (Docker: biocontainers/deeptools, alignmentSieve --ATACshift)
+4/-5bp Tn5 insertion-site correction
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├──▶ fragment_size_qc -- nucleosome-free / mono-nucleosome periodicity
├──▶ tss_enrichment -- ENCODE-style aggregate TSS enrichment score
└──▶ macs3_callpeak -- no Input control (ATAC compares against its
own background, unlike ChIP-seq)
Paired-end FASTQ (downsampled to 100k read pairs/sample, osmotic-stress
time course: T0 vs. T15 minutes, 2 replicates each) + the full S.
cerevisiae reference genome and annotation (7,126 transcripts), from
nf-core/test-datasets
(atacseq branch), derived from GSE66386 — Schep AN, Buenrostro JD,
Denny SK, et al. "Structured nucleosome fingerprints enable high-resolution
mapping of chromatin architecture within regulatory regions." Genome
Research. 2015 — one of the foundational ATAC-seq/NucleoATAC papers.
- Alignment: 73.6–84.2% overall rate (whole-genome reference, real numbers).
- Mitochondrial contamination: 0.2–0.9% across all 4 samples — low, as
expected for yeast (whose mito genome is far smaller relative to the
nuclear genome than in mammalian samples, where 10–50%+ is common).
results/qc/mito_fractions.tsv. - Fragment-size periodicity, the classic ATAC-seq signature: 14–18% of
fragments in the nucleosome-free range (<100bp), 37–42% in the
mono-nucleosome range (147–294bp), median fragment size 176–189bp across
samples.
results/qc/*_fragment_sizes.png. - TSS enrichment (ENCODE-style score): 1.16–1.27 across samples —
computed exactly as ENCODE defines it, and reported honestly even though
it's well below the 5–15+ scores typical in mammalian ATAC-seq. This
isn't a failed experiment: yeast's genome is ~70% coding and gene-dense,
so the "flanking" regions 500–1000bp from a TSS that this metric uses as
background are themselves often inside or near another gene's regulatory
region — the sparse-background assumption the ENCODE metric was
calibrated for doesn't hold in a compact genome. The profile itself still
shows real, above-flank signal centered on the TSS; the ratio just
reads lower here for a structural reason, not a QC failure.
results/qc/*_tss_enrichment.png. - MACS3 peak calling (
-q 0.05, BAMPE, no control): 290–512 peaks per sample — same order of magnitude as nf-core/atacseq's own documented expected output for this exact dataset (627–1135 peaks with their more elaborate merged-replicate broadPeak pipeline; my narrower single-sample narrowPeak approach naturally calls fewer).
bash scripts/download_data.sh
python3 -m venv .venv && source .venv/bin/activate
pip install snakemake matplotlib
snakemake -s workflow/Snakefile --cores 4 -p
# SLURM cluster execution (real profile, not run here -- no cluster in this environment)
snakemake -s workflow/Snakefile --workflow-profile workflow/profiles/slurm- Real bowtie2 alignment + MACS3 peak calling, containerized
- Full ATAC-seq-specific QC: mito filtering, duplicate removal, Tn5 shift (deeptools), fragment-size periodicity, TSS enrichment — not a relabeled ChIP-seq pipeline
- A QC metric (TSS enrichment) reported honestly with the structural reason it reads lower than mammalian benchmarks, rather than omitted
- An actual Snakemake DAG (35 rule invocations) with a SLURM executor profile
- Schep AN, Buenrostro JD, Denny SK, Schwartz K, Sherlock G, Greenleaf WJ. Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions. Genome Research. 2015;25(11):1757-1770.
- Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nature Methods. 2013;10(12):1213-1218.
- Ramírez F, Ryan DP, Grüning B, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Research. 2016;44(W1):W160-W165.
MIT — see LICENSE.