bio-atac-seq-nucleosome-positioning
Map nucleosome center positions, occupancy, and fuzziness from ATAC-seq fragment-size patterns using NucleoATAC, ATACseqQC, DANPOS3, or scprinter. Use when characterizing nucleosome organization at promoters and enhancers, calling +1/-1 nucleosomes flanking NFRs, generating V-plots for chromatin structure visualization, or comparing nucleosome positioning between conditions.
npx skills add BioTender-max/awesome-bio-agent-skills --skill nucleosome-positioning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Maps nucleosome center positions, occupancy, and fuzziness from ATAC-seq fragment-size patterns using NucleoATAC, ATACseqQC, DANPOS3, or scprinter. Use when characterizing nucleosome organization at promoters and enhancers, calling +1/-1 nucleosomes flanking NFRs, generating V-plots for chromatin structure visualization, or comparing nucleosome positioning between conditions.
How it works
The skill instructs to employ fragment-size classes to infer nucleosome features: use a mix of tools including NucleoATAC (cross-correlation with a V-plot template to call per-base signals), ATACseqQC (fragment-size split and V-plot-focused visualization), DANPOS3 (smoothing and peak calling with ATAC-tuned parameters), and scprinter (CNN-based multi-scale nucleosome inference). It provides concrete command examples for calling +1 nucleosomes by defining gene bodies, running NucleoATAC, and selecting the downstream nucleosome from TSSs. It covers V-plot interpretation, fragment-size windows for mono-/di-nucleosomal classes, and how to adjust windows per Buenrostro 2013 conventions (mono 180-247 bp, di 315-473 bp). It includes a complete differential nucleosome positioning workflow with the DANPOS3 dpos/dpeak commands and specific flags tailored for ATAC data (e.g., --width 145, --smooth_width 80, -jd 145, --frsz 200). It also describes NRL estimation from fragment sizes via histogram peaks and a Python example, and provides Python code to construct a V-plot.
When to use it
Use when there is a need to characterize nucleosome organization at regulatory elements, to call +1 nucleosomes relative to TSS, to visualize chromatin structure with V-plots, or to compare nucleosome positioning across conditions. It specifies workflows based on the analysis goal (per-base occupancy with NucleoATAC, V-plot visualization with ATACseqQC, differential positioning with DANPOS3, or single-cell nucleosome positioning with scprinter).
What it can touch
N/A
Caveats
Notes maintenance and compatibility: NucleoATAC last release was 2018; Python 3.6 compatibility may be required; verify versions before use. For ATAC-tuned DANPOS3, use --width 145 and --smooth_width 80 and confirm flags with danpos3 dpos --help. The activity mentions alternative methods (scprinter, DANPOS3) as options when NucleoATAC is not ideal. It cautions about mono-nucleosome fragment windows (180-247 bp) vs narrower filters; adhere to Buenrostro 2013 conventions to avoid undercounting mono-nucleosome signals. The license is NOASSERTION.
## Version Compatibility Reference examples tested with: NucleoATAC 0.3.4+, ATACseqQC 1.26+, DANPOS 3.1+, samtools 1.19+, pysam 0.22+, pyBigWig 0.3+, BSgenome.Hsapiens.UCSC.hg38 1.4+, TxDb.Hsapiens.UCSC.hg38.knownGene 3.18+. NucleoATAC is unmaintained since 2018 but remains the canonical ATAC-specific nucleosome caller; ATACseqQC, DANPOS3, and scprinter are actively developed alternatives. Verify versions before use: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws unexpected errors, introspect the installed package and adapt rather than retrying. # Nucleosome Positioning **"Where are the nucleosomes in my ATAC-seq data?"** -> Use fragment-size classes (Tn5 cuts twice through naked DNA generating short fragments; once on each side of a single nucleosome generating ~147+linker fragments) to call nucleosome centers, occupancy scores, and the spacing pattern around regulatory elements. - CLI: `nucleoatac run --bed regions.bed --bam sample.bam --fasta genome.fa` - R: `ATACseqQC::splitGAlignmentsByCut()` -
- Version Compatibility
- Nucleosome Physics for ATAC
- Fragment-Size Classes (Buenrostro 2013, refined)
- V-Plot Interpretation
- Algorithmic Taxonomy
- +1 Nucleosome Calling
- Per-Tool Failure Modes
- NucleoATAC -- Region size and depth dependence
- NucleoATAC -- Maintenance status
- ATACseqQC factorFootprints -- Asymmetric nucleosome flanks
- DANPOS dpos with default parameters -- ATAC mismatch
- Mono-nucleosome filter window mis-set
- Decision Tree by Goal
- Estimating NRL from Fragment-Size Distribution
bedtools slop -i genes.bed -g chrom.sizes -l 200 -r 1000 > gene_bodies.bed nucleoatac run --bed gene_bodies.bed --bam sample.dedup.bam --fasta genome.fa \ Compare control vs treatment nucleosome positions danpos3 dpeak \ danpos3 dpos sample.bam \
What does the bio-atac-seq-nucleosome-positioning skill do?
Map nucleosome center positions, occupancy, and fuzziness from ATAC-seq fragment-size patterns using NucleoATAC, ATACseqQC, DANPOS3, or scprinter. Use when characterizing nucleosome organization at promoters and enhancers, calling +1/-1 nucleosomes flanking NFRs, generating V-plots for chromatin structure visualization, or comparing nucleosome positioning between conditions.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill nucleosome-positioning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.