fragment-length-estimation-cross-correlation

fragment-length-estimation-cross-correlation is a skill for Claude Code, Codex from HolobiomicsLab/asb-skill-collections. It costs 35 tokens per session (1,685 once invoked), scanned A, original, Apache-2.0.

A ChIP-seq analysis step that estimates the typical DNA fragment length from single-end reads by comparing read patterns on opposite DNA strands. ChIP-seq identifies DNA regions associated with a protein.

In plain words
What is it for?
Use it after duplicate filtering when starting ChIP-seq analysis with single-end BED or SAM data and no known fragment length.
Why use it?
Peak calling and coverage calculations need the fragment length, but it may not be known from the sequencing data. Estimating it provides that value before later analysis.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it after duplicate filtering when starting ChIP-seq analysis with single-end BED or SAM data and no known fragment length.

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Install with agentmods
npx agentmods add skills/holobiomicslab/asb-skill-collections/fragment-length-estimation-cross-correlation
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add HolobiomicsLab/asb-skill-collections --skill fragment-length-estimation-cross-correlation
Clone the repo
git clone --depth 1 https://github.com/HolobiomicsLab/asb-skill-collections

Made for: Claude Code, Codex.

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README.md
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Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,685 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.01685
Opus 5 $0.00017 $0.00843
Sonnet 5 $0.00007 $0.00337
Haiku 4.5 $0.00003 $0.00169

Measured 9d ago against content hash 0d9ed1a138fc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

fragment-length-estimation-cross-correlation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

collections/epigenomics/v1/skills/fragment-length-estimation-cross-correlation/SKILL.md · 99 lines

How it starts

The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.

fragment-length-estimation-cross-correlation

Summary

Estimate the DNA fragment length d in ChIP-Seq experiments by cross-correlation analysis of read pileups, a critical parameter that determines downstream peak calling accuracy and local background normalization. This skill uses MACS3's predictd subcommand to infer the typical distance between forward and reverse strand reads.

When to use

When beginning ChIP-Seq analysis with single-end BED/SAM input and no prior knowledge of the library's fragment length. The fragment length is essential before generating coverage tracks and building local bias backgrounds, as it directly controls peak calling resolution (minimum peak length) and background normalization scales. Apply this skill immediately after duplicate filtering but before pileup track generation.

When NOT to use

  • Input is paired-end BED (BEDPE format) — use fragment length from BEDPE read pairs directly instead.
  • Fragment length is already known from prior QC (e.g., Bioanalyzer, library prep documentation) — skip prediction and use known value to save compute time.
  • Control-only or Input-only samples — predictd requires ChIP sample; cannot infer fragment length from background alone.

Inputs

  • Deduplicated ChIP-Seq BED file (single-end, post-filterdup)
  • Genome size (integer, e.g., 'hs' for human haploid ~2.7 billion bp)
  • mfold range (integers, default 5 50; defines fold-enrichment window for cross-correlation peak detection)

Outputs

  • Predicted fragment length d (integer, in base pairs)
  • Cross-correlation plot (diagnostic, optional PDF output)
  • Shift distance metrics (diagnostic output from predictd analysis)

How to apply

Run macs3 predictd on deduplicated ChIP sample using default parameters: macs3 predictd -i <filtered_chip.bed> -g <genome_size> -m 5 50 (mfold range). The tool performs cross-correlation analysis by shifting reads across strands and identifying the lag distance with maximum correlation, yielding fragment length d (e.g., 254 bp for CTCF data). The predicted d is then used as --extsize parameter in macs3 pileup (ChIP coverage extension), as the extension size in control background tracks (d/2 for d-background, typically 127 bp), and as minimum peak length constraint in macs3 bdgpeakcall. Validate by checking that d is biologically plausible for your protocol (typically 100–500 bp for sonication-based ChIP).

Read the full file on GitHub · 99 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 99 lines · 35 tokens per session scan A 0d9ed1a138fc

Subscribe to this mod's changes

fragment-length-estimation-cross-correlation is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 35 tokens to every session and 1,685 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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