Agent skill

Bio Long Read Sequencing Nanopore Methylation

by GPTomics in GPTomics/bioSkills

Calls DNA base modifications (5mC, 5hmC, 6mA, 4mC) directly from Oxford Nanopore and PacBio HiFi long reads encoded as MM/ML SAM tags, piles them into per-site bedMethyl with modkit (or pb-CpG-tools…

MITAuto-check passedResearch & Science

Install Bio Long Read Sequencing Nanopore Methylation

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-nanopore-methylation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-nanopore-methylation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/long-read-sequencing/nanopore-methylation .claude/skills/bio-long-read-sequencing-nanopore-methylation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-long-read-sequencing-nanopore-methylation
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3k tokens
SKILL.md length
1,231 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Calls DNA base modifications (5mC, 5hmC, 6mA, 4mC) directly from Oxford Nanopore and PacBio HiFi long reads encoded as MM/ML SAM tags, piles them into per-site bedMethyl with modkit (or pb-CpG-tools…

  • Works in 2 steps: If the reads were not basecalled with a… → The MM/ML tags silently die in a normal…
  • Calling 5mC/5hmC/6mA from a modBAM
  • SKILL.md covers Version Compatibility, The Single Most Important…, End-to-End modBAM Pipeline and MM / ML Tag Spec (what the…, plus 8 more sections
  • Runs Shell scripts from its folder

What it does

Bio Long Read Sequencing Nanopore Methylation is an agent skill from GPTomics/bioSkills. Calls DNA base modifications (5mC, 5hmC, 6mA, 4mC) directly from Oxford Nanopore and PacBio HiFi long reads encoded as MM/ML SAM tags, piles them into per-site bedMethyl with modkit (or pb-CpG-tools for PacBio), and produces phased allele-specific methylation. Covers why methylation is a basecalling decision that cannot be recovered later, the MM/ML tag-drop failure that silently zeroes methylation through alignment, the MM ? vs . no-call semantics, 5mC/5hmC resolution vs bisulfite, modkit's 10th-percentile…

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/modkit_methylation.sh` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Calling 5mC/5hmC/6mA from a modBAM
  • Generating bedMethyl
  • Preserving methylation tags through alignment
  • Doing allele-specific

Example prompts

  • “Use the bio-long-read-sequencing-nanopore-methylation skill to call DNA base modifications (5mC, 5hmC, 6mA, 4mC) directly from Oxford Nanopore and…”
  • “/bio-long-read-sequencing-nanopore-methylation”

Requirements

  • A Bash shell

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. If the reads were not basecalled with a modification model, the signal is already gone. Mods are inferred from raw signal at basecall time…
  2. The MM/ML tags silently die in a normal alignment workflow. samtools fastq drops auxiliary tags unless given -T MM,ML; minimap2 ignores…

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Shell), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Long Read Sequencing Nanopore Methylation loads about 3k tokens when it runs. Until then it costs about 201 tokens; SKILL.md has 1,231 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~201
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,231 words, ~3,021 tokens.

Download SKILL.mdSave it as .claude/skills/bio-long-read-sequencing-nanopore-methylation/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-long-read-sequencing-nanopore-methylation
description
Calls DNA base modifications (5mC, 5hmC, 6mA, 4mC) directly from Oxford Nanopore and PacBio HiFi long reads encoded as MM/ML SAM tags, piles them into per-site bedMethyl with modkit (or pb-CpG-tools for PacBio), and produces phased allele-specific methylation. Covers why methylation is a basecalling decision that cannot be recovered later, the MM/ML tag-drop failure that silently zeroes methylation through alignment, the MM ? vs . no-call semantics, 5mC/5hmC resolution vs bisulfite, modkit's 10th-percentile auto-threshold, and the haplotagged ASM workflow. Use when calling 5mC/5hmC/6mA from a modBAM, generating bedMethyl, preserving methylation tags through alignment, doing allele-specific or differential methylation, or QC-ing a modification BAM.
tool_type
cli
primary_tool
modkit

Version Compatibility

Reference examples tested with: modkit 0.3+, dorado 1.0+, minimap2 2.28+, samtools 1.19+, pb-CpG-tools 2.3+.

Before using code patterns, verify installed versions match. If versions differ:

  • CLI: <tool> --version then <tool> --help to confirm flags

Inputs that determine what is even possible - record them:

  • The basecaller MODIFICATION model (e.g. 5mCG_5hmCG) fixes which mods can ever be piled up; it must be requested at basecall time and cannot be added later.
  • The MM/ML tags must survive every fastq/alignment step or methylation is silently lost.
  • modkit auto-estimates the pass threshold from the data (per run); fix it for cross-sample comparisons.

If code throws an error, introspect the installed tool (modkit pileup --help, modkit --help) and adapt the example to the actual API rather than retrying.

Nanopore Methylation

"Call methylation from my long reads" -> First confirm the MM/ML tags exist and survived alignment, then pile them into per-site bedMethyl - because methylation is a basecalling decision, not something that can be added now.

  • CLI: modkit pileup aligned.bam out.bed --ref ref.fa --cpg --combine-strands

The Single Most Important Modern Insight -- Methylation Is a Basecalling Decision, and the Tags Silently Die in Alignment

Two facts gate the entire skill:

  1. If the reads were not basecalled with a modification model, the signal is already gone. Mods are inferred from raw signal at basecall time (ONT Remora model in Dorado; PacBio kinetics model in jasmine) and written into the unaligned BAM as MM/ML tags. A plain BAM or a FASTQ cannot yield methylation - there is no post-hoc tool. The only fix is re-basecalling from POD5 (dorado basecaller sup,5mCG_5hmCG pod5/). The agent's FIRST move is to check the tags exist: samtools view in.bam | head | grep -o 'MM:Z:[^\t]*'.
  2. The MM/ML tags silently die in a normal alignment workflow. samtools fastq drops auxiliary tags unless given -T MM,ML; minimap2 ignores them unless given -y; hard-clipping breaks MM's per-base skip counting unless -Y is set. Miss any one and the aligned BAM still sorts, indexes, and looks fine, but modkit pileup returns an empty/all-canonical bedMethyl with no error. Use dorado aligner (carries tags natively) or samtools fastq -T MM,ML | minimap2 -y -Y, and re-grep for MM:Z AFTER alignment.

End-to-End modBAM Pipeline

bash
# 1. MODS-BASECALL (from POD5; the only step that can ever produce methylation)
dorado basecaller sup,5mCG_5hmCG pod5/ > calls.bam      # unaligned BAM, has MM/ML

# 2. TAG-PRESERVING ALIGN (route a is simplest)
dorado aligner ref.mmi calls.bam > aligned.bam                                  # a) native
samtools fastq -T MM,ML calls.bam | minimap2 -y -Y -ax lr:hq ref.fa - \
  | samtools sort -o aligned.bam && samtools index aligned.bam                  # b) manual
samtools view aligned.bam | head | grep -q 'MM:Z' && echo 'tags survived'       # verify!

# 3. PILEUP -> bedMethyl (auto-thresholds at the 10th percentile of ML; NOT 0.5)
modkit pileup aligned.bam out.bed --ref ref.fa --cpg --combine-strands
bgzip out.bed && tabix -p bed out.bed.gz

# 4. (optional) DIFFERENTIAL methylation, long-read native
modkit dmr pair -a A.bed.gz -b B.bed.gz --ref ref.fa --regions cpgislands.bed -o dmr.tsv

MM / ML Tag Spec (what the numbers mean)

  • MM:Z encodes modification positions: <canonical base><strand><mod code><. or ?>,<skip counts>;. Mod codes: m=5mC, h=5hmC, a=6mA, c=4mC. The ./? modifier is load-bearing: . = skipped bases are implicitly canonical (count toward the unmodified denominator); ? = skipped bases are no-call/unknown (land in Nnocall, outside the denominator). Misreading ? as . inflates the canonical denominator and deflates methylation.
  • ML:B:C is a uint8 per call: value N means probability in [N/256, (N+1)/256), so 255 is ~0.998, never exactly 1.0. Do not threshold == 1.0.

bedMethyl Columns (modkit, 18 columns)

Cols 1-9 are tab-delimited BED9; cols 10-18 are space-delimited (a parsing gotcha). The ones that matter:

ColNameMeaning
10Nvalid_covNmod + Ncanonical + Nother_mod (the denominator; this is "coverage" for QC)
11percent_modified(Nmod / Nvalid_cov) * 100 (a percent, 0-100)
12Nmodpassing calls of this modification
13Ncanonicalpassing calls of the canonical base
14Nother_modpassing calls of a different mod on the same base (5hmC in a 5mC row)
16Nfailcalls below the pass threshold (excluded from Nvalid_cov)
18Nnocallaligned canonical base with no mod call (e.g. ?-skipped)

For count-based DMR (DSS/methylKit) hand over Nmod and Nvalid_cov, never percent_modified.

Decision Tree by Scenario

ScenarioRecommendedWhy
ONT 5mC for mammalsdorado ...sup,5mCG_5hmCG -> modkit pileup --cpg --combine-strandsmammalian 5mC is overwhelmingly CpG
Compare ONT to WGBS/arraymodkit pileup --combine-mods (or --preset traditional)WGBS conflates 5mC+5hmC; combine to match
Study 5hmC biologykeep 5mC and 5hmC split; ideally add oxBS/TAB-seqbisulfite cannot separate them
Plants (CHG/CHH) or bacterial 6mA/4mCall-context model + --motif (not --cpg)methylation is not CpG-restricted there
Allele-specific methylation / imprintingphase + haplotag -> modkit pileup --partition-tag HPone read carries SNV phase AND methylation
PacBio HiFi 5mCccs --hifi-kinetics -> jasmine -> pb-CpG-tools (or modkit)primrose is deprecated; Revio does 5mC on-instrument
Differential methylation statisticsmodkit dmr (native) or export to -> methylation-analysisDSS/methylKit for dispersion modeling
RNA modifications (m6A etc.)-> epitranscriptomicsdirect-RNA mods are out of scope here

Phased Allele-Specific Methylation

bash
# Order is strict: align (tags preserved) -> phase+haplotag -> pileup partitioned by HP
# 1-2. Clair3/DeepVariant -> whatshap/longphase phase + haplotag (adds HP:i:1/2) -> haplotype-phasing
modkit pileup aligned.haplotagged.bam asm_out/ --ref ref.fa --cpg --combine-strands --partition-tag HP
# --partition-tag writes one UNCOMPRESSED bedMethyl per HP value into asm_out/, named by the tag
# value (e.g. 1.bed, 2.bed). bgzip + tabix each before dmr, which requires indexed inputs:
bgzip asm_out/1.bed && tabix -p bed asm_out/1.bed.gz
bgzip asm_out/2.bed && tabix -p bed asm_out/2.bed.gz
modkit dmr pair -a asm_out/1.bed.gz -b asm_out/2.bed.gz --ref ref.fa -o asm.tsv

Each haplotype gets ~half the coverage, so the per-site 10x floor effectively wants ~20x total. Imprinted loci (one haplotype ~fully methylated) are the canonical positive control.

Per-Method Failure Modes

No methylation in a plain BAM

Trigger: BAM basecalled without a mods model. Mechanism: mods are a basecall-time decision. Symptom: no MM:Z tags; modkit returns nothing. Fix: re-basecall from POD5 with a mods model; there is no post-hoc tool.

Show full SKILL.md (484 more words)Show less
Tags died in alignment (the #1 silent killer)

Trigger: samtools fastq | minimap2 without -T MM,ML/-y. Mechanism: fastq export and minimap2 drop the tags. Symptom: valid aligned BAM, empty bedMethyl, no error. Fix: dorado aligner, or samtools fastq -T MM,ML | minimap2 -y -Y; verify MM:Z after alignment.

Methylation fraction looks too low

Trigger: misreading ? (no-call) as . (canonical). Mechanism: unscored bases counted as unmethylated. Symptom: deflated percent_modified; large Nnocall. Fix: check the MM modifier and Nnocall; modkit update-tags to convert styles if needed.

5mC understated vs WGBS

Trigger: comparing ONT-5mC-only to bisulfite. Mechanism: WGBS reads 5mC+5hmC together. Symptom: ONT looks lower by the 5hmC fraction. Fix: --combine-mods/--preset traditional to combine before comparing.

Cross-sample thresholds not comparable

Trigger: relying on modkit's auto-threshold per sample. Mechanism: the 10th-percentile cut is data-dependent. Symptom: sample-specific thresholds confound a DMR. Fix: fix a common --filter-threshold/--mod-thresholds across samples.

Quantitative Thresholds

ThresholdSourceRationale
Nvalid_cov >= 10 per CpGfield standardbelow it, single-site fractions are noisy (~20x total for phased)
modkit pass = 10th percentile of MLmodkit docsdiscards the lowest-confidence ~10%; improves WGBS concordance
R10 ONT vs WGBS r ~ 0.84-0.95benchmarksadequate-depth site-level concordance (R10 > R9)
ML 255 ~ 0.998 (not 1.0)SAM specuint8 bin [255/256, 1.0); never test == 1.0
no --min-coverage flag on pileupmodkit APIfilter bedMethyl on Nvalid_cov post-hoc instead

Common Errors

Error / symptomCauseSolution
Empty bedMethyl / Nvalid_cov 0tags dropped or never presentgrep MM:Z; re-basecall or re-align preserving tags
modkit pileup --min-coverage unknown flagno such flagfilter on Nvalid_cov (col 10) after pileup
modkit extract in.bam out.tsv errorsneeds a subcommandmodkit extract full / modkit extract calls
modkit dmr fails on raw .bedinputs must be indexedbgzip + tabix -p bed first
Sparse bedMethyl with --combine-strandsrecords lack MN tags (old basecaller or hard-clipped)basecall with current Dorado and align with -Y (no hard-clip)
Methylation lower than expected? read as .; or 5hmC excluded vs WGBScheck Nnocall; --combine-mods to match WGBS
primrose not found (PacBio)deprecated/archiveduse jasmine (or Revio on-instrument 5mC)

References

  • Simpson JT, Workman RE, Zuzarte PC, et al. 2017. Detecting DNA cytosine methylation using nanopore sequencing. Nat Methods 14:407-410.
  • Yuen ZW-S, Srivastava A, Daniel R, et al. 2021. Systematic benchmarking of tools for CpG methylation detection from nanopore sequencing (METEORE). Nat Commun 12:3438.
  • Tse OYO, Jiang P, Cheng SH, et al. 2021. Genome-wide detection of cytosine methylation by single molecule real-time sequencing. PNAS 118(5):e2019768118.
  • Cheetham SW, Kindlova M, Ewing AD. 2022. Methylartist: tools for visualizing modified bases from nanopore sequence data. Bioinformatics 38(11):3109-3112.
  • SAM Optional Fields Specification (SAMtags): MM/ML base-modification tags. samtools/hts-specs.
  • basecalling - The upstream gate: methylation must be requested at basecall time
  • long-read-alignment - Carry MM/ML through with -y -Y (or use dorado aligner)
  • haplotype-phasing - Phase + haplotag the BAM for allele-specific methylation
  • clair3-variants - SNVs to phase before allele-specific methylation
  • methylation-analysis/dmr-detection - DMR statistics downstream of bedMethyl
  • methylation-analysis/methylkit-analysis - methylKit differential methylation
  • epitranscriptomics/m6anet-analysis - Direct-RNA m6A (out of scope here)
  • workflows/methylation-pipeline - End-to-end methylation pipeline

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in long-read-sequencing/nanopore-methylation of GPTomics/bioSkills.

  • SKILL.md
  • examples/modkit_methylation.sh
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

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Questions about Bio Long Read Sequencing Nanopore Methylation

What does Bio Long Read Sequencing Nanopore Methylation do?

Calls DNA base modifications (5mC, 5hmC, 6mA, 4mC) directly from Oxford Nanopore and PacBio HiFi long reads encoded as MM/ML SAM tags, piles them into per-site bedMethyl with modkit (or pb-CpG-tools…. Bio Long Read Sequencing Nanopore Methylation is an agent skill from GPTomics/bioSkills. Calls DNA base modifications (5mC, 5hmC, 6mA, 4mC) directly from Oxford Nanopore and PacBio HiFi long reads encoded as MM/ML SAM tags, piles them into per-site bedMethyl with modkit (or pb-CpG-tools for PacBio), and produces phased allele-specific methylation.

When should I use Bio Long Read Sequencing Nanopore Methylation?

Bio Long Read Sequencing Nanopore Methylation fits situations like: calling 5mC/5hmC/6mA from a modBAM; generating bedMethyl; preserving methylation tags through alignment; doing allele-specific.

How do I install Bio Long Read Sequencing Nanopore Methylation in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-nanopore-methylation -a claude-code`. Or copy the skill folder (long-read-sequencing/nanopore-methylation in GPTomics/bioSkills) into .claude/skills/bio-long-read-sequencing-nanopore-methylation in your project. Claude Code loads it when a task matches its description.

How do I install Bio Long Read Sequencing Nanopore Methylation in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-nanopore-methylation -a codex`. Or copy the skill folder (long-read-sequencing/nanopore-methylation in GPTomics/bioSkills) into .agents/skills/bio-long-read-sequencing-nanopore-methylation in your project. Codex loads it when a task matches its description.

Can I use Bio Long Read Sequencing Nanopore Methylation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-nanopore-methylation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-long-read-sequencing-nanopore-methylation, .gemini/skills/bio-long-read-sequencing-nanopore-methylation, .github/skills/bio-long-read-sequencing-nanopore-methylation and .opencode/skills/bio-long-read-sequencing-nanopore-methylation in your project.

What does Bio Long Read Sequencing Nanopore Methylation need to run?

Going by SKILL.md and its folder, Bio Long Read Sequencing Nanopore Methylation needs a shell for the scripts in its folder. Our summary lists: A Bash shell.

Does Bio Long Read Sequencing Nanopore Methylation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bio Long Read Sequencing Nanopore Methylation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Long Read Sequencing Nanopore Methylation use?

Bio Long Read Sequencing Nanopore Methylation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Long Read Sequencing Nanopore Methylation use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Long Read Sequencing Nanopore Methylation?

Skills that share tags, products or a category with Bio Long Read Sequencing Nanopore Methylation: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Long Read Sequencing Nanopore Methylation?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.