Alphagenome Single Variant Analysis
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
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…
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-nanopore-methylation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-nanopore-methylation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "bio-long-read-sequencing-nanopore-methylation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/nanopore-methylation into .claude/skills/bio-long-read-sequencing-nanopore-methylation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-nanopore-methylation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/nanopore-methylationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-nanopore-methylation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-nanopore-methylation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/long-read-sequencing/nanopore-methylation .agents/skills/bio-long-read-sequencing-nanopore-methylation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bio-long-read-sequencing-nanopore-methylation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/nanopore-methylation into .agents/skills/bio-long-read-sequencing-nanopore-methylation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-nanopore-methylation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-nanopore-methylation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-nanopore-methylation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/long-read-sequencing/nanopore-methylation .cursor/skills/bio-long-read-sequencing-nanopore-methylation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bio-long-read-sequencing-nanopore-methylation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/nanopore-methylation into .cursor/skills/bio-long-read-sequencing-nanopore-methylation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-nanopore-methylation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/GPTomics/bioSkills.git --path long-read-sequencing/nanopore-methylation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-nanopore-methylation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-nanopore-methylation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/long-read-sequencing/nanopore-methylation .gemini/skills/bio-long-read-sequencing-nanopore-methylation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bio-long-read-sequencing-nanopore-methylation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/nanopore-methylation into .gemini/skills/bio-long-read-sequencing-nanopore-methylation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-nanopore-methylation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-nanopore-methylationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-nanopore-methylation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/long-read-sequencing/nanopore-methylation .github/skills/bio-long-read-sequencing-nanopore-methylation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bio-long-read-sequencing-nanopore-methylation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/nanopore-methylation into .github/skills/bio-long-read-sequencing-nanopore-methylation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-nanopore-methylation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-nanopore-methylation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-nanopore-methylation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/long-read-sequencing/nanopore-methylation .opencode/skills/bio-long-read-sequencing-nanopore-methylation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bio-long-read-sequencing-nanopore-methylation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/nanopore-methylation into .opencode/skills/bio-long-read-sequencing-nanopore-methylation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-nanopore-methylation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
bio-long-read-sequencing-nanopore-methylationCalls 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. 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.
2 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.
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.
Ships script files (Shell), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,231 words, ~3,021 tokens.
.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.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:
<tool> --version then <tool> --help to confirm flagsInputs that determine what is even possible - record them:
5mCG_5hmCG) fixes which mods can ever be piled up; it must be requested at basecall time and cannot be added later.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.
"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.
modkit pileup aligned.bam out.bed --ref ref.fa --cpg --combine-strandsTwo facts gate the entire skill:
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]*'.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.# 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.tsvMM: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.Cols 1-9 are tab-delimited BED9; cols 10-18 are space-delimited (a parsing gotcha). The ones that matter:
| Col | Name | Meaning |
|---|---|---|
| 10 | Nvalid_cov | Nmod + Ncanonical + Nother_mod (the denominator; this is "coverage" for QC) |
| 11 | percent_modified | (Nmod / Nvalid_cov) * 100 (a percent, 0-100) |
| 12 | Nmod | passing calls of this modification |
| 13 | Ncanonical | passing calls of the canonical base |
| 14 | Nother_mod | passing calls of a different mod on the same base (5hmC in a 5mC row) |
| 16 | Nfail | calls below the pass threshold (excluded from Nvalid_cov) |
| 18 | Nnocall | aligned canonical base with no mod call (e.g. ?-skipped) |
For count-based DMR (DSS/methylKit) hand over Nmod and Nvalid_cov, never percent_modified.
| Scenario | Recommended | Why |
|---|---|---|
| ONT 5mC for mammals | dorado ...sup,5mCG_5hmCG -> modkit pileup --cpg --combine-strands | mammalian 5mC is overwhelmingly CpG |
| Compare ONT to WGBS/array | modkit pileup --combine-mods (or --preset traditional) | WGBS conflates 5mC+5hmC; combine to match |
| Study 5hmC biology | keep 5mC and 5hmC split; ideally add oxBS/TAB-seq | bisulfite cannot separate them |
| Plants (CHG/CHH) or bacterial 6mA/4mC | all-context model + --motif (not --cpg) | methylation is not CpG-restricted there |
| Allele-specific methylation / imprinting | phase + haplotag -> modkit pileup --partition-tag HP | one read carries SNV phase AND methylation |
| PacBio HiFi 5mC | ccs --hifi-kinetics -> jasmine -> pb-CpG-tools (or modkit) | primrose is deprecated; Revio does 5mC on-instrument |
| Differential methylation statistics | modkit dmr (native) or export to -> methylation-analysis | DSS/methylKit for dispersion modeling |
| RNA modifications (m6A etc.) | -> epitranscriptomics | direct-RNA mods are out of scope here |
# 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.tsvEach 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.
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.
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.
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.
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.
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.
| Threshold | Source | Rationale |
|---|---|---|
| Nvalid_cov >= 10 per CpG | field standard | below it, single-site fractions are noisy (~20x total for phased) |
| modkit pass = 10th percentile of ML | modkit docs | discards the lowest-confidence ~10%; improves WGBS concordance |
| R10 ONT vs WGBS r ~ 0.84-0.95 | benchmarks | adequate-depth site-level concordance (R10 > R9) |
| ML 255 ~ 0.998 (not 1.0) | SAM spec | uint8 bin [255/256, 1.0); never test == 1.0 |
no --min-coverage flag on pileup | modkit API | filter bedMethyl on Nvalid_cov post-hoc instead |
| Error / symptom | Cause | Solution |
|---|---|---|
| Empty bedMethyl / Nvalid_cov 0 | tags dropped or never present | grep MM:Z; re-basecall or re-align preserving tags |
modkit pileup --min-coverage unknown flag | no such flag | filter on Nvalid_cov (col 10) after pileup |
modkit extract in.bam out.tsv errors | needs a subcommand | modkit extract full / modkit extract calls |
modkit dmr fails on raw .bed | inputs must be indexed | bgzip + tabix -p bed first |
Sparse bedMethyl with --combine-strands | records 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 WGBS | check Nnocall; --combine-mods to match WGBS |
| primrose not found (PacBio) | deprecated/archived | use jasmine (or Revio on-instrument 5mC) |
-y -Y (or use dorado aligner)© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files in long-read-sequencing/nanopore-methylation of GPTomics/bioSkills.
Open the folder on GitHubat commit d91ed3d
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.
Bio Long Read Sequencing Nanopore Methylation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Bio Long Read Sequencing Nanopore Methylation this skillGPTomics/bioSkills | 1.2k | 1 repos | ~3k | Automated safety check: Pass | MIT | |
| Alphagenome Single Variant Analysisgoogle-deepmind/science-skills | 3.2k | 2 repos | ~3k | Automated safety check: Notes | Apache-2.0 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Clinvar Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.9k | Automated safety check: Notes | Apache-2.0 | |
| Metabolic Study Planneraiming-lab/AutoResearchClaw | 15k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Dbsnp Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.4k | Automated safety check: Notes | Apache-2.0 |
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
K-Dense-AI/scientific-agent-skills
Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.
google-deepmind/science-skills
A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…
aiming-lab/AutoResearchClaw
Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.
google-deepmind/science-skills
A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.
aiming-lab/AutoResearchClaw
Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.
GPTomics/bioSkills
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.
GPTomics/bioSkills
Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.
GPTomics/bioSkills
Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.
GPTomics/bioSkills
Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.
GPTomics/bioSkills
Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.
GPTomics/bioSkills
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.
Categories
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.