Agent skill

Bio Long Read Sequencing Clair3 Variants

by GPTomics in GPTomics/bioSkills

Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and…

MITAuto-check passedResearch & Science

Install Bio Long Read Sequencing Clair3 Variants

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-clair3-variants -a claude-code

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

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-clair3-variants --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/clair3-variants .claude/skills/bio-long-read-sequencing-clair3-variants && 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-clair3-variants
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.9k tokens
SKILL.md length
1,198 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and…

  • Works in 2 steps: The model is hand-picked and a mismatch… → ONT indels in homopolymers/STRs are the…
  • Calling germline SNVs/indels from ONT
  • SKILL.md covers Version Compatibility, The Single Most Important…, Two-Stage Architecture and Model Selection, plus 7 more sections
  • Runs Shell scripts from its folder

What it does

Bio Long Read Sequencing Clair3 Variants is an agent skill from GPTomics/bioSkills. Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and basecaller-version-matched model, enabling read-based phasing, and benchmarking against GIAB with stratification. Covers why the model string is the experiment (no auto-detection, silent degradation on mismatch), why ONT homopolymer/STR indels are the residual error whole-genome F1 hides, and the somatic/trio/RNA…

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/clair3_workflow.sh` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics and Deep learning. 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 germline SNVs/indels from ONT
  • Choosing a Clair3 model
  • Phasing variants
  • Benchmarking long-read calls

Example prompts

  • “Use the bio-long-read-sequencing-clair3-variants skill to call germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long…”
  • “/bio-long-read-sequencing-clair3-variants”

Requirements

  • A Bash shell

Workflow steps

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

  1. The model is hand-picked and a mismatch fails silently. There is no auto-detection - the user must point --model_path at a specific model…
  2. ONT indels in homopolymers/STRs are the residual error whole-genome F1 conceals. Even on R10.4.1 sup, insertions/deletions in homopolymer…

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 Clair3 Variants loads about 2.9k tokens when it runs. Until then it costs about 183 tokens; SKILL.md has 1,198 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~183
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

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). 1,198 words, ~2,899 tokens.

Download SKILL.mdSave it as .claude/skills/bio-long-read-sequencing-clair3-variants/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-clair3-variants
description
Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and basecaller-version-matched model, enabling read-based phasing, and benchmarking against GIAB with stratification. Covers why the model string is the experiment (no auto-detection, silent degradation on mismatch), why ONT homopolymer/STR indels are the residual error whole-genome F1 hides, and the somatic/trio/RNA boundary to the ClairS/Clair3-Trio family. Use when calling germline SNVs/indels from ONT or HiFi BAMs, choosing a Clair3 model, phasing variants, or benchmarking long-read calls.
tool_type
cli
primary_tool
Clair3

Version Compatibility

Reference examples tested with: Clair3 2.0+, whatshap 2.0+, bcftools 1.19+, hap.py 0.3.15+.

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

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

Results depend on inputs that outlive the binary version - record them:

  • The Clair3 MODEL must match the platform + chemistry + basecaller tier + basecaller version (e.g. r1041_e82_400bps_sup_v500). There is NO auto-detection; --model_path is mandatory and a mismatch silently degrades calls.
  • Clair3 v2 moved TensorFlow -> PyTorch; models are pileup.pt/full_alignment.pt. v1 TensorFlow models do NOT load in v2.
  • The full ONT model set (every version, hac/fast, _with_mv signal-aware) lives in the rerio clair3_models/ repo; only a subset is bundled.

If code throws an error, introspect the installed tool (run_clair3.sh --help) and adapt the example to the actual API rather than retrying.

Clair3 Variant Calling

"Call variants from my long reads" -> Run Clair3 with the model that matches how the reads were basecalled, phase, and benchmark with stratification - because the model string, not the command, determines accuracy.

  • CLI: run_clair3.sh --bam_fn=aln.bam --ref_fn=ref.fa --output=out/ --threads=16 --platform=ont --model_path=/models/r1041_e82_400bps_sup_v500

Scope: germline diploid SNPs + small indels. NOT structural variants (-> structural-variants), NOT somatic/mosaic (-> ClairS/ClairS-TO), NOT RNA (-> Clair3-RNA).

The Single Most Important Modern Insight -- The Model String Is the Experiment, and ONT Indels Hide in the Strata

Clair3's accuracy is gated by two facts a naive user misses:

  1. The model is hand-picked and a mismatch fails silently. There is no auto-detection - the user must point --model_path at a specific model folder. Three axes must ALL match: chemistry (r941 vs r1041), basecaller tier (fast/hac/sup), and basecaller version (g5014/v430/v500/v520), plus the optional _with_mv signal-aware axis if the BAM has Dorado mv tags. Wrong model = no crash, no warning, measurably worse calls (indels most). Derive the model from the basecaller string in the run metadata; pick the model version closest to but not above the basecaller version.
  2. ONT indels in homopolymers/STRs are the residual error whole-genome F1 conceals. Even on R10.4.1 sup, insertions/deletions in homopolymer runs and short tandem repeats are the weak point (G/C homopolymers worst), because the pore cannot reliably count identical consecutive bases. A genome-wide indel F1 of ~99.5% hides much lower performance inside LowComplexity/homopolymer strata - exactly the medically relevant loci. HiFi largely solves this; do not transfer ONT-indel pessimism to HiFi. Always benchmark with GIAB stratification, never a single global number.

Two-Stage Architecture

Clair3 "symphonizes" two networks: a fast pileup model (summarized per-position statistics) that calls the large majority of sites, and a slow full-alignment model (haplotype-resolved read tensor) that re-evaluates only the uncertain subset. Internally Clair3 phases the top het-SNP pileup calls with WhatsHap, haplotags the BAM, and feeds the haplotagged reads to the full-alignment model - which is why read-based phasing buys ~6% indel F1, not cosmetics. Output: merge_output.vcf.gz (final).

Model Selection

Model name anatomy (r1041_e82_400bps_sup_v500): pore (r1041=R10.4.1), flowcell (e82), speed (400bps), basecaller tier (sup/hac/fast), basecaller version (v500=Dorado 5.0.0, g5014=Guppy 5.0.14). The _with_mv suffix uses Dorado move-table tags for best accuracy when present.

Data--platformModel
ONT R10.4.1 sup, Dorado v5.x, mv tags presentontr1041_e82_400bps_sup_v520_with_mv
ONT R10.4.1 sup, Dorado v5.0.0ontr1041_e82_400bps_sup_v500
ONT R10.4.1 hacontr1041_e82_400bps_hac_v500/_v520
ONT R9.4.1 (any tier)ontr941_prom_sup_g5014
PacBio HiFi Reviohifihifi_revio
PacBio HiFi Sequel IIhifihifi_sequel2
Illumina (supported)ilmnilmn
PacBio CLR-not supported -> PEPPER-Margin-DeepVariant

Decision Tree by Scenario

ScenarioToolWhy
Germline SNV/indel, single sampleClair3this skill
Somatic, paired tumor-normalClairSVAF-aware; Clair3 germline priors cannot find low-VAF somatic
Somatic, tumor-onlyClairS-TOtumor-only ensemble
De novo / Mendelian trioClair3-Nova / Clair3-Triofamily-aware
Long-read RNA variantsClair3-RNARNA model
ONT R10.4.1, also considering DeepVarianteitherneck-and-neck on R10 sup; native-ONT DeepVariant (Kolesnikov 2024) superseded PEPPER-Margin
Non-human / draft / bacterial referenceClair3 + --include_all_ctgsdefault calls only chr1-22,X,Y -> empty output otherwise
Cohort joint genotypingClair3 gVCF -> GLnexusbcftools merge on gVCFs is NOT joint genotyping

Core Commands

bash
# Germline ONT calling (model MUST match the basecaller)
run_clair3.sh \
  --bam_fn=aln.bam --ref_fn=ref.fa --output=clair3_out/ \
  --threads=16 --platform=ont \
  --model_path=/opt/models/r1041_e82_400bps_sup_v500
# final VCF: clair3_out/merge_output.vcf.gz

# Phase the final output VCF (WhatsHap); --longphase_for_phasing swaps only the INTERNAL
# phaser to LongPhase (faster, SV-aware). For a LongPhase-phased final VCF use
# --use_longphase_for_final_output_phasing instead of --enable_phasing.
run_clair3.sh ... --enable_phasing --longphase_for_phasing
# Phased calls go to clair3_out/phased_merge_output.vcf.gz; merge_output.vcf.gz stays UNPHASED.

# Non-human / draft assembly reference - call ALL contigs
run_clair3.sh ... --include_all_ctgs

# Targeted / amplicon panel
run_clair3.sh ... --bed_fn=panel.bed --gvcf

# Benchmark against GIAB with stratification (the step that reveals ONT indel errors)
hap.py giab_truth.vcf.gz clair3_out/merge_output.vcf.gz \
  -f giab_confident.bed -r ref.fa --engine=vcfeval \
  --stratification giab_stratifications.tsv -o bench/hg002

Per-Method Failure Modes

Silent model mismatch

Trigger: --model_path pointing at a model that does not match the basecaller chemistry/tier/version. Mechanism: no auto-detection; the wrong network runs. Symptom: no error, lower F1 (indels most). Fix: derive the model from the basecaller string; verify the folder exists (rerio for the full set); for v2 ensure .pt models.

Clair3 found nothing on a non-human reference

Trigger: bacterial genome or draft assembly without chr1-22,X,Y names. Mechanism: Clair3 calls only standard human contigs by default. Symptom: near-empty VCF. Fix: --include_all_ctgs.

Show full SKILL.md (484 more words)Show less
Global F1 looks great, clinical genes are wrong

Trigger: reporting only whole-genome F1. Mechanism: ONT indel errors concentrate in homopolymer/STR/low-complexity strata. Symptom: ~99.5% global indel F1 but much lower in LowComplexity. Fix: stratify with GIAB BEDs (Dwarshuis 2024); use CMRG for medically relevant genes.

Treating Clair3 as a somatic caller

Trigger: lowering --snp_min_af/--indel_min_af to catch low-VAF variants. Mechanism: germline model expects ~0.5/1.0 allele fractions, is not VAF-aware. Symptom: germline-model false positives at low AF, missed true somatic. Fix: ClairS (paired) / ClairS-TO (tumor-only).

v1 model with v2 Clair3

Trigger: an old TensorFlow model dir with Clair3 v2. Mechanism: v2 needs PyTorch .pt models. Symptom: model load failure. Fix: use pileup.pt/full_alignment.pt models (Converted Rerio).

Quantitative Thresholds

ThresholdSourceRationale
Recommended depth ~20-60xClair3 guidancesensitivity (hets, indels) falls off below ~20x; --min_coverage default 2 is a floor, not a recommendation
Phasing buys ~6% indel F1Zheng 2022haplotagged reads disambiguate indel alleles in repeats
ONT R10.4.1 sup: SNP F1 ~99.99%, indel F1 ~99.5%GIAB benchmarksindel residual lives in homopolymer/STR strata
--var_pct_full 0.3 (default)Clair3 READMEfraction of low-quality pileup calls re-run by full-alignment; raise for recall, slower
Stratify with GIAB / CMRGDwarshuis 2024global F1 hides the ONT indel problem

Common Errors

Error / symptomCauseSolution
Empty/near-empty VCF on non-human refdefault calls only chr1-22,X,Y--include_all_ctgs
Model fails to loadv1 TF model with v2 Clair3use .pt (PyTorch) models
--model_path .../models/ont not foundno generic ont/hifi modelpoint at a specific model subfolder
Worse-than-expected indelswrong-version or wrong-tier modelmatch the basecaller model exactly
"joint genotyping" gave odd mergesbcftools merge on gVCFs is not joint callinguse GLnexus
Looking for somatic/low-VAF variantsgermline calleruse ClairS / ClairS-TO

References

  • Zheng Z, Li S, Su J, Leung AWS, Lam TW, Luo R. 2022. Symphonizing pileup and full-alignment for deep learning-based long-read variant calling (Clair3). Nat Comput Sci 2:797-803.
  • Zheng Z, He M, Yu X, et al. 2026. Accelerated long-read variant calling with Clair3 for whole-genome sequencing. Bioinformatics (advance access) btag181.
  • Kolesnikov A, Cook D, Nattestad M, et al. 2024. Local read haplotagging enables accurate long-read small variant calling. Nat Commun 15:5907.
  • Dwarshuis N, Kalra D, McDaniel J, et al. 2024. The GIAB genomic stratifications resource for human reference genomes. Nat Commun 15:9029.
  • Lin JH, Chen LC, Yu SC, Huang YT. 2022. LongPhase: an ultra-fast chromosome-scale phasing algorithm for small and large variants. Bioinformatics 38(7):1816-1822.
  • Chen L, Zheng Z, Su J, et al. 2025. ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling. Nat Commun 16:9630.
  • basecalling - The basecaller model+version the Clair3 model must match
  • long-read-alignment - Produces the BAM (keep --MD; use minimap2 >=2.28)
  • haplotype-phasing - whatshap/longphase phasing and haplotagging Clair3 uses internally
  • medaka-polishing - ONT consensus; medaka diploid variant calling is deprecated in favor of Clair3
  • structural-variants - SVs are out of Clair3's scope (Sniffles2/cuteSV)
  • variant-calling/deepvariant - DeepVariant native ONT/HiFi models (neck-and-neck on R10)
  • variant-calling/vcf-statistics - Summarize/filter the VCF Clair3 emits
  • clinical-databases/variant-prioritization - Prioritize the called variants

© 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/clair3-variants of GPTomics/bioSkills.

  • SKILL.md
  • examples/clair3_workflow.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 Clair3 Variants

What does Bio Long Read Sequencing Clair3 Variants do?

Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and…. Bio Long Read Sequencing Clair3 Variants is an agent skill from GPTomics/bioSkills. Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and basecaller-version-matched model, enabling read-based phasing, and benchmarking against GIAB with stratification.

When should I use Bio Long Read Sequencing Clair3 Variants?

Bio Long Read Sequencing Clair3 Variants fits situations like: calling germline SNVs/indels from ONT; choosing a Clair3 model; phasing variants; benchmarking long-read calls.

How do I install Bio Long Read Sequencing Clair3 Variants in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-clair3-variants -a claude-code`. Or copy the skill folder (long-read-sequencing/clair3-variants in GPTomics/bioSkills) into .claude/skills/bio-long-read-sequencing-clair3-variants in your project. Claude Code loads it when a task matches its description.

How do I install Bio Long Read Sequencing Clair3 Variants in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-clair3-variants -a codex`. Or copy the skill folder (long-read-sequencing/clair3-variants in GPTomics/bioSkills) into .agents/skills/bio-long-read-sequencing-clair3-variants in your project. Codex loads it when a task matches its description.

Can I use Bio Long Read Sequencing Clair3 Variants 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-clair3-variants -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-clair3-variants, .gemini/skills/bio-long-read-sequencing-clair3-variants, .github/skills/bio-long-read-sequencing-clair3-variants and .opencode/skills/bio-long-read-sequencing-clair3-variants in your project.

What does Bio Long Read Sequencing Clair3 Variants need to run?

Going by SKILL.md and its folder, Bio Long Read Sequencing Clair3 Variants needs a shell for the scripts in its folder. Our summary lists: A Bash shell.

Does Bio Long Read Sequencing Clair3 Variants 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 Clair3 Variants 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 Clair3 Variants use?

Bio Long Read Sequencing Clair3 Variants 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 Clair3 Variants use?

About 2.9k 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 Clair3 Variants?

Skills that share tags, products or a category with Bio Long Read Sequencing Clair3 Variants: tangermeme Genomic Model Analysis (jmschrei/tangermeme, 318 stars), Cellxgene Census (davila7/claude-code-templates, 33k stars), Pixi Environment Builder (xuzhougeng/wisp-science, 1k stars) and Bio Imaging Mass Cytometry Cell Segmentation (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k 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 Clair3 Variants?

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.