Agent skill

Bio Long Read Sequencing Basecalling

by GPTomics in GPTomics/bioSkills

Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG5hmCG, 6mA, m6A) at…

MITAuto-check passedResearch & Science

Install Bio Long Read Sequencing Basecalling

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

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

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-basecalling --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/basecalling .claude/skills/bio-long-read-sequencing-basecalling && 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-basecalling
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.5k tokens
SKILL.md length
1,422 words
Files
4
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG5hmCG, 6mA, m6A) at…

  • Works in 3 steps: Methylation is a basecalling decision,… → Downstream polish/variant models must… → Mixing model versions across a cohort is…
  • Converting POD5/FAST5 to reads
  • SKILL.md covers Version Compatibility, The Single Most Important…, Dorado Subcommand Taxonomy and Model Naming Scheme…, plus 7 more sections
  • Runs Shell scripts from its folder

What it does

Bio Long Read Sequencing Basecalling is an agent skill from GPTomics/bioSkills. Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG5hmCG, 6mA, m6A) at basecall time, and handling duplex, demultiplexing, trimming, and HERRO read correction. Covers why the model+version is an irreversible analysis decision, why methylation cannot be recovered later, and why downstream polish/variant models must match the basecaller. Use when converting POD5/FAST5 to reads, picking a…

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `examples/basecall_pipeline.sh`, `examples/dorado_basecall.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

  • Converting POD5/FAST5 to reads
  • Picking a Dorado model for R9/R10
  • Enabling methylation calling
  • Basecalling duplex

Example prompts

  • “Use the bio-long-read-sequencing-basecalling skill to basecall raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the…”
  • “/bio-long-read-sequencing-basecalling”

Requirements

  • A Bash shell

Workflow steps

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

  1. Methylation is a basecalling decision, not a later analysis step. Modified bases are inferred from raw signal at basecall time by Remora…
  2. Downstream polish/variant models must match the basecaller model+version. medaka and Clair3 ship per-model weights (Clair3…
  3. Mixing model versions across a cohort is a batch effect. Different model versions have different identity and homopolymer-indel error…

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 Basecalling loads about 3.5k tokens when it runs. Until then it costs about 175 tokens; SKILL.md has 1,422 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~175
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k

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). 1,422 words, ~3,507 tokens.

Download SKILL.mdSave it as .claude/skills/bio-long-read-sequencing-basecalling/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
bio-long-read-sequencing-basecalling
description
Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG_5hmCG, 6mA, m6A) at basecall time, and handling duplex, demultiplexing, trimming, and HERRO read correction. Covers why the model+version is an irreversible analysis decision, why methylation cannot be recovered later, and why downstream polish/variant models must match the basecaller. Use when converting POD5/FAST5 to reads, picking a Dorado model for R9/R10 or RNA004, enabling methylation calling, basecalling duplex, demultiplexing barcoded runs, or correcting reads for assembly.
tool_type
cli
primary_tool
dorado

Version Compatibility

Reference examples tested with: Dorado 1.0+, pod5 0.3+, samtools 1.19+, chopper 0.7+.

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 basecaller MODEL string (e.g. dna_r10.4.1_e8.2_400bps_sup@v5.2.0) sets the entire error profile and must be propagated to every downstream tool. Pin it.
  • Modified-base models carry a SECOND version (..._sup@v5.0.0_5mCG_5hmCG@v3); the mod version can lag the simplex version - check dorado download --list.
  • R9.4.1 and RNA002 models were removed from Dorado v1.0 defaults; legacy data needs an archived model path.

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

Nanopore Basecalling

"Basecall my Nanopore data" -> Convert raw signal (POD5) into reads with Dorado using the chemistry-matched model, deciding the accuracy tier and whether to call modifications now - because the model choice is baked irreversibly into the output.

  • CLI: dorado basecaller sup pod5s/ > calls.bam (simplex), dorado basecaller sup,5mCG_5hmCG pod5s/ > calls.bam (with methylation), dorado duplex sup pod5s/ > duplex.bam (duplex)

PacBio note: PacBio "basecalling" (CCS -> HiFi reads) runs on-instrument/in SMRT Link; users receive HiFi BAMs already at Q20-Q30+. This skill is Oxford Nanopore / Dorado. HiFi assembly lives in genome-assembly/hifi-assembly.

The Single Most Important Modern Insight -- There Is No "The Reads," Only "The Reads As Called By This Model"

Basecalling is not fixed preprocessing that yields a neutral FASTQ. The model and version chosen are an analysis decision written permanently into the BAM, with three consequences a naive user misses:

  1. Methylation is a basecalling decision, not a later analysis step. Modified bases are inferred from raw signal at basecall time by Remora models and emitted as MM/ML tags. A plain BAM/FASTQ with no MM/ML tags has thrown the signal away - mods CANNOT be recovered without re-basecalling from POD5. If methylation might ever matter, request it now (sup,5mCG_5hmCG) and KEEP the POD5. See nanopore-methylation.
  2. Downstream polish/variant models must match the basecaller model+version. medaka and Clair3 ship per-model weights (Clair3 r1041_e82_400bps_sup_v500; medaka the dotted r1041_e82_400bps_sup_v5.2.0). A mismatched model silently degrades accuracy with no error. Propagate the basecaller model name to every downstream step.
  3. Mixing model versions across a cohort is a batch effect. Different model versions have different identity and homopolymer-indel error profiles. Re-basecall the WHOLE cohort with ONE current model before joint or differential analysis.

Dorado Subcommand Taxonomy

Dorado (one GPU-first executable) replaced Guppy, which is end-of-life. Bonito is ONT's research/training basecaller (not production); Rerio hosts research-release models (niche mods, bacterial methylation).

SubcommandPurposeCanonical invocation
basecallersimplex basecallingdorado basecaller hac pod5s/ > calls.bam
duplextemplate+complement duplexdorado duplex sup pod5s/ > duplex.bam
demuxbarcode classification/splitdorado demux --kit-name SQK-NBD114-24 --output-dir out/ calls.bam
trimstandalone adapter/primer trimdorado trim reads.bam > trimmed.bam
alignerminimap2 alignment (carries MM/ML)dorado aligner ref.mmi reads.bam > aln.bam
correctHERRO single-read correctiondorado correct reads.fastq > corrected.fasta
summarysequencing-summary TSV from BAMdorado summary calls.bam > summary.tsv
downloadmodel managementdorado download --model <name> / --list

Model Naming Scheme (load-bearing)

Format {analyte}_{pore}_{chemistry}_{speed}@v{ver} + optional mod suffix, e.g. dna_r10.4.1_e8.2_400bps_sup@v5.2.0_5mCG_5hmCG@v3.

TokenMeaningExamples
analytemoleculedna, rna004
poreflow-cell generationr10.4.1 (current), r9.4.1 (legacy)
chemistrykit chemistrye8.2 (Kit 14)
speedtranslocation speed -> sampling rate400bps (5 kHz DNA), 130bps (RNA004, 4 kHz)
tiermodel size/accuracyfast, hac, sup
versionmodel version@v4.3.0, @v5.2.0, @v6.0.0

Passing the bare tier (sup) lets Dorado auto-detect chemistry from POD5 metadata and fetch the matching latest model; pin a version (sup@v5.2.0) or a full path for reproducibility. Append mods comma-separated (sup,5mCG_5hmCG,6mA); only one mod model per canonical base may be active.

Decision Tree by Scenario

ScenarioRecommendedWhy
Any analysis (variant/assembly/methylation)sup + matched model, pinned versionfast/hac error profile leaks into calls
Live run / adaptive sampling / quick QC onlyfastspeed; never for downstream analysis
Routine work, compute-limitedhacstrong accuracy/compute balance (v5.2 closed much of the gap to sup)
Methylation wanted now or maybe latersup,5mCG_5hmCG (DNA), keep POD5mods are unrecoverable from a plain BAM -> nanopore-methylation
Per-molecule accuracy, low input, phasingdorado duplex sup~Q30 reads, but expect <10% duplex yield
Diploid/phased T2T assembly from simplexdorado correct (HERRO) before assemblerhaplotype-aware Q22->Q40 -> genome-assembly/long-read-assembly
Barcoded multiplexed runbasecall --no-trim, then dorado demuxtrimming first strips barcodes before demux sees them
Legacy R9.4.1 / RNA002 dataexplicit archived model pathremoved from Dorado v1.0 default downloads
PacBio dataalready HiFi; no Dorado stepCCS runs on-instrument -> genome-assembly/hifi-assembly

Core Commands

bash
# Simplex, super-accuracy, auto-detected chemistry-matched model (BAM is the default output)
dorado basecaller sup pod5s/ > calls.bam

# Pin the model version for reproducibility
dorado basecaller dna_r10.4.1_e8.2_400bps_sup@v5.2.0 pod5s/ > calls.bam

# Call methylation AT basecall time (CpG 5mC + 5hmC); KEEP pod5s/ - mods are unrecoverable later
dorado basecaller sup,5mCG_5hmCG pod5s/ > calls.bam
dorado basecaller sup,6mA pod5s/ > calls.bam               # all-context 6mA
# RNA004 direct RNA (cDNA CANNOT call mods - PCR erases the signal):
dorado basecaller rna004_130bps_sup@v5.1.0,m6A_DRACH pod5s/ > rna_mods.bam

# FASTQ output and a per-read quality floor (relative filter, not a calibrated accuracy)
dorado basecaller sup pod5s/ --emit-fastq --min-qscore 10 > calls.fastq

# Duplex (needs raw POD5; cannot be recovered from simplex FASTQ); dx tag marks read types
dorado duplex sup pod5s/ > duplex.bam

# Demultiplex: basecall WITHOUT trimming, then demux (demux trims barcodes itself)
dorado basecaller sup pod5s/ --no-trim > calls.bam
dorado demux --kit-name SQK-NBD114-24 --output-dir demux/ calls.bam
dorado demux --kit-name SQK-NBD114-24 --barcode-both-ends --output-dir demux/ calls.bam  # stringent

# HERRO read correction for diploid/phased assembly (input FASTQ of HAC/SUP R10 reads >=10kb -> FASTA)
dorado download --model herro-v1
dorado correct reads.fastq > corrected.fasta

POD5 is ONT's default raw format (faster random access than FAST5). Convert FAST5 first:

bash
pod5 convert fast5 raw/*.fast5 --output pod5s/    # FAST5 is legacy; basecalling it directly is slow
pod5 view pod5s/                                   # summary table (replaces deprecated `pod5 inspect reads`)
pod5 merge pod5s/*.pod5 --output merged.pod5

Per-Method Failure Modes

Methylation gone forever

Trigger: basecalling without a mod model, then wanting 5mC later. Mechanism: Remora infers mods from raw signal at basecall time; a plain BAM has only bases. Symptom: no MM/ML tags; modkit pileup returns nothing. Fix: re-basecall from POD5 with sup,5mCG_5hmCG; keep POD5 archives.

Barcodes land in unclassified

Trigger: default --trim all basecall, then a separate dorado demux. Mechanism: trimming removes the barcode before demux can read it. Symptom: most reads in unclassified.bam, low classification rate. Fix: basecall --no-trim, then demux (it trims barcodes itself).

Show full SKILL.md (581 more words)Show less
Silent accuracy loss downstream

Trigger: polishing/calling with a medaka/Clair3 model that doesn't match the basecaller model+version. Mechanism: per-model neural weights expect a specific error profile. Symptom: no error, just quietly worse consensus/calls. Fix: propagate the basecaller model name; use medaka tools resolve_model --auto_model; pick the matching Clair3 model dir.

Duplex double-counting

Trigger: treating every read in a duplex BAM as an independent molecule. Mechanism: a simplex parent and its duplex offspring both appear. Symptom: inflated coverage/allele counts. Fix: the dx:i:-1 tag marks simplex parents of duplex reads - filter them when counting molecules (dx:i:1 = duplex, dx:i:0 = simplex-only).

Cohort batch effect

Trigger: runs basecalled with different model versions joined for analysis. Mechanism: version-specific identity/indel error profiles confound a technical batch with biology. Symptom: spurious between-run differences. Fix: re-basecall the whole cohort with one model version.

Quantitative Thresholds

ThresholdSourceRationale
sup for any analysisONT model guidancefast/hac error profiles contaminate variant/assembly/methylation calls
R10.4.1 SUP modal accuracy ~Q20 (99%)Sereika 2022dual-reader head fixes homopolymers; enables nanopore-only near-finished genomes
Duplex read ~Q30; yield typically <10% of readscommunity benchmarksduplex is library-prep/loading-limited, not free accuracy
A "Q20" base errs at ~Q12.5 empiricallyDelahaye 2021nanopore qscores >Q10 are overconfident posteriors; use for relative filtering only
HERRO input reads >=10 kbp, HAC/SUP R10Dorado correct docsHERRO operates on 4096-bp chunks; shorter reads dropped
--min-qscore 10 as a permissive QC floorconventionQ10 ~ 90% nominal; a starting filter, not a hard rule

Common Errors

Error / symptomCauseSolution
"Failed to determine sequencing chemistry from data"R9/RNA002 or non-standard kit; bare tier can't auto-resolvepass an explicit model path; for legacy chemistry use an archived model
No MM/ML tags in BAMbasecalled without a mod modelre-basecall from POD5 with sup,5mCG_5hmCG
Most reads unclassified after demuxtrimmed before demuxbasecall --no-trim, then demux
--model sup errorsmodel is the positional arg, not a flagdorado basecaller sup pod5s/
dorado correct reads.bam failsinput is FASTQ(.gz), output FASTAdorado correct reads.fastq > corrected.fasta
Out of GPU memorybatch too large for VRAM (sup is heaviest)lower --batchsize; or drop to hac
cDNA m6A calling returns nothingPCR erased native modificationsuse direct RNA (RNA004), not cDNA

References

  • Sereika M, Kirkegaard RH, Karst SM, et al. 2022. Oxford Nanopore R10.4 long-read sequencing enables the generation of near-finished bacterial genomes from pure cultures and metagenomes without short-read or reference polishing. Nat Methods 19:823-826.
  • Stanojević D, Lin D, Nurk S, Florez de Sessions P, Šikić M. 2026. Telomere-to-telomere assembly using HERRO-corrected Nanopore simplex reads. Nature (online ahead of print). DOI 10.1038/s41586-026-10563-y.
  • Wick RR, Judd LM, Holt KE. 2019. Performance of neural network basecalling tools for Oxford Nanopore sequencing. Genome Biol 20:129.
  • Pagès-Gallego M, de Ridder J. 2023. Comprehensive benchmark and architectural analysis of deep learning models for nanopore sequencing basecalling. Genome Biol 24:71.
  • Delahaye C, Nicolas J. 2021. Sequencing DNA with nanopores: troubles and biases. PLoS ONE 16(10):e0257521.
  • Gamaarachchi H, Samarakoon H, et al. 2025. The enduring advantages of the SLOW5 file format for raw nanopore sequencing data. GigaScience giaf118.
  • long-read-qc - Assess read length/quality and run health after basecalling
  • nanopore-methylation - Pile up the MM/ML tags this skill must request at basecall time
  • long-read-alignment - Map the reads; use -y to carry MM/ML tags through alignment
  • medaka-polishing - Consensus model that must match this basecaller model+version
  • clair3-variants - Variant model that must match this basecaller model+version
  • genome-assembly/long-read-assembly - Assemble the reads (HERRO-corrected for diploid/T2T)
  • genome-assembly/hifi-assembly - PacBio HiFi (basecalled on-instrument, not here)
  • epitranscriptomics/m6anet-analysis - ONT direct-RNA m6A from signal
  • workflows/longread-sv-pipeline - End-to-end basecall -> align -> SV call

© 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 3 other files in long-read-sequencing/basecalling of GPTomics/bioSkills.

  • SKILL.md
  • examples/basecall_pipeline.sh
  • examples/dorado_basecall.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.

Compare with similar skills

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Questions about Bio Long Read Sequencing Basecalling

What does Bio Long Read Sequencing Basecalling do?

Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG5hmCG, 6mA, m6A) at…. Bio Long Read Sequencing Basecalling is an agent skill from GPTomics/bioSkills. Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG5hmCG, 6mA, m6A) at basecall time, and handling duplex, demultiplexing, trimming, and HERRO read correction.

When should I use Bio Long Read Sequencing Basecalling?

Bio Long Read Sequencing Basecalling fits situations like: converting POD5/FAST5 to reads; picking a Dorado model for R9/R10; enabling methylation calling; basecalling duplex.

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

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

How do I install Bio Long Read Sequencing Basecalling in Codex?

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

Can I use Bio Long Read Sequencing Basecalling 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-basecalling -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-basecalling, .gemini/skills/bio-long-read-sequencing-basecalling, .github/skills/bio-long-read-sequencing-basecalling and .opencode/skills/bio-long-read-sequencing-basecalling in your project.

What does Bio Long Read Sequencing Basecalling need to run?

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

Does Bio Long Read Sequencing Basecalling 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 Basecalling 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 Basecalling use?

Bio Long Read Sequencing Basecalling 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 Basecalling use?

About 3.5k tokens (SKILL.md is roughly 14k 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 Basecalling?

Skills that share tags, products or a category with Bio Long Read Sequencing Basecalling: 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 Basecalling?

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.