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.
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…
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-basecalling -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-basecalling --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/basecalling .claude/skills/bio-long-read-sequencing-basecalling && 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-basecalling" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/basecalling into .claude/skills/bio-long-read-sequencing-basecalling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-basecalling", 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/basecallingType 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-basecalling -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-basecalling --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/basecalling .agents/skills/bio-long-read-sequencing-basecalling && 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-basecalling" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/basecalling into .agents/skills/bio-long-read-sequencing-basecalling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-basecalling", 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-basecalling -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-basecalling --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/basecalling .cursor/skills/bio-long-read-sequencing-basecalling && 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-basecalling" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/basecalling into .cursor/skills/bio-long-read-sequencing-basecalling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-basecalling", 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/basecalling--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-basecalling -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-basecalling --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/basecalling .gemini/skills/bio-long-read-sequencing-basecalling && 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-basecalling" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/basecalling into .gemini/skills/bio-long-read-sequencing-basecalling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-basecalling", 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-basecallingInstalls 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-basecalling -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/basecalling .github/skills/bio-long-read-sequencing-basecalling && 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-basecalling" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/basecalling into .github/skills/bio-long-read-sequencing-basecalling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-basecalling", 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-basecalling -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-basecalling --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/basecalling .opencode/skills/bio-long-read-sequencing-basecalling && 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-basecalling" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/basecalling into .opencode/skills/bio-long-read-sequencing-basecalling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-basecalling", 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-basecallingBasecalls 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. 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.
3 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 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.
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). 1,422 words, ~3,507 tokens.
.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.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:
<tool> --version then <tool> --help to confirm flagsResults depend on inputs that outlive the binary version - record them:
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...._sup@v5.0.0_5mCG_5hmCG@v3); the mod version can lag the simplex version - check dorado download --list.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.
"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.
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.
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:
sup,5mCG_5hmCG) and KEEP the POD5. See nanopore-methylation.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.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).
| Subcommand | Purpose | Canonical invocation |
|---|---|---|
basecaller | simplex basecalling | dorado basecaller hac pod5s/ > calls.bam |
duplex | template+complement duplex | dorado duplex sup pod5s/ > duplex.bam |
demux | barcode classification/split | dorado demux --kit-name SQK-NBD114-24 --output-dir out/ calls.bam |
trim | standalone adapter/primer trim | dorado trim reads.bam > trimmed.bam |
aligner | minimap2 alignment (carries MM/ML) | dorado aligner ref.mmi reads.bam > aln.bam |
correct | HERRO single-read correction | dorado correct reads.fastq > corrected.fasta |
summary | sequencing-summary TSV from BAM | dorado summary calls.bam > summary.tsv |
download | model management | dorado download --model <name> / --list |
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.
| Token | Meaning | Examples |
|---|---|---|
| analyte | molecule | dna, rna004 |
| pore | flow-cell generation | r10.4.1 (current), r9.4.1 (legacy) |
| chemistry | kit chemistry | e8.2 (Kit 14) |
| speed | translocation speed -> sampling rate | 400bps (5 kHz DNA), 130bps (RNA004, 4 kHz) |
| tier | model size/accuracy | fast, hac, sup |
| version | model 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.
| Scenario | Recommended | Why |
|---|---|---|
| Any analysis (variant/assembly/methylation) | sup + matched model, pinned version | fast/hac error profile leaks into calls |
| Live run / adaptive sampling / quick QC only | fast | speed; never for downstream analysis |
| Routine work, compute-limited | hac | strong accuracy/compute balance (v5.2 closed much of the gap to sup) |
| Methylation wanted now or maybe later | sup,5mCG_5hmCG (DNA), keep POD5 | mods are unrecoverable from a plain BAM -> nanopore-methylation |
| Per-molecule accuracy, low input, phasing | dorado duplex sup | ~Q30 reads, but expect <10% duplex yield |
| Diploid/phased T2T assembly from simplex | dorado correct (HERRO) before assembler | haplotype-aware Q22->Q40 -> genome-assembly/long-read-assembly |
| Barcoded multiplexed run | basecall --no-trim, then dorado demux | trimming first strips barcodes before demux sees them |
| Legacy R9.4.1 / RNA002 data | explicit archived model path | removed from Dorado v1.0 default downloads |
| PacBio data | already HiFi; no Dorado step | CCS runs on-instrument -> genome-assembly/hifi-assembly |
# 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.fastaPOD5 is ONT's default raw format (faster random access than FAST5). Convert FAST5 first:
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.pod5Trigger: 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.
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).
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.
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).
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.
| Threshold | Source | Rationale |
|---|---|---|
sup for any analysis | ONT model guidance | fast/hac error profiles contaminate variant/assembly/methylation calls |
| R10.4.1 SUP modal accuracy ~Q20 (99%) | Sereika 2022 | dual-reader head fixes homopolymers; enables nanopore-only near-finished genomes |
| Duplex read ~Q30; yield typically <10% of reads | community benchmarks | duplex is library-prep/loading-limited, not free accuracy |
| A "Q20" base errs at ~Q12.5 empirically | Delahaye 2021 | nanopore qscores >Q10 are overconfident posteriors; use for relative filtering only |
| HERRO input reads >=10 kbp, HAC/SUP R10 | Dorado correct docs | HERRO operates on 4096-bp chunks; shorter reads dropped |
--min-qscore 10 as a permissive QC floor | convention | Q10 ~ 90% nominal; a starting filter, not a hard rule |
| Error / symptom | Cause | Solution |
|---|---|---|
| "Failed to determine sequencing chemistry from data" | R9/RNA002 or non-standard kit; bare tier can't auto-resolve | pass an explicit model path; for legacy chemistry use an archived model |
| No MM/ML tags in BAM | basecalled without a mod model | re-basecall from POD5 with sup,5mCG_5hmCG |
Most reads unclassified after demux | trimmed before demux | basecall --no-trim, then demux |
--model sup errors | model is the positional arg, not a flag | dorado basecaller sup pod5s/ |
dorado correct reads.bam fails | input is FASTQ(.gz), output FASTA | dorado correct reads.fastq > corrected.fasta |
| Out of GPU memory | batch too large for VRAM (sup is heaviest) | lower --batchsize; or drop to hac |
| cDNA m6A calling returns nothing | PCR erased native modifications | use direct RNA (RNA004), not cDNA |
-y to carry MM/ML tags through alignment© 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 3 other files in long-read-sequencing/basecalling 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 Basecalling 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 Basecalling this skillGPTomics/bioSkills | 1.2k | 1 repos | ~3.5k | 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
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.
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.
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.
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.
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.
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.
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 Basecalling is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
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.
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.
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.