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.
Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks.
$ npx skills add GPTomics/bioSkills --skill bio-workflows-tcr-pipeline -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-workflows-tcr-pipeline --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/workflows/tcr-pipeline .claude/skills/bio-workflows-tcr-pipeline && 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-workflows-tcr-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/tcr-pipeline into .claude/skills/bio-workflows-tcr-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-tcr-pipeline", 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/workflows/tcr-pipelineType 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-workflows-tcr-pipeline -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-workflows-tcr-pipeline --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/workflows/tcr-pipeline .agents/skills/bio-workflows-tcr-pipeline && 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-workflows-tcr-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/tcr-pipeline into .agents/skills/bio-workflows-tcr-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-tcr-pipeline", 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-workflows-tcr-pipeline -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-workflows-tcr-pipeline --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/workflows/tcr-pipeline .cursor/skills/bio-workflows-tcr-pipeline && 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-workflows-tcr-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/tcr-pipeline into .cursor/skills/bio-workflows-tcr-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-tcr-pipeline", 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 workflows/tcr-pipeline--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-workflows-tcr-pipeline -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-workflows-tcr-pipeline --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/workflows/tcr-pipeline .gemini/skills/bio-workflows-tcr-pipeline && 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-workflows-tcr-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/tcr-pipeline into .gemini/skills/bio-workflows-tcr-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-tcr-pipeline", 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-workflows-tcr-pipelineInstalls 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-workflows-tcr-pipeline -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/workflows/tcr-pipeline .github/skills/bio-workflows-tcr-pipeline && 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-workflows-tcr-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/tcr-pipeline into .github/skills/bio-workflows-tcr-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-tcr-pipeline", 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-workflows-tcr-pipeline -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-workflows-tcr-pipeline --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/workflows/tcr-pipeline .opencode/skills/bio-workflows-tcr-pipeline && 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-workflows-tcr-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/tcr-pipeline into .opencode/skills/bio-workflows-tcr-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-tcr-pipeline", 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-workflows-tcr-pipelineOrchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks.
Bio Workflows Tcr Pipeline is an agent skill from GPTomics/bioSkills. Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks. Use when deciding bulk vs single-cell (bulk amplicon/RNA-seq - MiXCR analyze preset - VDJtools/immunarch depth-normalized diversity and overlap - figures; 10x paired VDJ - MiXCR 10x preset or Cell Ranger - scirpy gene-expression integration, chain QC, clonotype clusters); and TCR vs BCR (TCR - exact CDR3-nt+V/J clonotypes, VDJtools diversity is fine; BCR -…
Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/tcr_full_pipeline.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.
5 steps, taken from the step headings 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 Workflows Tcr Pipeline loads about 4.7k tokens when it runs. Until then it costs about 225 tokens; SKILL.md has 1,532 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). 1,532 words, ~4,719 tokens.
.claude/skills/bio-workflows-tcr-pipeline/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: MiXCR 4.7+, VDJtools 1.2.1+, Immcantation suite 4.x, scirpy 0.24+
Before using code patterns, verify installed versions match. If versions differ:
<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Note: MiXCR 4.x replaced the 3.x hand-built mixcr align -s hsa -p rna-seq chain with the preset-driven mixcr analyze <preset> system, and 4.x refuses to run any command without an activated license (mixcr activate-license, or MI_LICENSE_FILE on HPC/Docker). A copied 3.x recipe fails on both counts.
"Analyze my immune-repertoire sequencing data end-to-end" -> Route the data by chemistry and receptor, assemble clonotypes with MiXCR, then hand off to depth-normalized diversity (bulk TCR), clonal clustering plus somatic-hypermutation and lineages (BCR), or single-cell gene-expression integration (10x), and finally figures.
This workflow is a router, not a fixed line. Two forks decide everything downstream; pick both before running anything.
A repertoire measurement is a depth- and chemistry-confounded sample of an unevenly-expanded clonal population, so the correct pipeline depends on how the library was made and which receptor was sequenced. Choosing the wrong branch silently produces plausible-but-wrong numbers.
| Axis | Bulk (amplicon or RNA-seq) | Single-cell (10x VDJ) |
|---|---|---|
| Assembler | mixcr analyze <bulk preset> | mixcr analyze 10x-sc-xcr-vdj OR Cell Ranger vdj |
| Chain pairing | UNPAIRED (TRB or IGH alone) | Native pairing (TRA+TRB, IGH+IGK/L) |
| Depth vs breadth | Deep repertoire, no cell state | Shallower, links receptor to transcriptome |
| Downstream | VDJtools / immunarch diversity + overlap -> figures | scirpy: chain QC, clonotype clusters, GEX integration |
| Best when | Diversity, overlap, tracking, deep clonotype capture | Antigen-specific cell state, alpha-beta / heavy-light pairing |
| Axis | TCR (TRA/TRB/TRG/TRD) | BCR (IGH/IGK/IGL) |
|---|---|---|
| Somatic hypermutation | None | Yes -- clone members are NOT identical |
| Clonotype definition | Exact CDR3-nt + V + J (after UMI/error correction) | NEVER exact CDR3; cluster same-V/J/junction-length by distance |
| Diversity path | VDJtools CalcDiversityStats on exact clonotypes is fine | Cluster clones FIRST, then diversity on clone_id |
| Extra stages | none | germline reconstruction, SHM, selection, Dowser lineage trees |
| Tool | VDJtools / immunarch | Immcantation (Change-O, SHazaM, SCOPer, Dowser, TIGGER) |
The most common pipeline mistake is running BCR through exact-CDR3 VDJtools diversity. SHM shatters one clone into hundreds of near-identical variants, so exact clonotypes over-count diversity and destroy lineage structure. BCR must route to Immcantation clonal clustering (distToNearest -> findThreshold) before any diversity, SHM, or lineage step. TCR has no SHM, so exact CDR3-nt+V/J is the correct, defensible clonotype and VDJtools diversity is appropriate.
FASTQ (+ chemistry, species, receptor known)
|
v
[0. License + preset choice] --- mixcr activate-license ; pick preset by kit
|
v
[1. MiXCR analyze] ---------- <preset> R1 R2 out_prefix -> clones.clns + reports
|
+-- QC: mixcr qc / exportQc align + chainUsage
|
v
FORK on data type
|
|-- bulk --> [2b. Export] exportClones (VDJtools) / exportAirr
| |
| v
| FORK on receptor
| |-- TCR --> [3t. DownSample to equal depth] --> CalcDiversityStats + overlap
| |-- BCR --> [3b. Immcantation] distToNearest->findThreshold->clones
| | -> CreateGermlines --cloned -> SHM -> Dowser trees
| v
| [4. Visualization] VDJtools / immunarch / ggplot
|
|-- single-cell --> [2s. exportAirr / Cell Ranger] --> [3s. scirpy]
chain_qc -> ir_dist -> define_clonotypes -> GEX integration
|
v
[5. Optional] specificity annotation (VDJdb / GLIPH2 / TCRdist) -- hypothesis, not labelMiXCR 4.x will not run unlicensed. Activate once (academic license is free), then choose the preset by the exact kit -- the preset encodes species, RNA vs DNA, 5' boundary model (floating for multiplex primers, rigid for 5'-RACE), tag pattern, and assembling feature. The wrong preset does not error; it silently mis-calls V and truncates CDR3.
mixcr activate-license # or: export MI_LICENSE_FILE=/path/mi.license
mixcr exportPreset --preset-name generic-amplicon # audit what a preset actually doesPreset by chemistry (verify against mixcr analyze --help and the built-in preset list; MiLaboratories renames occasionally):
| Data | Preset |
|---|---|
| Generic multiplex/RACE amplicon | generic-amplicon, generic-amplicon-with-umi (+ --species hsa, --rna/--dna, boundary mixins) |
| Bulk RNA-seq mining | rna-seq (judge by clonotype yield, not alignment %) |
| 10x single-cell V(D)J | 10x-sc-xcr-vdj |
| Takara SMARTer | takara-human-rna-tcr-umi-smarter-v2, takara-human-rna-bcr-umi-smarter |
| BD Rhapsody | bd-human-sc-xcr-rhapsody-cdr3 |
| Full component-skill preset table | tcr-bcr-analysis/mixcr-analysis |
# One command runs align -> refineTagsAndSort -> (assemblePartial) -> assemble -> export.
# From MiXCR 4.7, presets without an intrinsic assembling feature require --assemble-clonotypes-by.
# generic-amplicon REQUIRES material type + both alignment-boundary mixins (it errors without them).
# Multiplex primers on both ends -> floating boundaries; 5'RACE -> --rigid-left-alignment-boundary.
mixcr analyze generic-amplicon \
--species hsa \
--rna \
--floating-left-alignment-boundary \
--floating-right-alignment-boundary C \
sample_R1.fastq.gz sample_R2.fastq.gz \
results/sample
# QC every sample -- low alignment or off-target chains means wrong preset/species/contamination
mixcr qc results/sample.clns
mixcr exportQc align results/*.clns results/qc_align.pdf
mixcr exportQc chainUsage results/*.clns results/qc_chains.pdfQC checkpoint 1 (after align): amplicon libraries should align at high rate (often >80-90%); a low rate signals wrong species, wrong boundary model, or untrimmed primers. RNA-seq mining legitimately aligns a tiny fraction -- judge it by absolute clonotype yield. chainUsage catches cross-contamination and index hopping (a TRB library showing appreciable IGH).
Detailed alignment, UMI/cell-barcode handling, and export flags: tcr-bcr-analysis/mixcr-analysis.
# Bulk -> VDJtools-readable clonotype table (per chain)
mixcr exportClones -c TRB results/sample.clns results/sample.clones_TRB.tsv
# BCR or single-cell -> AIRR Rearrangement TSV (the Immcantation / scirpy interchange)
mixcr exportAirr results/sample.clns results/sample.airr.tsvQC checkpoint 2 (after assemble): a large reads-to-clonotypes drop-off is normal (millions of reads -> thousands of clones), especially after UMI collapse. Report the right denominator: uniqueMoleculeCount on UMI libraries (reporting reads re-introduces the PCR bias the UMIs removed), cells on single-cell, reads only on non-UMI bulk.
Diversity (richness, Shannon, clonality) and set-based overlap (Jaccard, shared-clonotype counts) are strictly increasing functions of sequencing depth. Comparing raw values across samples of unequal depth measures depth, not biology -- the single most common error in the field. DownSample every sample to a common depth (or read rarefaction curves at a common x) before comparing.
# 1. Equalize depth: set the target near the cohort lower quartile, and EXCLUDE (do not drag
# everyone down to) any sample far below it -- an under-sampled library cannot support a claim.
vdjtools DownSample -x 50000 -m metadata.txt ds/
# 2. Diversity on depth-normalized samples; report the resampled table for cross-sample claims
vdjtools CalcDiversityStats -m ds/metadata.txt diversity/
# 3. Overlap with a depth-robust, abundance-weighted metric (F2 / Morisita-Horn), not Jaccard
vdjtools CalcPairwiseDistances -m ds/metadata.txt overlap/Version caveat (MiXCR 4.x -> VDJtools): VDJtools is unmaintained for MiXCR 4.x and its parser breaks on raw exportClones output (Unable to parse clonotype string; 4.x injects commas and renames/moves columns). For a MiXCR 4.7+ cohort, prefer MiXCR's own mixcr postanalysis individual / mixcr postanalysis overlap (its native 4.x replacement for VDJtools diversity/overlap, with the same downsample-first semantics) over the vdjtools Convert -S mixcr route; if VDJtools is required, strip the added contig/target-sequence columns before Convert. immunarch (R) is the other modern alternative.
QC checkpoint 3 (before diversity): confirm all samples share one depth, and drop any sample whose rarefaction curve is still climbing steeply below that depth (under-sampled -- exclude rather than normalize the cohort down to it). Hold the clonotype match key (nt vs aa, +/-V, +/-J) constant study-wide; aa-level matching inflates apparent sharing via convergent recombination. Report clonality alongside a q=2 Hill number (inverse Simpson) and a rarefaction curve, not alone. immunarch is the modern R alternative with the same normalization semantics: tcr-bcr-analysis/vdjtools-analysis.
BCR cannot use exact clonotypes. Feed the AIRR TSV to Immcantation and follow the mandatory order: annotate -> (TIGGER genotype) -> per-sequence germline -> data-derived clonal threshold -> cluster -> per-clone germline -> SHM/selection -> lineage trees. The threshold from the bimodal distance-to-nearest distribution drives every downstream number; a wrong threshold merges or splits clones.
library(shazam); library(scoper); library(dowser)
db <- airr::read_rearrangement('results/sample.airr.tsv')
# 1. Clonal threshold: valley between the intra-clone and inter-clone modes (per dataset, never reused)
dtn <- distToNearest(db, model = 'ham', normalize = 'len')
thr <- findThreshold(dtn$dist_nearest, method = 'density')@threshold
# 2. Cluster within same-V/J/junction-length partitions at that threshold
cl <- hierarchicalClones(dtn, threshold = thr)
# 3. Reconstruct per-clone germline (CreateGermlines.py --cloned), then observedMutations, then Dowser getTreesCluster clones within an individual only (genotypes and thresholds are private). Diversity for BCR runs on clone_id, not exact CDR3. Full germline reconstruction, SHM quantification, BASELINe selection, and IgPhyML/Dowser trees: tcr-bcr-analysis/immcantation-analysis.
10x paired VDJ carries native chain pairing and links receptor to cell state. The clonotype definition is a choice, not a default, and multichain cells are likely doublets.
import scirpy as ir
import mudata as mu
airr = ir.io.read_airr('results/sample.airr.tsv') # or read_10x_vdj on Cell Ranger output
mdata = mu.MuData({'gex': gex_adata, 'airr': airr}) # scirpy 0.13+ stores AIRR as an awkward array
ir.pp.index_chains(mdata) # REQUIRED before chain_qc / clonotyping
ir.tl.chain_qc(mdata) # flag multichain (doublet) / orphan cells
# TCR path: exact-identity clonotypes on CDR3-nt + V/J
ir.pp.ir_dist(mdata) # default metric='identity'
ir.tl.define_clonotypes(mdata)
# BCR path: SHM breaks exact identity, so cluster. ir_dist MUST be recomputed with the SAME
# metric+sequence the clustering call uses (scirpy keys the matrix as ir_dist_{sequence}_{metric};
# the identity matrix above will not satisfy a normalized_hamming call).
# ir.pp.ir_dist(mdata, metric='normalized_hamming', sequence='nt')
# ir.tl.define_clonotype_clusters(mdata, sequence='nt', metric='normalized_hamming', same_v_gene=True)
# integrate with the scanpy GEX modality; measure expansion vs cell stateFiltering multichain/orphan cells before expansion analysis preferentially deletes small clones and inflates apparent expansion -- state the trade-off, do not blindly drop them. CellRanger BCR contigs are not IMGT-numbered and include partial/nonproductive contigs; reannotate with IgBLAST (dandelion/airrflow) before rigorous BCR clustering. GEX side (clustering, annotation): single-cell/preprocessing and single-cell/clustering. Full clonotype-definition decisions: tcr-bcr-analysis/scirpy-analysis.
Spectratype (CDR3-length), V-J usage circos, clonal-space bars, rarefaction curves, and clonal tracking across timepoints. Every figure inherits the depth caveat -- plot rarefaction at a common x, and track clones only after downsampling timepoints to equal depth. Recipes: tcr-bcr-analysis/repertoire-visualization.
Annotate or cluster clonotypes by likely antigen (VDJdb/McPAS lookup, or GLIPH2/TCRdist clustering). A database hit is a sequence match to a published antigen-specific receptor, not proof the clone binds that antigen. "Public" clonotypes are enriched for high generation-probability (Pgen), short, low-insertion CDR3s produced independently in many donors by convergent recombination (Venturi 2006 PNAS 103:18691-18696) -- publicity is not antigen selection. Treat every specificity call as a hypothesis, condition on Pgen, and validate. Handoff: tcr-bcr-analysis/specificity-annotation.
| Symptom | Cause | Fix |
|---|---|---|
| MiXCR exits immediately, "no license" | 4.x needs an activated license | mixcr activate-license, or set MI_LICENSE_FILE on HPC/Docker; whitelist the phone-home IPs on firewalled clusters |
mixcr align -s hsa -p rna-seq unrecognized | 3.x syntax removed in 4.x | Use mixcr analyze <preset> R1 R2 out_prefix |
| Analysis runs but clonotypes look wrong (truncated CDR3, inflated diversity) | Wrong preset -- RNA/DNA, boundary model, or missing tag pattern | Match preset to the exact kit; mixcr exportPreset to audit; set --species on generic presets |
MiXCR 4.7 errors on analyze needing an assembling feature | Preset lacks an intrinsic assembling feature | Add --assemble-clonotypes-by CDR3 |
| Diversity/clonality differ across samples but tracks read count | Comparing raw diversity at unequal depth | DownSample to a common depth first; report the resampled table / rarefaction at common x |
| BCR clones fragmented, diversity absurdly high, no lineages | Exact-CDR3 clonotypes applied to a hypermutating receptor | Route BCR to Immcantation distToNearest -> findThreshold clustering before any diversity/SHM |
| SHM counts inflated, spurious mutations in junction | No germline reconstruction, or junction not masked | CreateGermlines.py -g dmask then --cloned; restrict observedMutations to IMGT_V |
| scirpy expansion inflated by doublets | multichain cells not filtered | Run chain_qc and drop multichain cells before clonotype/expansion analysis |
| Overlap dominated by the shallower sample | Jaccard / shared-count on unequal depth | Downsample, then use abundance-weighted F2 or Morisita-Horn |
© 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 workflows/tcr-pipeline 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 Workflows Tcr Pipeline 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 Workflows Tcr Pipeline this skillGPTomics/bioSkills | 1.2k | 1 repos | ~4.7k | 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
Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks. Bio Workflows Tcr Pipeline is an agent skill from GPTomics/bioSkills. Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks.
Bio Workflows Tcr Pipeline fits situations like: 10x paired VDJ - MiXCR 10x preset; cell Ranger - scirpy gene-expression integration; clonotype clusters); TCR vs BCR (TCR - exact CDR3-nt+V/J clonotypes.
Run `npx skills add GPTomics/bioSkills --skill bio-workflows-tcr-pipeline -a claude-code`. Or copy the skill folder (workflows/tcr-pipeline in GPTomics/bioSkills) into .claude/skills/bio-workflows-tcr-pipeline in your project. Claude Code loads it when a task matches its description.
Run `npx skills add GPTomics/bioSkills --skill bio-workflows-tcr-pipeline -a codex`. Or copy the skill folder (workflows/tcr-pipeline in GPTomics/bioSkills) into .agents/skills/bio-workflows-tcr-pipeline 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-workflows-tcr-pipeline -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-workflows-tcr-pipeline, .gemini/skills/bio-workflows-tcr-pipeline, .github/skills/bio-workflows-tcr-pipeline and .opencode/skills/bio-workflows-tcr-pipeline in your project.
Going by SKILL.md and its folder, Bio Workflows Tcr Pipeline needs a shell for the scripts in its folder. Our summary lists: A Bash shell; Docker.
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 Workflows Tcr Pipeline is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.7k tokens (SKILL.md is roughly 19k 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 Workflows Tcr Pipeline: 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.