tooluniverse-immune-repertoire-analysis-24861f — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited tooluniverse-immune-repertoire-analysis-24861f (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
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The primary manifest — the file an agent reads to learn what this artifact does.
Comprehensive skill for analyzing T-cell receptor (TCR) and B-cell receptor (BCR) repertoire sequencing data to characterize adaptive immune responses, clonal expansion, and antigen specificity.
Repertoire diversity reflects immune history. High clonality — a few clones dominating — indicates antigen-driven expansion, as seen in active infection, tumor-infiltrating lymphocytes, or chronic stimulation. Low diversity points to immunodeficiency or treatment-induced lymphopenia. Always compare observed metrics against healthy donor reference distributions before drawing conclusions; a Shannon entropy of 7 is unremarkable in a healthy adult but alarming post-chemotherapy.
iedb_search_tcell_assays and BVBRC_search_epitopes; never infer antigen identity from CDR3 alone.Adaptive immune receptor repertoire sequencing (AIRR-seq) enables comprehensive profiling of T-cell and B-cell populations through high-throughput sequencing of TCR and BCR variable regions. This skill provides an 8-phase workflow for:
Load AIRR-seq data from common formats (MiXCR, ImmunoSEQ, AIRR standard, 10x Genomics VDJ). Standardize columns to: cloneId, count, frequency, cdr3aa, cdr3nt, v_gene, j_gene, chain. Define clonotypes using one of three methods:
Aggregate by clonotype, sort by count, assign ranks.
Calculate diversity metrics for the repertoire:
Generate rarefaction curves to assess whether sequencing depth is sufficient.
Analyze V and J gene usage patterns weighted by clonotype count:
Characterize CDR3 sequences:
Identify expanded clonotypes above a frequency threshold (default: 95th percentile). Track clonotypes longitudinally across multiple timepoints to measure persistence, mean/max frequency, and fold changes.
Query epitope databases for known TCR-epitope associations:
iedb_search_tcell_assays): Search T-cell assay records by sequence or MHC class; use iedb_search_epitopes with sequence_contains for motif searchBVBRC_search_epitopes): Best for organism-based epitope discovery (e.g., taxon_id="2697049" for SARS-CoV-2); returns epitope sequences with T-cell/B-cell assay countsPubMed_search_articles): Search for CDR3 + epitope/antigen/specificityiedb_get_epitope_antigens (link epitope→antigen), iedb_get_epitope_mhc (MHC restriction)Link TCR/BCR clonotypes to cell phenotypes from paired single-cell RNA-seq:
Key Tools Used:
iedb_search_tcell_assays - T-cell assay records (sequence, MHC class filters)iedb_search_bcell - B-cell assay recordsiedb_search_epitopes - Epitope motif search via sequence_containsBVBRC_search_epitopes - Organism-based epitope discovery (best for pathogen-specific queries)NCBI_SRA_search_runs - Find public TCR/BCR-seq datasets (use strategy="AMPLICON")ImmPort_search_studies - NIAID immunology studies (vaccine trials, flow cytometry)PubMed_search_articles - Literature on TCR/BCR specificityUniProt_get_entry_by_accession - Antigen protein informationIntegration with Other Skills:
tooluniverse-single-cell - Single-cell transcriptomicstooluniverse-rnaseq-deseq2 - Bulk RNA-seq analysistooluniverse-variant-analysis - Somatic hypermutation analysis (BCR)from tooluniverse import ToolUniverse
# 1. Load data
tcr_data = load_airr_data("clonotypes.txt", format='mixcr')
# 2. Define clonotypes
clonotypes = define_clonotypes(tcr_data, method='vj_cdr3')
# 3. Calculate diversity
diversity = calculate_diversity(clonotypes['count'])
print(f"Shannon entropy: {diversity['shannon_entropy']:.2f}")
# 4. Detect expanded clones
expansion = detect_expanded_clones(clonotypes)
print(f"Expanded clonotypes: {expansion['n_expanded']}")
# 5. Analyze V(D)J usage
vdj_usage = analyze_vdj_usage(tcr_data)
# 6. Query epitope databases
top_clones = expansion['expanded_clonotypes']['clonotype'].head(10)
epitopes = query_epitope_database(top_clones)| Grade | Criteria | Example |
|---|---|---|
| Strong | Clonal expansion > 1% frequency, convergent recombination confirmed, epitope match in IEDB/VDJdb | CDR3 at 5% frequency with 3 nucleotide variants encoding same amino acid, IEDB hit |
| Moderate | Expanded clone (0.1-1%), V(D)J bias significant (chi-sq p < 0.01), partial epitope match | Clone at 0.5% with TRBV20-1 bias, similar CDR3 motif in VDJdb |
| Weak | Low-frequency expansion (0.01-0.1%), single timepoint only, no epitope database match | Moderately expanded clone without convergence or known specificity |
| Insufficient | Below detection threshold, sequencing depth < 10,000 clonotypes, no replication | Singleton clonotypes that may be PCR/sequencing artifacts |
ANALYSIS_DETAILS.md - Detailed code snippets for all 8 phasesUSE_CASES.md - Complete use cases (immunotherapy, vaccine, autoimmune, single-cell integration) and best practices~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.