bio-tcr-bcr-analysis-mixcr-analysis
Perform V(D)J alignment and clonotype assembly from TCR-seq or BCR-seq data using MiXCR. Use when processing raw immune repertoire sequencing data to identify clonotypes and their frequencies.
Best use case
bio-tcr-bcr-analysis-mixcr-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Perform V(D)J alignment and clonotype assembly from TCR-seq or BCR-seq data using MiXCR. Use when processing raw immune repertoire sequencing data to identify clonotypes and their frequencies.
Teams using bio-tcr-bcr-analysis-mixcr-analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bio-tcr-bcr-analysis-mixcr-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bio-tcr-bcr-analysis-mixcr-analysis Compares
| Feature / Agent | bio-tcr-bcr-analysis-mixcr-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Perform V(D)J alignment and clonotype assembly from TCR-seq or BCR-seq data using MiXCR. Use when processing raw immune repertoire sequencing data to identify clonotypes and their frequencies.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
## Version Compatibility
Reference examples tested with: MiXCR 4.6+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# MiXCR Analysis
**"Extract TCR/BCR clonotypes from my sequencing data"** → Assemble immune receptor sequences from raw reads, identify V(D)J gene segments, and generate clonotype tables for repertoire analysis.
- CLI: `mixcr analyze` for end-to-end TCR/BCR extraction and clonotype assembly
## Complete Workflow (Recommended)
**Goal:** Run end-to-end V(D)J alignment and clonotype assembly from raw FASTQ files in a single command.
**Approach:** Use MiXCR's preset-based `analyze` command which chains alignment, assembly, and export steps automatically.
```bash
mixcr analyze generic-tcr-amplicon \
--species human \
--rna \
--rigid-left-alignment-boundary \
--floating-right-alignment-boundary C \
input_R1.fastq.gz input_R2.fastq.gz \
output_prefix
mixcr analyze 10x-vdj-tcr \
input_R1.fastq.gz input_R2.fastq.gz \
output_prefix
```
## Step-by-Step Workflow
**Goal:** Process immune repertoire data through individual alignment, refinement, assembly, and export stages for fine-grained control.
**Approach:** Chain MiXCR CLI steps sequentially: align reads to V(D)J references, refine UMIs and sort, assemble clonotypes, then export results.
### Step 1: Align Reads
```bash
mixcr align \
--species human \
--preset generic-tcr-amplicon-umi \
input_R1.fastq.gz input_R2.fastq.gz \
alignments.vdjca
mixcr align \
--species human \
--rna \
-OallowPartialAlignments=true \
input_R1.fastq.gz input_R2.fastq.gz \
alignments.vdjca
```
### Step 2: Refine and Assemble
```bash
mixcr refineTagsAndSort alignments.vdjca alignments_refined.vdjca
mixcr assemble alignments_refined.vdjca clones.clns
```
### Step 3: Export Results
```bash
mixcr exportClones \
--chains TRB \
--preset full \
clones.clns \
clones.tsv
mixcr exportClones \
--chains TRB \
-cloneId -readCount -readFraction \
-nFeature CDR3 -aaFeature CDR3 \
-vGene -dGene -jGene \
clones.clns \
clones_custom.tsv
```
## Preset Protocols
| Protocol | Use Case |
|----------|----------|
| `generic-tcr-amplicon` | TCR amplicon sequencing |
| `generic-bcr-amplicon` | BCR amplicon sequencing |
| `generic-tcr-amplicon-umi` | TCR amplicon with UMIs |
| `rnaseq-tcr` | TCR extraction from bulk RNA-seq |
| `rnaseq-bcr` | BCR extraction from bulk RNA-seq |
| `10x-vdj-tcr` | 10x Genomics TCR enrichment |
| `10x-vdj-bcr` | 10x Genomics BCR enrichment |
| `takara-human-tcr-v2` | Takara SMARTer kit |
## Species Support
```bash
mixcr align --species human ...
mixcr align --species mmu ...
# Available: human, mmu, rat, rhesus, dog, pig, rabbit, chicken
```
## Output Format
| Column | Description |
|--------|-------------|
| cloneId | Unique clone identifier |
| readCount | Number of reads |
| cloneFraction | Proportion of repertoire |
| nSeqCDR3 | Nucleotide CDR3 sequence |
| aaSeqCDR3 | Amino acid CDR3 sequence |
| allVHitsWithScore | V gene assignments |
| allDHitsWithScore | D gene assignments |
| allJHitsWithScore | J gene assignments |
## Quality Metrics
**Goal:** Assess alignment and assembly quality to identify problematic samples.
**Approach:** Export MiXCR alignment reports and check key success rate metrics.
```bash
mixcr exportReports alignments.vdjca
# Key metrics:
# - Successfully aligned reads (>80% is good)
# - CDR3 found (>70% of aligned)
# - Clonotype count (varies by sample type)
```
## Parse MiXCR Output in Python
**Goal:** Load MiXCR clonotype tables into pandas for downstream analysis and integration.
**Approach:** Read tab-delimited export files and rename columns to standardized names.
```python
import pandas as pd
def load_mixcr_clones(filepath):
df = pd.read_csv(filepath, sep='\t')
df = df.rename(columns={
'readCount': 'count',
'cloneFraction': 'frequency',
'aaSeqCDR3': 'cdr3_aa',
'nSeqCDR3': 'cdr3_nt'
})
return df
```
## Related Skills
- vdjtools-analysis - Downstream diversity analysis
- scirpy-analysis - Single-cell VDJ integration
- repertoire-visualization - Visualize MiXCR outputRelated Skills
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