--> --- name: bio-tcr-bcr-analysis-mixcr-analysis description: 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. tool_type: cli primary_tool: MiXCR measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---
Scanned 9/8/2026
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---
name: bio-tcr-bcr-analysis-mixcr-analysis
description: 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.
tool_type: cli
primary_tool: MiXCR
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
---
# MiXCR Analysis
## Complete Workflow (Recommended)
```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
### 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
```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
```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 output
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