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