tcr-bcr-analysis-mixcr-analysis

name	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

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.

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

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

mixcr refineTagsAndSort alignments.vdjca alignments_refined.vdjca

mixcr assemble alignments_refined.vdjca clones.clns

Step 3: Export Results

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

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.

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.

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