bio-workflows-tcr-pipeline
TCR/BCR Analysis Pipeline
Pipeline Overview
FASTQ → MiXCR align → Assemble → Export → VDJtools diversity → Visualization
Step 1: MiXCR Processing
# Align reads to V(D)J segments
mixcr align -s hsa -p rna-seq \
R1.fastq.gz R2.fastq.gz \
aligned.vdjca
# Assemble clonotypes
mixcr assemble aligned.vdjca clones.clns
# Export
mixcr exportClones clones.clns clones.txt
Step 2: VDJtools Analysis
# Convert to VDJtools format
vdjtools Convert -S mixcr clones.txt vdjtools/
# Diversity metrics
vdjtools CalcDiversityStats vdjtools/clones.txt diversity/
# Sample overlap
vdjtools CalcPairwiseDistances vdjtools/*.txt overlap/
Step 3: Visualization
# Spectratype plot
vdjtools PlotFancySpectratype vdjtools/clones.txt spectra/
# V usage
vdjtools PlotFancyVJUsage vdjtools/clones.txt usage/
QC Checkpoints
- After alignment: Check V/J assignment rate (>70% typical)
- After assembly: Verify clonotype count and coverage
- After diversity: Compare metrics to expected range
Related Skills
- tcr-bcr-analysis/mixcr-analysis - Detailed MiXCR usage
- tcr-bcr-analysis/vdjtools-analysis - Diversity metrics
- tcr-bcr-analysis/repertoire-visualization - Plots
More from gptomics/bioskills
bioskills
Installs 425 bioinformatics skills covering sequence analysis, RNA-seq, single-cell, variant calling, metagenomics, structural biology, and 56 more categories. Use when setting up bioinformatics capabilities or when a bioinformatics task requires specialized skills not yet installed.
100bio-epitranscriptomics-merip-preprocessing
Align and QC MeRIP-seq IP and input samples for m6A analysis. Use when preparing MeRIP-seq data for peak calling or differential methylation analysis.
5bio-data-visualization-multipanel-figures
Combine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, or matplotlib GridSpec with shared legends and panel labels. Use when combining multiple plots into publication figures.
5bio-data-visualization-specialized-omics-plots
Reusable plotting functions for common omics visualizations. Custom ggplot2/matplotlib implementations of volcano, MA, PCA, enrichment dotplots, boxplots, and survival curves. Use when creating volcano, MA, or enrichment plots.
5bio-read-qc-fastp-workflow
All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.
5bio-data-visualization-genome-tracks
Create genome browser-style visualizations showing multiple data tracks (coverage, peaks, genes) using pyGenomeTracks, Gviz, and IGV. Use when visualizing genomic data at specific loci with multiple aligned tracks.
5