DocumentationDocumentationExamples
// examples
Examples Gallery
Real-world examples of AlignAIR in action — input sequences, commands, and expected outputs for different use cases.
// categories
01 / basic
Basic analysis
Single sequence processing
02 / batch
Batch processing
Large dataset analysis
03 / custom
Custom parameters
Optimized configurations
04 / advanced
Advanced
Complex workflows
// example 01
Basic heavy chain analysis
Single sequence V(D)J assignment with default parameters.
input · sequences.csv
sequence_id,sequence seq_001,CAGGTGCAGCTGGTGGAGTCTGGG... seq_002,GAGGTGCAGCTGGTGGAGTCTGGG...
command
python app.py run \ --model-checkpoint=/app/pretrained_models/IGH_S5F_576 \ --chain-type=heavy \ --sequences=/data/input/sequences.csv \ --save-path=/data/output/results
output · results.csv
sequence_id,v_call,d_call,j_call,productive seq_001,IGHV1-2*01,IGHD3-3*01,IGHJ4*01,True seq_002,IGHV1-3*01,IGHD2-2*01,IGHJ6*01,True
// processing
- 2 sequences processed
- Default thresholds (V:0.75, D:0.3, J:0.8)
- Both sequences are productive
// example 02
Light chain with custom thresholds
High-stringency analysis for clean data.
// use case
High-quality light chain sequences from flow-sorted B cells. Stricter thresholds for precise allele calling.
command
python app.py run \ --model-checkpoint=/app/pretrained_models/IGL_S5F_576 \ --chain-type=light \ --sequences=/data/input/light_chains.csv \ --save-path=/data/output/light_results \ --v-allele-threshold=0.9 \ --j-allele-threshold=0.85 \ --airr-format
// threshold impact
V calls — default (0.75)1,850
V calls — strict (0.9)1,650
Higher confidence, fewer ambiguous calls.
// output format
Full AIRR Schema with standardized column names for downstream analysis pipelines.
// example 03
Large dataset processing
Optimized parameters for 100K+ sequences.
command
python app.py run \ --model-checkpoint=/app/pretrained_models/IGH_S5F_576 \ --chain-type=heavy \ --sequences=/data/input/large_dataset.csv \ --save-path=/data/output/batch_results \ --batch-size=4096 \ --fix-orientation
// performance tips
- Increased batch size to 4096
- Enabled orientation fixing
- GPU memory: 16GB+
// processing times
100K sequences45 minutes
500K sequences3.5 hours
// resource usage
12GB
GPU memory
95%
GPU utilization
// templates
Quick start templates
standard heavy chain
python app.py run \ --model-checkpoint=/app/pretrained_models/IGH_S5F_576 \ --chain-type=heavy \ --sequences=/data/input/sequences.csv \ --save-path=/data/output
high-quality light chain
python app.py run \ --model-checkpoint=/app/pretrained_models/IGL_S5F_576 \ --chain-type=light \ --sequences=/data/input/light_chains.csv \ --save-path=/data/output \ --v-allele-threshold=0.85 \ --j-allele-threshold=0.9 \ --airr-format
// next
Ready to try these?
Use these examples as starting points for your own analyses. Modify parameters based on your specific data and requirements.
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