Clair3 Variant Calling
Basic Usage
# ONT variant calling
run_clair3.sh \
--bam_fn=sample.bam \
--ref_fn=reference.fasta \
--threads=32 \
--platform=ont \
--model_path=${CONDA_PREFIX}/bin/models/ont \
--output=clair3_output
# PacBio HiFi variant calling
run_clair3.sh \
--bam_fn=sample.bam \
--ref_fn=reference.fasta \
--threads=32 \
--platform=hifi \
--model_path=${CONDA_PREFIX}/bin/models/hifi \
--output=clair3_output
# Output: clair3_output/merge_output.vcf.gz
Platform-Specific Models
| Platform |
Model |
Recommended Coverage |
| ONT R10 |
r1041_e82_400bps_sup_v430 |
30-60x |
| ONT R9 |
r941_prom_sup_g5014 |
30-60x |
| PacBio HiFi |
hifi |
20-40x |
| PacBio CLR |
- |
Use PEPPER-Margin-DeepVariant |
# List available models
ls ${CONDA_PREFIX}/bin/models/
# Specify exact model
run_clair3.sh \
--bam_fn=sample.bam \
--ref_fn=reference.fasta \
--model_path=${CONDA_PREFIX}/bin/models/r1041_e82_400bps_sup_v430 \
--output=clair3_out \
--threads=32
Key Parameters
| Parameter |
Description |
| --platform |
ont, hifi, or ilmn |
| --model_path |
Path to trained model |
| --bed_fn |
Restrict calling to regions |
| --include_all_ctgs |
Call on all contigs (not just chr1-22,X,Y) |
| --no_phasing_for_fa |
Disable phasing |
| --gvcf |
Output gVCF format |
| --qual |
Minimum variant quality (default: 2) |
Region-Specific Calling
# Call variants in specific regions
run_clair3.sh \
--bam_fn=sample.bam \
--ref_fn=reference.fasta \
--bed_fn=target_regions.bed \
--threads=32 \
--platform=ont \
--model_path=${CONDA_PREFIX}/bin/models/ont \
--output=clair3_targeted
# Call on non-human genomes (all contigs)
run_clair3.sh \
--bam_fn=sample.bam \
--ref_fn=reference.fasta \
--include_all_ctgs \
--threads=32 \
--platform=hifi \
--model_path=${CONDA_PREFIX}/bin/models/hifi \
--output=clair3_all_contigs
gVCF Output
# Generate gVCF for joint calling
run_clair3.sh \
--bam_fn=sample.bam \
--ref_fn=reference.fasta \
--gvcf \
--threads=32 \
--platform=ont \
--model_path=${CONDA_PREFIX}/bin/models/ont \
--output=clair3_gvcf
# Joint genotyping multiple samples
bcftools merge sample1.g.vcf.gz sample2.g.vcf.gz -o cohort.vcf.gz
Phased Variant Calling
# With phasing information (requires haplotagged BAM)
run_clair3.sh \
--bam_fn=haplotagged.bam \
--ref_fn=reference.fasta \
--enable_phasing \
--longphase_for_phasing \
--threads=32 \
--platform=ont \
--model_path=${CONDA_PREFIX}/bin/models/ont \
--output=clair3_phased
Quality Filtering
# Filter by quality score
bcftools view -i 'QUAL>20' clair3_output/merge_output.vcf.gz -Oz -o filtered.vcf.gz
# Filter by genotype quality
bcftools view -i 'GQ>30' clair3_output/merge_output.vcf.gz -Oz -o high_gq.vcf.gz
# SNPs only
bcftools view -v snps clair3_output/merge_output.vcf.gz -Oz -o snps.vcf.gz
# Indels only
bcftools view -v indels clair3_output/merge_output.vcf.gz -Oz -o indels.vcf.gz
Python Wrapper
import subprocess
from pathlib import Path
def run_clair3(bam, reference, output_dir, platform='ont', model_path=None,
threads=32, bed=None, gvcf=False, include_all_ctgs=False):
if model_path is None:
import os
conda_prefix = os.environ.get('CONDA_PREFIX', '')
model_path = f'{conda_prefix}/bin/models/{platform}'
cmd = [
'run_clair3.sh',
f'--bam_fn={bam}',
f'--ref_fn={reference}',
f'--threads={threads}',
f'--platform={platform}',
f'--model_path={model_path}',
f'--output={output_dir}'
]
if bed:
cmd.append(f'--bed_fn={bed}')
if gvcf:
cmd.append('--gvcf')
if include_all_ctgs:
cmd.append('--include_all_ctgs')
subprocess.run(cmd, check=True)
return Path(output_dir) / 'merge_output.vcf.gz'
def filter_variants(vcf, output, min_qual=20, variant_type=None):
cmd = ['bcftools', 'view', '-i', f'QUAL>{min_qual}']
if variant_type:
cmd.extend(['-v', variant_type])
cmd.extend([vcf, '-Oz', '-o', output])
subprocess.run(cmd, check=True)
subprocess.run(['bcftools', 'index', '-t', output], check=True)
return output
# Example
vcf = run_clair3('sample.bam', 'ref.fa', 'clair3_out', platform='hifi', threads=48)
snps = filter_variants(str(vcf), 'snps_q20.vcf.gz', min_qual=20, variant_type='snps')
Comparison with Other Callers
| Caller |
Best For |
Speed |
Accuracy |
| Clair3 |
ONT/HiFi germline |
Fast |
High |
| DeepVariant |
HiFi, Illumina |
Medium |
Very high |
| PEPPER-DV |
ONT (integrated) |
Slow |
Very high |
| Longshot |
ONT SNPs |
Fast |
Good |
Troubleshooting
| Issue |
Solution |
| Missing model |
Download from Clair3 releases or use conda models |
| Low call rate |
Check coverage; increase --qual threshold |
| Slow performance |
Reduce --threads or use --bed_fn for targeted calling |
| Wrong variants on non-human |
Use --include_all_ctgs |
Docker Usage
# Using Docker
docker run -v /data:/data \
hkubal/clair3:latest \
/opt/bin/run_clair3.sh \
--bam_fn=/data/sample.bam \
--ref_fn=/data/reference.fasta \
--threads=32 \
--platform=ont \
--model_path=/opt/models/ont \
--output=/data/clair3_output
# Singularity
singularity exec clair3.sif run_clair3.sh \
--bam_fn=sample.bam \
--ref_fn=reference.fasta \
--threads=32 \
--platform=ont \
--model_path=/opt/models/ont \
--output=clair3_output
Related Skills
- variant-calling/bcftools-basics - VCF manipulation
- variant-calling/filtering-best-practices - Quality filtering
- long-read-sequencing/long-read-qc - Input quality control
- long-read-sequencing/long-read-alignment - Mapping with minimap2