
Bio Long Read Sequencing Clair3 Variants
- 3 installs
- 1.1k repo stars
- Updated July 25, 2026
- gptomics/bioskills
Call germline SNPs and small indels from ONT or PacBio alignments using the deep-learning caller Clair3.
About
Performs deep-learning-based germline variant calling from long-read alignments using Clair3. A developer uses it when calling SNPs and small indels from ONT or PacBio data where high accuracy is needed.
- Clair3 deep-learning variant calling
- Germline SNP and small indel detection from long reads
Bio Long Read Sequencing Clair3 Variants by the numbers
- 3 all-time installs (skills.sh)
- Ranked #1,661 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 3 |
|---|---|
| repo stars | ★ 1.1k |
| Last updated | July 25, 2026 |
| Repository | gptomics/bioskills ↗ |
What it does
Call germline SNPs and small indels from ONT or PacBio alignments using the deep-learning caller Clair3.
Files
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.gzPlatform-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=32Key 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_contigsgVCF Output
# Generate gVCF for joint calling
run_clair3.sh \
--bam_fn=sample.bam \
--ref_fn=reference.fasta \
--gvcf \
--threads=32 \
--platform=ont \
--model_path=${CONDA_PREFIX}/bin/models/ont \
--output=clair3_gvcf
# Joint genotyping multiple samples
bcftools merge sample1.g.vcf.gz sample2.g.vcf.gz -o cohort.vcf.gzPhased Variant Calling
# With phasing information (requires haplotagged BAM)
run_clair3.sh \
--bam_fn=haplotagged.bam \
--ref_fn=reference.fasta \
--enable_phasing \
--longphase_for_phasing \
--threads=32 \
--platform=ont \
--model_path=${CONDA_PREFIX}/bin/models/ont \
--output=clair3_phasedQuality 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.gzPython 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_outputRelated 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
Related skills
Data Science & MLpipelines