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