bio-long-read-sequencing-clair3-variants

name	bio-long-read-sequencing-clair3-variants
description	Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.
tool_type	cli
primary_tool	Clair3

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=):
    cmd = [, , , ]
     variant_type:
        cmd.extend([, variant_type])
    cmd.extend([vcf, , , output])
    subprocess.run(cmd, check=)
    subprocess.run([, , , output], check=)
     output


vcf = run_clair3(, , , platform=, threads=)
snps = filter_variants((vcf), , min_qual=, variant_type=)

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