Version Compatibility

Reference examples tested with: Clair3 2.0+, whatshap 2.0+, bcftools 1.19+, hap.py 0.3.15+.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: <tool> --version then <tool> --help to confirm flags

Results depend on inputs that outlive the binary version - record them:

• The Clair3 MODEL must match the platform + chemistry + basecaller tier + basecaller version (e.g. r1041e82400bpssupv500). There is NO auto-detection; --model_path is mandatory and a mismatch silently degrades calls.
• Clair3 v2 moved TensorFlow -> PyTorch; models are pileup.pt/full_alignment.pt. v1 TensorFlow models do NOT load in v2.
• The full ONT model set (every version, hac/fast, withmv signal-aware) lives in the rerio clair3_models/ repo; only a subset is bundled.

If code throws an error, introspect the installed tool (run_clair3.sh --help) and adapt the example to the actual API rather than retrying.

Clair3 Variant Calling

"Call variants from my long reads" -> Run Clair3 with the model that matches how the reads were basecalled, phase, and benchmark with stratification - because the model string, not the command, determines accuracy.

• CLI: runclair3.sh --bamfn=aln.bam --reffn=ref.fa --output=out/ --threads=16 --platform=ont --modelpath=/models/r1041e82400bpssupv500

Scope: germline diploid SNPs + small indels. NOT structural variants (-> structural-variants), NOT somatic/mosaic (-> ClairS/ClairS-TO), NOT RNA (-> Clair3-RNA).

The Single Most Important Modern Insight -- The Model String Is the Experiment, and ONT Indels Hide in the Strata

Clair3's accuracy is gated by two facts a naive user misses:

1. The model is hand-picked and a mismatch fails silently. There is no auto-detection - the user must point --modelpath at a specific model folder. Three axes must ALL match: chemistry (r941 vs r1041), basecaller tier (fast/hac/sup), and basecaller version (g5014/v430/v500/v520), plus the optional with_mv signal-aware axis if the BAM has Dorado mv tags. Wrong model = no crash, no warning, measurably worse calls (indels most). Derive the model from the basecaller string in the run metadata; pick the model version closest to but not above the basecaller version.
2. ONT indels in homopolymers/STRs are the residual error whole-genome F1 conceals. Even on R10.4.1 sup, insertions/deletions in homopolymer runs and short tandem repeats are the weak point (G/C homopolymers worst), because the pore cannot reliably count identical consecutive bases. A genome-wide indel F1 of ~99.5% hides much lower performance inside LowComplexity/homopolymer strata - exactly the medically relevant loci. HiFi largely solves this; do not transfer ONT-indel pessimism to HiFi. Always benchmark with GIAB stratification, never a single global number.

Two-Stage Architecture

Clair3 "symphonizes" two networks: a fast pileup model (summarized per-position statistics) that calls the large majority of sites, and a slow full-alignment model (haplotype-resolved read tensor) that re-evaluates only the uncertain subset. Internally Clair3 phases the top het-SNP pileup calls with WhatsHap, haplotags the BAM, and feeds the haplotagged reads to the full-alignment model - which is why read-based phasing buys ~6% indel F1, not cosmetics. Output: merge_output.vcf.gz (final).

Model Selection

Model name anatomy (r1041e82400bpssupv500): pore (r1041=R10.4.1), flowcell (e82), speed (400bps), basecaller tier (sup/hac/fast), basecaller version (v500=Dorado 5.0.0, g5014=Guppy 5.0.14). The withmv suffix uses Dorado move-table tags for best accuracy when present.

Decision Tree by Scenario

Core Commands

# Germline ONT calling (model MUST match the basecaller)
run_clair3.sh \
  --bam_fn=aln.bam --ref_fn=ref.fa --output=clair3_out/ \
  --threads=16 --platform=ont \
  --model_path=/opt/models/r1041_e82_400bps_sup_v500
# final VCF: clair3_out/merge_output.vcf.gz

# Phase the final output VCF (WhatsHap); --longphase_for_phasing swaps only the INTERNAL
# phaser to LongPhase (faster, SV-aware). For a LongPhase-phased final VCF use
# --use_longphase_for_final_output_phasing instead of --enable_phasing.
run_clair3.sh ... --enable_phasing --longphase_for_phasing
# Phased calls go to clair3_out/phased_merge_output.vcf.gz; merge_output.vcf.gz stays UNPHASED.

# Non-human / draft assembly reference - call ALL contigs
run_clair3.sh ... --include_all_ctgs

# Targeted / amplicon panel
run_clair3.sh ... --bed_fn=panel.bed --gvcf

# Benchmark against GIAB with stratification (the step that reveals ONT indel errors)
hap.py giab_truth.vcf.gz clair3_out/merge_output.vcf.gz \
  -f giab_confident.bed -r ref.fa --engine=vcfeval \
  --stratification giab_stratifications.tsv -o bench/hg002

Per-Method Failure Modes

Silent model mismatch

Trigger: --model_path pointing at a model that does not match the basecaller chemistry/tier/version. Mechanism: no auto-detection; the wrong network runs. Symptom: no error, lower F1 (indels most). Fix: derive the model from the basecaller string; verify the folder exists (rerio for the full set); for v2 ensure .pt models.

Clair3 found nothing on a non-human reference

Trigger: bacterial genome or draft assembly without chr1-22,X,Y names. Mechanism: Clair3 calls only standard human contigs by default. Symptom: near-empty VCF. Fix: --includeallctgs.

Global F1 looks great, clinical genes are wrong

Trigger: reporting only whole-genome F1. Mechanism: ONT indel errors concentrate in homopolymer/STR/low-complexity strata. Symptom: ~99.5% global indel F1 but much lower in LowComplexity. Fix: stratify with GIAB BEDs (Dwarshuis 2024); use CMRG for medically relevant genes.

Treating Clair3 as a somatic caller

Trigger: lowering --snpminaf/--indelminaf to catch low-VAF variants. Mechanism: germline model expects ~0.5/1.0 allele fractions, is not VAF-aware. Symptom: germline-model false positives at low AF, missed true somatic. Fix: ClairS (paired) / ClairS-TO (tumor-only).

v1 model with v2 Clair3

Trigger: an old TensorFlow model dir with Clair3 v2. Mechanism: v2 needs PyTorch .pt models. Symptom: model load failure. Fix: use pileup.pt/full_alignment.pt models (Converted Rerio).

Quantitative Thresholds

Common Errors

References

• Zheng Z, Li S, Su J, Leung AWS, Lam TW, Luo R. 2022. Symphonizing pileup and full-alignment for deep learning-based long-read variant calling (Clair3). Nat Comput Sci 2:797-803.
• Zheng Z, He M, Yu X, et al. 2026. Accelerated long-read variant calling with Clair3 for whole-genome sequencing. Bioinformatics (advance access) btag181.
• Kolesnikov A, Cook D, Nattestad M, et al. 2024. Local read haplotagging enables accurate long-read small variant calling. Nat Commun 15:5907.
• Dwarshuis N, Kalra D, McDaniel J, et al. 2024. The GIAB genomic stratifications resource for human reference genomes. Nat Commun 15:9029.
• Lin JH, Chen LC, Yu SC, Huang YT. 2022. LongPhase: an ultra-fast chromosome-scale phasing algorithm for small and large variants. Bioinformatics 38(7):1816-1822.
• Chen L, Zheng Z, Su J, et al. 2025. ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling. Nat Commun 16:9630.

Related Skills

• basecalling - The basecaller model+version the Clair3 model must match
• long-read-alignment - Produces the BAM (keep --MD; use minimap2 >=2.28)
• haplotype-phasing - whatshap/longphase phasing and haplotagging Clair3 uses internally
• medaka-polishing - ONT consensus; medaka diploid variant calling is deprecated in favor of Clair3
• structural-variants - SVs are out of Clair3's scope (Sniffles2/cuteSV)
• variant-calling/deepvariant - DeepVariant native ONT/HiFi models (neck-and-neck on R10)
• variant-calling/vcf-statistics - Summarize/filter the VCF Clair3 emits
• clinical-databases/variant-prioritization - Prioritize the called variants