Polishes Oxford Nanopore draft assemblies to higher consensus accuracy with medaka, a basecaller-model-specific neural consensus net, produces haploid variant calls (VCF) for microbial, mitochondrial, or viral samples, and generates amplicon/viral consensus sequences. Covers the model-matching footgun that silently degrades output, why Racon-first is obsolete and medaka runs directly on Flye output as a single pass, why HiFi must never be fed to medaka, the v1->v2 subcommand renames, and the precise medaka_variant deprecation. Use when polishing an ONT-only assembly, generating an amplicon/viral consensus, calling a haploid ONT consensus, or deciding whether medaka, dorado polish, or Clair3 is the right tool.
Polishes Oxford Nanopore draft assemblies to higher consensus accuracy with medaka, a basecaller-model-specific neural consensus net, produces haploid variant calls (VCF) for microbial, mitochondrial, or viral samples, and generates amplicon/viral consensus sequences. Covers the model-matching footgun that silently degrades output, why Racon-first is obsolete and medaka runs directly on Flye output as a single pass, why HiFi must never be fed to medaka, the v1->v2 subcommand renames, and the precise medaka_variant deprecation. Use when polishing an ONT-only assembly, generating an amplicon/viral consensus, calling a haploid ONT consensus, or deciding whether medaka, dorado polish, or Clair3 is the right tool.
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 medaka MODEL must match the basecaller (pore + chemistry + speed + mode + version), e.g. r1041_e82_400bps_sup_v5.2.0. A mismatch silently degrades output. Prefer auto-detection from the BAM; verify with medaka tools list_models.
Default models advance with each release (consensus , variant at time of writing); confirm with .
..._sup_v5.2.0
..._sup_variant_v5.0.0
medaka tools list_models
medaka v2 renamed subcommands (consensus->inference, stitch->sequence, variant->vcf) and moved the backend to PyTorch; v1 tutorials fail.
If code throws an error, introspect the installed tool (medaka --help, medaka_consensus --help) and adapt the example to the actual API rather than retrying.
Medaka Polishing
"Polish my Nanopore assembly" -> Run one medaka consensus pass directly on the assembler output, with the model that matches the basecaller - because a mismatched model silently makes the consensus worse.
CLI: medaka_consensus -i reads.fq -d draft.fa -o out/ -t 8 (model auto-detected from the basecaller annotation)
medaka is an Oxford Nanopore tool. For PacBio (HiFi/CLR) it is the wrong tool entirely - route to genome-assembly/assembly-polishing.
The Single Most Important Modern Insight -- A Mismatched Model Silently Degrades; HiFi Must Never Be Fed to Medaka; Prove It on Held-Out Data
medaka is a basecaller-model-specific neural consensus net trained on one exact stack (pore + motor enzyme + speed + basecaller mode + basecaller version). Three consequences:
The model must match the basecaller, and a mismatch fails silently. Fed reads from a different stack, medaka applies corrections calibrated for an error fingerprint that is not there and misses the real one - the consensus gets WORSE, but medaka exits 0, writes a FASTA, and prints no warning. This is the #1 ONT-polishing footgun, sprung by ordinary acts (re-basecalling with newer Dorado, copying a 2020 model name, polishing a public assembly with the default). Prefer auto-detection (medaka tools resolve_model --auto_model consensus reads.bam); treat a stale model name as a reason to re-basecall, not to proceed.
HiFi (and CLR) must never be fed to medaka. It has no PacBio models; an ONT error-model net "corrects" HiFi toward errors HiFi does not make, and HiFi is already QV40+. If the reads are PacBio, medaka is simply wrong -> genome-assembly/assembly-polishing.
Success is only real on held-out data. medaka maximizes agreement between the consensus and its input pileup, so grading it on those same reads is circular and always looks good. medaka's "N changes" is a risk signal, not a success signal. Measure with reference-free Merqury QV before vs after on held-out / different-platform k-mers (design deferred to genome-assembly/assembly-polishing).
What medaka Is For (three modes, same model rule)
Mode
Input
medaka's role
Assembly polishing
Flye/Canu draft + ONT reads
raise per-base QV (homopolymer-indel cleanup is the dominant win)
Haploid variant calling
ONT reads + reference (microbial, mito, viral)
medaka_variant wrapper -> haploid VCF (apply with bcftools consensus for a FASTA)
Amplicon / viral consensus
tiling-amplicon ONT reads
the non-signal consensus arm of ARTIC fieldbioinformatics / EPI2ME wf-artic
Decision Tree by Scenario
Scenario
Recommended
Why
ONT-only Flye/Canu assembly
medaka_consensus, ONE pass, auto-detected model
model-matched consensus; racon pre-step is obsolete
Native bacterial isolate (modified DNA)
medaka_consensus --bacteria
bacterial-methylation model fixes methylation-motif errors
ONT small-variant (diploid/germline) calling
-> clair3-variants
medaka diploid calling deprecated in v2 (Clair3 surpassed it)
Haploid microbial/mito/viral VCF
medaka_variant (the renamed haploid wrapper)
still supported in v2
Read-level / human polishing
dorado polish
ONT's emerging successor; identical bacterial weights to medaka today
PacBio HiFi/CLR
-> genome-assembly/assembly-polishing
medaka has no PacBio models; never ONT-polish HiFi
Unsure which basecaller model produced the reads
re-basecall, then auto-detect
a guessed model silently degrades the consensus
medaka_consensus Mechanics
The wrapper runs three steps: align (mini_align, a thin veil over minimap2 -x map-ont), infer (medaka inference, the neural net over the pileup), and stitch (medaka sequence, regions -> consensus FASTA).
# Canonical modern usage - model auto-detected from the basecaller annotation in the reads
medaka_consensus -i reads.fastq -d draft.fa -o medaka_out/ -t 8
# medaka_out/consensus.fasta is the polished assembly# Native bacterial isolate: use the methylation-aware bacterial model
medaka_consensus -i reads.fastq -d draft.fa -o medaka_out/ -t 8 --bacteria
# Resolve / list models (do this when auto-detection cannot pick)
medaka tools resolve_model --auto_model consensus reads.bam
medaka tools list_models
medaka runs directly on the assembler (Flye) output as a SINGLE pass - do NOT pre-run Racon (contemporary models are trained on raw assembler output; v2 removed the bundled racon wrapper) and do NOT run medaka twice (iteration was racon's role; a second pass risks flipping correct bases).
Haploid variant calling (v2 names)
medaka_variant emits a VCF only (no consensus FASTA); apply it to the reference with bcftools consensus to get a haploid consensus sequence.
# Wrapper form (renamed from medaka_haploid_variant in v2) - haploid samples only
medaka_variant -i reads.fastq -r reference.fa -o variant_out/
# Manual form - note v2 subcommand names and the hdf -> ref -> out argument order
minimap2 -ax map-ont reference.fa reads.fq | samtools sort -o aln.bam && samtools index aln.bam
medaka inference aln.bam probs.hdf --model r1041_e82_400bps_sup_variant_v5.0.0
medaka vcf probs.hdf reference.fa variants.vcf
# Optional: turn the VCF into a haploid consensus FASTA
bgzip variants.vcf && tabix -p vcf variants.vcf.gz
bcftools consensus -f reference.fa variants.vcf.gz > consensus.fasta
Per-Method Failure Modes
Silent model mismatch
Trigger: running medaka with a model that does not match the basecaller chemistry/version. Mechanism: the net corrects toward the wrong error fingerprint. Symptom: lower held-out QV; medaka exits 0 with no warning. Fix: auto-detect from the BAM; if forced to pick, derive from the actual basecaller and confirm in list_models; treat a stale name as a reason to re-basecall.
HiFi fed to medaka
Trigger: polishing a PacBio assembly with medaka. Mechanism: ONT-only error model, no PacBio support, on already-QV40+ data. Symptom: degraded/homogenized consensus. Fix: do not; route to genome-assembly/assembly-polishing.
Racon-first off-distribution
Trigger: running Racon before medaka out of habit. Mechanism: contemporary models are trained on raw assembler output; racon-polished input is off the training distribution. Symptom: no gain or mild harm. Fix: run medaka directly on the Flye output; one pass.
Missing plasmid poisons the chromosome
Trigger: an assembly missing a small replicon (~80% identical to a chromosomal region). Mechanism: the absent plasmid's reads misalign onto the chromosome, and medaka "corrects" toward that spurious evidence. Symptom: clustered changes that introduce real errors. Fix: make the assembly structurally complete first; inspect medaka's changes for clustering (clustered = mapping artifact, not scattered homopolymer fixes).
Validating on the polishing reads
Trigger: judging the polish by medaka's change count or by re-mapping the same reads. Mechanism: medaka optimizes agreement with its input pileup. Symptom: "improvement" that is circular. Fix: reference-free Merqury QV before vs after on held-out / different-platform k-mers.
Quantitative Thresholds
Threshold
Source
Rationale
1 medaka pass
medaka README
a single trained-model pass; iteration was racon's role, extra passes flip correct bases
model must match basecaller version
medaka model design
mismatch silently degrades; the #1 ONT-polishing error
inference threads ~2
medaka inference behavior
the net is GPU-bound and scales poorly past ~2 CPU threads
HiFi QV40+ already
EBP/HiFi baseline
nothing for an ONT consensus net to gain; only harm
measure with held-out Merqury QV
Rhie 2020
the only honest, reference-free before/after instrument
Common Errors
Error / symptom
Cause
Solution
medaka consensus not found / wrong args
v1 subcommand renamed
use medaka inference (or the medaka_consensus wrapper)
medaka stitch / medaka variant fail
v1 names
medaka sequence / medaka vcf
Polished assembly worse than draft
model mismatch
auto-detect the model; re-basecall if the model is stale
medaka errors on PacBio reads
no PacBio models
route to genome-assembly/assembly-polishing
Clustered, suspicious changes
missing/mis-structured contig in the draft
complete the assembly first; filter to high-identity alignments
Zheng Z, Li S, Su J, Leung AW, 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.
Vaser R, Sović I, Nagarajan N, Šikić M. 2017. Fast and accurate de novo genome assembly from long uncorrected reads (Racon). Genome Res 27:737-746.
Wick RR, Judd LM, Holt KE. 2023. Assembling the perfect bacterial genome using Oxford Nanopore and Illumina sequencing. PLoS Comput Biol 19(3):e1010905.
Rhie A, Walenz BP, Koren S, Phillippy AM. 2020. Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biol 21:245.
