Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG_5hmCG, 6mA, m6A) at basecall time, and handling duplex, demultiplexing, trimming, and HERRO read correction. Covers why the model+version is an irreversible analysis decision, why methylation cannot be recovered later, and why downstream polish/variant models must match the basecaller. Use when converting POD5/FAST5 to reads, picking a Dorado model for R9/R10 or RNA004, enabling methylation calling, basecalling duplex, demultiplexing barcoded runs, or correcting reads for assembly.
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Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG_5hmCG, 6mA, m6A) at basecall time, and handling duplex, demultiplexing, trimming, and HERRO read correction. Covers why the model+version is an irreversible analysis decision, why methylation cannot be recovered later, and why downstream polish/variant models must match the basecaller. Use when converting POD5/FAST5 to reads, picking a Dorado model for R9/R10 or RNA004, enabling methylation calling, basecalling duplex, demultiplexing barcoded runs, or correcting reads for assembly.
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 basecaller MODEL string (e.g. dna_r10.4.1_e8.2_400bps_sup@v5.2.0) sets the entire error profile and must be propagated to every downstream tool. Pin it.
Modified-base models carry a SECOND version (..._sup@v5.0.0_5mCG_5hmCG@v3); the mod version can lag the simplex version - check dorado download --list.
R9.4.1 and RNA002 models were removed from Dorado v1.0 defaults; legacy data needs an archived model path.
If code throws an error, introspect the installed tool (dorado --help, dorado basecaller --help) and adapt the example to the actual API rather than retrying.
Nanopore Basecalling
"Basecall my Nanopore data" -> Convert raw signal (POD5) into reads with Dorado using the chemistry-matched model, deciding the accuracy tier and whether to call modifications now - because the model choice is baked irreversibly into the output.
PacBio note: PacBio "basecalling" (CCS -> HiFi reads) runs on-instrument/in SMRT Link; users receive HiFi BAMs already at Q20-Q30+. This skill is Oxford Nanopore / Dorado. HiFi assembly lives in genome-assembly/hifi-assembly.
The Single Most Important Modern Insight -- There Is No "The Reads," Only "The Reads As Called By This Model"
Basecalling is not fixed preprocessing that yields a neutral FASTQ. The model and version chosen are an analysis decision written permanently into the BAM, with three consequences a naive user misses:
Methylation is a basecalling decision, not a later analysis step. Modified bases are inferred from raw signal at basecall time by Remora models and emitted as MM/ML tags. A plain BAM/FASTQ with no MM/ML tags has thrown the signal away - mods CANNOT be recovered without re-basecalling from POD5. If methylation might ever matter, request it now (sup,5mCG_5hmCG) and KEEP the POD5. See nanopore-methylation.
Downstream polish/variant models must match the basecaller model+version. medaka and Clair3 ship per-model weights (Clair3 r1041_e82_400bps_sup_v500; medaka the dotted r1041_e82_400bps_sup_v5.2.0). A mismatched model silently degrades accuracy with no error. Propagate the basecaller model name to every downstream step.
Mixing model versions across a cohort is a batch effect. Different model versions have different identity and homopolymer-indel error profiles. Re-basecall the WHOLE cohort with ONE current model before joint or differential analysis.
Dorado Subcommand Taxonomy
Dorado (one GPU-first executable) replaced Guppy, which is end-of-life. Bonito is ONT's research/training basecaller (not production); Rerio hosts research-release models (niche mods, bacterial methylation).
Format {analyte}_{pore}_{chemistry}_{speed}@v{ver} + optional mod suffix, e.g. dna_r10.4.1_e8.2_400bps_sup@v5.2.0_5mCG_5hmCG@v3.
Token
Meaning
Examples
analyte
molecule
dna, rna004
pore
flow-cell generation
r10.4.1 (current), r9.4.1 (legacy)
chemistry
kit chemistry
e8.2 (Kit 14)
speed
translocation speed -> sampling rate
400bps (5 kHz DNA), 130bps (RNA004, 4 kHz)
tier
model size/accuracy
fast, hac, sup
version
model version
@v4.3.0, @v5.2.0, @v6.0.0
Passing the bare tier (sup) lets Dorado auto-detect chemistry from POD5 metadata and fetch the matching latest model; pin a version (sup@v5.2.0) or a full path for reproducibility. Append mods comma-separated (sup,5mCG_5hmCG,6mA); only one mod model per canonical base may be active.
Decision Tree by Scenario
Scenario
Recommended
Why
Any analysis (variant/assembly/methylation)
sup + matched model, pinned version
fast/hac error profile leaks into calls
Live run / adaptive sampling / quick QC only
fast
speed; never for downstream analysis
Routine work, compute-limited
hac
strong accuracy/compute balance (v5.2 closed much of the gap to sup)
Methylation wanted now or maybe later
sup,5mCG_5hmCG (DNA), keep POD5
mods are unrecoverable from a plain BAM -> nanopore-methylation
Trigger: basecalling without a mod model, then wanting 5mC later. Mechanism: Remora infers mods from raw signal at basecall time; a plain BAM has only bases. Symptom: no MM/ML tags; modkit pileup returns nothing. Fix: re-basecall from POD5 with sup,5mCG_5hmCG; keep POD5 archives.
Barcodes land in unclassified
Trigger: default --trim all basecall, then a separate dorado demux. Mechanism: trimming removes the barcode before demux can read it. Symptom: most reads in unclassified.bam, low classification rate. Fix: basecall --no-trim, then demux (it trims barcodes itself).
Silent accuracy loss downstream
Trigger: polishing/calling with a medaka/Clair3 model that doesn't match the basecaller model+version. Mechanism: per-model neural weights expect a specific error profile. Symptom: no error, just quietly worse consensus/calls. Fix: propagate the basecaller model name; use medaka tools resolve_model --auto_model; pick the matching Clair3 model dir.
Duplex double-counting
Trigger: treating every read in a duplex BAM as an independent molecule. Mechanism: a simplex parent and its duplex offspring both appear. Symptom: inflated coverage/allele counts. Fix: the dx:i:-1 tag marks simplex parents of duplex reads - filter them when counting molecules (dx:i:1 = duplex, dx:i:0 = simplex-only).
Cohort batch effect
Trigger: runs basecalled with different model versions joined for analysis. Mechanism: version-specific identity/indel error profiles confound a technical batch with biology. Symptom: spurious between-run differences. Fix: re-basecall the whole cohort with one model version.
dual-reader head fixes homopolymers; enables nanopore-only near-finished genomes
Duplex read ~Q30; yield typically <10% of reads
community benchmarks
duplex is library-prep/loading-limited, not free accuracy
A "Q20" base errs at ~Q12.5 empirically
Delahaye 2021
nanopore qscores >Q10 are overconfident posteriors; use for relative filtering only
HERRO input reads >=10 kbp, HAC/SUP R10
Dorado correct docs
HERRO operates on 4096-bp chunks; shorter reads dropped
--min-qscore 10 as a permissive QC floor
convention
Q10 ~ 90% nominal; a starting filter, not a hard rule
Common Errors
Error / symptom
Cause
Solution
"Failed to determine sequencing chemistry from data"
R9/RNA002 or non-standard kit; bare tier can't auto-resolve
pass an explicit model path; for legacy chemistry use an archived model
No MM/ML tags in BAM
basecalled without a mod model
re-basecall from POD5 with sup,5mCG_5hmCG
Most reads unclassified after demux
trimmed before demux
basecall --no-trim, then demux
--model sup errors
model is the positional arg, not a flag
dorado basecaller sup pod5s/
dorado correct reads.bam fails
input is FASTQ(.gz), output FASTA
dorado correct reads.fastq > corrected.fasta
Out of GPU memory
batch too large for VRAM (sup is heaviest)
lower --batchsize; or drop to hac
cDNA m6A calling returns nothing
PCR erased native modifications
use direct RNA (RNA004), not cDNA
References
Sereika M, Kirkegaard RH, Karst SM, et al. 2022. Oxford Nanopore R10.4 long-read sequencing enables the generation of near-finished bacterial genomes from pure cultures and metagenomes without short-read or reference polishing. Nat Methods 19:823-826.
Stanojević D, Lin D, Nurk S, Florez de Sessions P, Šikić M. 2026. Telomere-to-telomere assembly using HERRO-corrected Nanopore simplex reads. Nature (online ahead of print). DOI 10.1038/s41586-026-10563-y.
Wick RR, Judd LM, Holt KE. 2019. Performance of neural network basecalling tools for Oxford Nanopore sequencing. Genome Biol 20:129.
Pagès-Gallego M, de Ridder J. 2023. Comprehensive benchmark and architectural analysis of deep learning models for nanopore sequencing basecalling. Genome Biol 24:71.
Delahaye C, Nicolas J. 2021. Sequencing DNA with nanopores: troubles and biases. PLoS ONE 16(10):e0257521.
Gamaarachchi H, Samarakoon H, et al. 2025. The enduring advantages of the SLOW5 file format for raw nanopore sequencing data. GigaScience giaf118.
Related Skills
long-read-qc - Assess read length/quality and run health after basecalling
nanopore-methylation - Pile up the MM/ML tags this skill must request at basecall time
long-read-alignment - Map the reads; use -y to carry MM/ML tags through alignment
medaka-polishing - Consensus model that must match this basecaller model+version
clair3-variants - Variant model that must match this basecaller model+version
genome-assembly/long-read-assembly - Assemble the reads (HERRO-corrected for diploid/T2T)
genome-assembly/hifi-assembly - PacBio HiFi (basecalled on-instrument, not here)
epitranscriptomics/m6anet-analysis - ONT direct-RNA m6A from signal
