Version Compatibility

Reference examples tested with: Flye 2.9+, Canu 2.2+, NextDenovo 2.5+, Shasta 0.11+, Raven 1.8+, wtdbg2 2.5+, miniasm 0.3+, minimap2 2.26+, purgedups 1.2+, PorechopABI 0.5+, Trycycler 0.5+.

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

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

Two behaviors are version-dependent and load-bearing: Flye --genome-size became optional (auto-estimated) and --scaffold flipped OFF by default in recent releases - confirm against the installed Flye. Shasta --config names are dated and chemistry-specific (e.g. Nanopore-R10-Fast-Nov2022) - list with shasta --command listConfigurations rather than hard-coding one. Canu read-type flag spellings and correctedErrorRate defaults changed across major versions. If a command errors, introspect the installed tool and adapt rather than retrying.

Long-Read Assembly (Noisy Reads)

"Assemble a genome from Nanopore/PacBio-CLR long reads" -> Build a contiguous de novo assembly whose input mode matches the basecaller error regime, then hand off to polishing because the raw consensus is contiguous but not yet accurate.

• CLI: flye --nano-hq reads.fq.gz --out-dir out -t 16 (modern ONT R10/Dorado), canu -p asm -d out genomeSize=4.6m -nanopore reads.fq.gz (thorough), wtdbg2 -x ont -g 4.6m -i reads.fq.gz -fo asm && wtpoa-cns -i asm.ctg.lay.gz -fo asm.fa (fast draft)

The Single Most Important Modern Insight -- The Basecaller Era Picks the Flag, and the Assembly Is Only Half-Done Until Polished

Two load-bearing facts no assembler README states:

1. The basecaller era dictates the input flag, and a mismatch silently wrecks the assembly while the report looks finished. Noisy long reads are not one error regime: ONT R9.4.1+Guppy is ~5-10% error, ONT R10.4.1+Dorado SUP is Q20+ (~1-2%), PacBio CLR is ~10-15%. Each assembler has separate modes tuned to each (Flye --nano-raw / --nano-hq / --pacbio-raw). The dangerous direction is telling the assembler the reads are noisier than they are - feeding R10/Dorado data to --nano-raw makes Flye treat real repeat-copy and allele differences as noise and over-collapse them. The result is fewer contigs and a HIGHER N50 as the assembly gets worse - the most dangerous failure mode because the headline metric improved and nothing crashes. The chemistry/kit/basecaller model is an assembly parameter, not optional metadata; if it is unknown, the flag cannot be chosen and the assembly is uninterpretable - find out, do not guess.

1. The bottleneck flipped from contiguity to consensus accuracy. A raw noisy-long-read assembly emits a FASTA with a spectacular N50 that looks finished, but per-base accuracy is often Q20-Q30 (one error every ~100-1000 bp). The dominant ONT error is the indel, especially in homopolymers, and a single indel frameshifts a protein - so a contiguous assembly can have every gene model broken. Contiguity and correctness are orthogonal axes; N50 is blind to QV. The assembler is the halfway point: the deliverable is a polished assembly with a measured QV (Merqury), not a high-N50 FASTA. Flye does one internal polishing round and stops - that is not "polished." Hand off to assembly-polishing and assembly-qc.

Tool Taxonomy

PacBio CLR (--pacbio-raw) is legacy - superseded by HiFi for all new PacBio work; treat CLR support as maintenance for archival data, do not recommend generating new CLR.

Decision Tree by Scenario

Flye (the default)

flye --nano-hq reads.fq.gz --out-dir out -t 16                              # ONT R10 / Dorado SUP
flye --nano-hq reads.fq.gz --read-error 0.05 --out-dir out -t 16            # ONT R9 SUP
flye --nano-raw reads.fq.gz --out-dir out -t 16                             # legacy ONT R9 fast/HAC
flye --pacbio-raw reads.fq.gz --out-dir out -t 16                           # PacBio CLR (legacy)
flye --nano-hq reads.fq.gz --genome-size 3g --asm-coverage 40 -o out -t 32  # large genome: use longest 40x for initial assembly

--genome-size is optional in recent Flye (auto-estimated) but required when paired with --asm-coverage, which downsamples to the longest N-coverage of reads for the initial disjointig step (cuts runtime/RAM on deep large-genome data; the rest are still used). --iterations defaults to 1 polishing round (0 to skip); --keep-haplotypes retains alt bubble paths for diploid awareness; --meta is metaFlye for uneven-coverage communities (-> metagenome-assembly). Output: assembly.fasta, assemblyinfo.txt (per-contig length/coverage/circularity), assemblygraph.gfa.

Canu (thorough), wtdbg2 / Raven (fast), miniasm (layout only)

canu -p asm -d out genomeSize=4.6m -nanopore reads.fq.gz useGrid=false maxThreads=16   # correct->trim->assemble
wtdbg2 -x ont -g 4.6m -t 16 -i reads.fq.gz -fo asm && wtpoa-cns -t 16 -i asm.ctg.lay.gz -fo asm.ctg.fa  # consensus step is MANDATORY
raven -t 16 reads.fq.gz > asm.fasta                                                     # near parameter-free

Canu's master meta-parameter is correctedErrorRate (max expected difference between two corrected reads): defaults ~0.144 (Nanopore) / ~0.045 (PacBio); raise for heterozygosity/divergence, lower for clean high-coverage data. Read-type flags -nanopore / -pacbio / -pacbio-hifi (there is no -nanopore-hifi; high-accuracy ONT still uses -nanopore); useGrid=false forces a single machine. wtdbg2 needs the separate wtpoa-cns consensus call - the -x preset (ont/sq/rs/ccs) is set FIRST, and -L discards short reads (default 5000 for ont/sq). miniasm does no consensus at all - its output carries the full raw read error rate and is unusable until polished, so use it only as a fast layout inside a polished pipeline.

Haplotig False-Duplication and purge_dups

A diploid assembly from noisy reads either collapses heterozygous loci or emits both haplotypes as separate primary contigs (haplotig duplication). The diagnostic trifecta: (1) assembly size 1.5-2x the expected genome size; (2) a bimodal read-depth histogram with a half-coverage peak (haplotigs split reads between two copies); (3) inflated BUSCO-Duplicated. Fix with purge_dups (read-depth + self-alignment):

minimap2 -xmap-ont asm.fa reads.fq.gz | gzip > aln.paf.gz
pbcstat aln.paf.gz && calcuts PB.stat > cutoffs          # INSPECT the coverage histogram before trusting auto-cuts
split_fa asm.fa > asm.split && minimap2 -xasm5 -DP asm.split asm.split | gzip > self.paf.gz
purge_dups -2 -T cutoffs -c PB.base.cov self.paf.gz > dups.bed
get_seqs -e dups.bed asm.fa                              # -> purged.fa (primary) + hap.fa (haplotigs)

purge_dups cannot tell a haplotig from a real recent segmental duplication/paralog - both look like similar sequence at fractional depth. On a genome with known recent WGD or high SD content (many plants), over-purging deletes real genes; eyeball the histogram and validate against a related assembly. True haplotype-resolved assembly is a HiFi capability - do not promise phasing from noisy reads (-> hifi-assembly).

Pre-Assembly: Mid-Read Adapter De-Chimerization

porechop_abi -abi -i reads.fq.gz -o trimmed.fq.gz -t 16   # ab initio adapter detection; SPLITS internal-adapter chimeras

ONT occasionally sequences two molecules as one read with an internal adapter - an untrimmed chimera becomes a structural mis-join (a layout error polishing cannot fix). Porechop_ABI detects adapters ab initio (no fixed DB, which matters because kit adapter sequences change) and splits chimeric reads. Use it, not the original Porechop (unmaintained since 2018, frozen adapter DB). PacBio CLR handles adapter/scrap removal upstream on the instrument, so this is an ONT-specific concern.

Per-Method Failure Modes

Flag noisier than reads actually are

Trigger: --nano-raw (or low-error-rate omission) on R10/Dorado-SUP data. Mechanism: assembler treats real repeat-copy and allele differences as noise and over-collapses. Symptom: fewer contigs, HIGHER N50, lost repeats/SVs - looks better. Fix: match the flag to the basecaller era (--nano-hq for R10); record pore+kit+model before assembling.

"It assembled, so it's done"

Trigger: shipping the raw assembler FASTA on its high N50. Mechanism: raw consensus is Q20-Q30; indels frameshift genes. Symptom: broken gene models, failed variant calling, BLAST misses present genes. Fix: polish (long-read Medaka/Racon, model-matched), then measure QV with Merqury; an assembly without a stated QV is a draft.

Trusting polishing to fix a structural error

Trigger: expecting polish to repair a mis-resolved repeat, inversion, collapsed tandem array, or dropped plasmid. Mechanism: polishers correct per-base consensus (QV) only; they never change contig layout. Symptom: a Q50 assembly that is still structurally wrong. Fix: catch layout errors with read-back coverage uniformity, multi-assembler consensus (Trycycler/Autocycler), or Hi-C - not QV.

Haplotig duplication read as completeness

Trigger: "my genome is bigger than expected - more complete!" Mechanism: both haplotypes kept as primary contigs. Symptom: size 1.5-2x expected, half-coverage depth peak, inflated BUSCO-Duplicated. Fix: purge_dups (inspect cutoffs); do not over-purge real SDs.

High coverage from a short-N50 library

Trigger: 200x of reads with a 5 kb N50, expecting contiguity. Mechanism: reads physically cannot span long repeats; depth buys consensus, not spanning. Symptom: fragmented assembly that more depth never fixes. Fix: coverage and read-N50 are non-substitutable; spend effort on read length (extraction, size selection) or ultra-long ONT.

Over-filtering by length

Trigger: aggressive Filtlong/chopper length filter to "keep the best reads." Mechanism: the longest reads are often not the highest quality; the filter discards the long-but-lower-Q reads that span hard repeats. Symptom: lost contiguity. Fix: filter conservatively; spanning reads are precious.

Quantitative Thresholds

Common Errors

References

• Kolmogorov M, Yuan J, Lin Y, Pevzner PA. 2019. Assembly of long, error-prone reads using repeat graphs (Flye). Nat Biotechnol 37:540-546.
• Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. 2017. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 27:722-736.
• Hu J, Wang Z, Sun Z, et al. 2024. NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads. Genome Biol 25:107.
• Shafin K, Pesout T, Lorig-Roach R, et al. 2020. Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes. Nat Biotechnol 38:1044-1053.
• Vaser R, Sikic M. 2021. Time- and memory-efficient genome assembly with Raven. Nat Comput Sci 1:332-336.
• Ruan J, Li H. 2020. Fast and accurate long-read assembly with wtdbg2. Nat Methods 17:155-158.
• Li H. 2016. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics 32:2103-2110.
• Guan D, McCarthy SA, Wood J, Howe K, Wang Y, Durbin R. 2020. Identifying and removing haplotypic duplication in primary genome assemblies (purge_dups). Bioinformatics 36:2896-2898.
• Wick RR, Judd LM, Cerdeira LT, et al. 2021. Trycycler: consensus long-read assemblies for bacterial genomes. Genome Biol 22:266.
• Rhie A, Walenz BP, Koren S, Phillippy AM. 2020. Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biol 21:245.

Related Skills

• genome-profiling - k-mer genome-size estimate feeds Flye --genome-size and the haplotig sanity check
• hifi-assembly - PacBio HiFi (Q30+) phased haplotype-resolved assembly with hifiasm; the right tool for accurate reads
• assembly-polishing - Polishes the contiguous-but-low-QV contigs this skill produces; assembly is not finished without it
• assembly-qc - QUAST/BUSCO/Merqury; QV is the deliverable, N50 alone is a vanity metric
• long-read-sequencing/long-read-qc - Read length/quality QC and conservative filtering before assembly
• long-read-sequencing/basecalling - Basecaller era and model that determine the assembler input flag
• read-qc/contamination-screening - Screen reads for host/vector contamination before assembling
• workflows/genome-assembly-pipeline - End-to-end QC -> assemble -> polish -> scaffold -> QC