Version Compatibility

Reference examples tested with: BUSCO 5.5+, OMArk 0.3+, CheckM2 1.0+, compleasm 0.2.6+, gffutils 0.12+, matplotlib 3.8+.

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

• CLI: <tool> --version then <tool> --help to confirm flags
• Python: pip show <package> then help(module.function) to check signatures

BUSCO results depend on the lineage dataset (e.g. vertebrata_odb10 vs the shallow eukaryota_odb10) and the OrthoDB version - record them; a 99% on a shallow ~255-gene set and a 99% on a deep ~5,500-gene clade set are very different claims. If code throws an error, introspect the installed tool and adapt rather than retrying.

Annotation QC

"Is my genome annotation any good?" -> Measure conserved-gene completeness, proteome consistency/contamination, and gene-set sanity, and decide whether the limiting factor is the assembly or the predictor.

• CLI: busco -i proteins.faa -m proteins -l <lineage>_odb10 (proteome) and busco -i genome.fa -m genome -l <lineage>_odb10 (assembly), omark, checkm2 predict (prokaryotes)

The Single Most Important Modern Insight -- BUSCO Measures the Easy 10%; the Diagnostic Is Genome-vs-Proteome

BUSCO completeness measures recovery of the ~1,000-5,500 most conserved single-copy orthologs - the most conserved, highly expressed, intron-stable genes any half-broken pipeline will find. A 98%-complete BUSCO confirms the housekeeping core is intact; it says nothing about whether the other ~20,000 models are chimeric, fragmented, frame-shifted, fused, or hallucinated from TEs. BUSCO is a smoke detector in one room of a burning house. Three load-bearing moves:

1. Run assembly-BUSCO vs proteome-BUSCO on the same assembly - the diagnostic almost nobody runs. -m genome does its own gene-finding; -m proteins scores the delivered proteome. If assembly is 98% and proteome is 85%, the genes are physically present and the predictor missed them - a training/evidence/masking problem, fixable without touching the assembly. If both are low, the genes aren't in the assembly - stop annotating and fix the assembly. This single fork redirects more wasted effort than any other check. (Always run BUSCO on the delivered proteome, -m proteins, not genome mode reported as if it described the annotation.)
2. Gene count is a vanity metric. The same assembly annotated by two pipelines routinely differs 20-40% in gene count, and a higher count is as likely to mean spurious TE ORFs and split models as more real genes. The diagnostic signal is in the mono-exonic fraction, protein-length distribution, mean exons/gene, and mRNA:gene ratio - not the headline count.
3. Complement BUSCO with OMArk. BUSCO's single-copy-ortholog lens is blind to over-prediction, chimeras, and contamination; OMArk assesses the whole proteome for completeness AND consistency (are genes consistent with their homologs?) and detects contaminant species. Use both.

Tool Taxonomy

Tool	Citation	Measures	Domain
BUSCO	Manni 2021 Mol Biol Evol	conserved single-copy ortholog recovery (C/D/F/M)	euk + prok + viral; proteome/genome/transcriptome modes
OMArk	Nevers 2025 Nat Biotechnol	proteome completeness + consistency + contamination	eukaryotic proteomes; catches over-prediction BUSCO misses
compleasm	Huang 2023	faster miniprot-based BUSCO-compatible completeness	euk; genome mode
CheckM2	Chklovski 2023 Nat Methods	completeness + contamination (ML, lineage-agnostic)	bacteria/archaea, isolates + MAGs
Gene-set sanity panel	(gffutils)	gene count, mono-exonic %, protein length, mRNA:gene, coding density	any; the panel BUSCO can't provide
LAI	Ou 2018 NAR	LTR-RT assembly resolution (repeat-space contiguity)	LTR-rich genomes; assembly-side QC

Decision Tree by Scenario

Scenario	QC to run	Why
Prokaryotic genome/MAG	CheckM2 (completeness/contamination) + coding density + tRNA/rRNA counts	CheckM2 is the field-standard completeness call; BUSCO is conservative on prokaryotes
Eukaryotic annotation	BUSCO -m proteins AND -m genome + OMArk + gene-set sanity panel	the genome-vs-proteome fork + over-prediction/contamination
Suspect high gene count	mono-exonic fraction + protein-length + BUSCO-Duplicated	distinguish haplotigs / unmasked TEs / split models
High BUSCO-Duplicated	check synteny + clade ploidy	uncollapsed haplotigs (purge) vs real WGD (keep)
Transferred annotation	BUSCO on the lifted set vs reference + ORF integrity	quantify silently lost conserved genes
Low proteome-BUSCO, high genome-BUSCO	fix evidence/masking/training	the genes are present; the predictor missed them
Both BUSCO low	-> genome-assembly/assembly-qc (purge_dups, more data)	the limit is the assembly

BUSCO and How to Read It

busco -i proteins.faa -m proteins -l vertebrata_odb10 -o busco_prot -c 16   # the delivered proteome
busco -i genome.fa    -m genome   -l vertebrata_odb10 -o busco_genome -c 16 # the assembly

Reported as C:[S,D],F,M: Complete (full-length match), Duplicated (subset of Complete, found ≥2x), Fragmented (partial), Missing. Use the deepest applicable clade dataset, not the shallow eukaryota_odb10 (a 99% on a 255-gene set is trivially easy and not comparable to a 99% on a 5,500-gene clade set). High Duplicated is the most-misread signal - three causes, opposite responses: uncollapsed haplotigs (genome-wide, heterozygosity-scaled, non-syntenic -> purge_dups before annotating), real recent WGD (syntenic, ploidy-consistent, Ks duplication peak -> keep), or split models from a fragmented assembly. A clean haploid annotation runs ~1-3% Duplicated; >5-8% with no known WGD screams purge_dups first. compleasm is a faster miniprot-based alternative that often reports higher completeness than BUSCO's metaeuk path.

OMArk (Proteome Consistency and Contamination)

omamer search --db LUCA.h5 --query proteins.faa --out proteins.omamer
omark -f proteins.omamer -d LUCA.h5 -o omark_out

OMArk catches what BUSCO's single-copy lens misses: it classifies the whole proteome as consistent / inconsistent (a gene whose structure conflicts with its homologs - a chimera or fragment) / unknown, and detects contaminant species mixed into the proteome. A high "inconsistent" fraction flags over-prediction or fused/split models even when BUSCO is green.

CheckM2 (Prokaryotic Completeness/Contamination)

checkm2 predict --input genome.fna --output-directory checkm2_out --threads 16

For bacteria/archaea, CheckM2's lineage-agnostic ML model is the standard completeness/contamination call (handles reduced/novel lineages where marker sets fail). Gate annotation on it: contamination >5% mixes two organisms' genes into a chimeric set; completeness <90% with contamination >5-10% makes gene count, coding density, and hypothetical fraction uninterpretable - fix the assembly/binning first.

Gene-Set Sanity Panel with Python

Goal: Compute the triage panel that reveals annotation health where gene count and BUSCO cannot.

Approach: Load the GFF3 into gffutils; compute the mRNA:gene ratio (1.00 = isoform/UTR-naive), the mono-exonic fraction, and the protein-length distribution; flag clade-anomalous values.

import gffutils

MONOEXONIC_FLAG = 0.30   # >30% single-exon in a vertebrate suggests unmasked TEs/pseudogenes/fragments (calibrate per clade; fungi run higher)

def sanity_panel(gff_file):
    db = gffutils.create_db(gff_file, ':memory:', merge_strategy='merge')
    genes = list(db.features_of_type('gene'))
    mrnas = list(db.features_of_type(['mRNA', 'transcript']))
    exon_counts = [len(list(db.children(tx, featuretype='exon'))) for tx in mrnas]
    mono_frac = sum(1 for e in exon_counts if e == 1) / len(exon_counts) if exon_counts else 0
    mrna_per_gene = len(mrnas) / len(genes) if genes else 0
    if mrna_per_gene <= 1.001:
        print('WARNING: mRNA:gene == 1.00 -- isoform/UTR-naive; AS/3-prime-tag scRNA-seq analyses untrustworthy')
    if mono_frac > MONOEXONIC_FLAG:
        print(f'WARNING: mono-exonic fraction {mono_frac:.1%} high -- check masking/contamination')
    return {'genes': len(genes), 'mrna_per_gene': mrna_per_gene, 'mono_exonic_fraction': mono_frac}

Read gene count against the nearest well-annotated relative and the species' ploidy, never in isolation (1.5-2x with no WGD = haplotigs/over-prediction; ~0.5x = over-masking/under-training). A healthy protein-length distribution is unimodal near the clade-typical ~300-450 aa; a sub-100-aa spike = spurious/fragmented calls; a fat left tail = partials from a fragmented assembly.

Per-Method Failure Modes

Reporting genome-mode BUSCO as the annotation's score

Trigger: running BUSCO -m genome and citing it as annotation quality. Mechanism: genome mode does its own gene-finding, often better than the author's pipeline on the conserved core. Symptom: reported BUSCO higher than the real proteome BUSCO. Fix: run -m proteins on the delivered proteome; report both and compare.

High Duplicated read as success

Trigger: treating high BUSCO-D as good. Mechanism: uncollapsed haplotigs vs real WGD vs split models. Symptom: inflated gene count. Fix: plot duplicated pairs against synteny + clade ploidy; if no WGD and D>5-8%, purge_dups the assembly first.

Shallow lineage dataset

Trigger: eukaryota_odb10 (~255 genes) instead of the deep clade set. Mechanism: a small, conserved set is trivially easy to hit 99%. Symptom: misleadingly high completeness. Fix: use the deepest applicable clade dataset; record it.

BUSCO alone, no OMArk

Trigger: judging a proteome by BUSCO completeness only. Mechanism: the single-copy-ortholog lens misses over-prediction, chimeras, and contamination. Symptom: green BUSCO on a proteome full of fused/fragmented or contaminant models. Fix: add OMArk for consistency + contamination.

Annotating before the QC gate (prokaryote)

Trigger: Bakta/Prokka before CheckM2. Mechanism: contamination mixes organisms; low completeness truncates. Symptom: chimeric/inflated gene set, uninterpretable coding density. Fix: CheckM2 first; contamination >5% -> decontaminate.

Quantitative Thresholds

Threshold	Source	Rationale
Assembly-BUSCO vs proteome-BUSCO gap	the diagnostic fork	large gap = predictor missed present genes; both low = fix assembly
BUSCO-Duplicated ~1-3% (clean haploid); >5-8% no WGD	assembly norm	purge_dups before annotating
Use deepest applicable clade _odb10	BUSCO guidance	shallow sets trivially hit 99%
Mono-exonic ~10-20% (vertebrate); >25-30% flag	clade norm	unmasked TEs/pseudogenes/fragments; fungi legitimately higher
Protein length unimodal ~300-450 aa	eukaryote norm	sub-100-aa spike = spurious; fat left tail = partials
mRNA:gene ratio == 1.00	annotation structure	isoform/UTR-naive
CheckM2 contamination ≤5%, completeness ≥90%	MIMAG-aligned	above/below -> prokaryotic QC numbers uninterpretable
Prokaryotic coding density ~88-90%	bacterial norm	<85% wrong table/fragmentation; >93% over-calling

Common Errors

Error / symptom	Cause	Solution
BUSCO high, annotation still bad	BUSCO measures only the conserved core	add OMArk + sanity panel; inspect mono-exonic models
Proteome-BUSCO < genome-BUSCO	predictor missed present genes	fix evidence/masking/training, not the assembly
High Duplicated	haplotigs vs WGD vs split models	synteny + ploidy check; purge_dups if no WGD
99% completeness looks too good	shallow lineage dataset	rerun with the deep clade set
Cross-pipeline gene counts disagree	gene count is not a quality metric	compare sanity panels, not counts
CheckM2 high contamination	mixed organisms / poor binning	decontaminate before annotation

References

• Manni M, et al. 2021. BUSCO update: novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Mol Biol Evol 38:4647-4654.
• Simão FA, et al. 2015. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31:3210-3212.
• Nevers Y, et al. 2025. Quality assessment of gene repertoire annotations with OMArk. Nat Biotechnol 43:124-133.
• Chklovski A, et al. 2023. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat Methods 20:1203-1212.
• Huang N, Li H. 2023. compleasm: a faster and more accurate reimplementation of BUSCO. Bioinformatics 39:btad595.
• Guan D, et al. 2020. Identifying and removing haplotypic duplication in primary genome assemblies (purge_dups). Bioinformatics 36:2896-2898.
• Ou S, Chen J, Jiang N. 2018. Assessing genome assembly quality using the LTR Assembly Index (LAI). Nucleic Acids Res 46:e126.

Related Skills

• eukaryotic-gene-prediction - The annotation whose proteome/gene-set this QC evaluates
• prokaryotic-annotation - CheckM2 gate and coding-density sanity for prokaryotes
• annotation-transfer - BUSCO on a lifted set quantifies silently lost conserved genes
• repeat-annotation - LAI and over-masking diagnosis in the assembly-to-annotation handoff
• genome-assembly/assembly-qc - Assembly-side completeness; purge haplotigs before annotating