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Get Started Free →Assess genome assembly quality using QUAST for contiguity metrics and BUSCO for completeness. Essential for evaluating assembly success and comparing assemblers. Use when evaluating assembly completeness and quality.
.claude/skills/bio-genome-assembly-assembly-qc/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 238% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 68% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 93% | 0% |
| case-06 | ✓→✓ | = Same ✓ | 126% | 0% |
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Evaluate genome assembly quality with contiguity metrics (QUAST) and gene completeness (BUSCO).
| Metric | Good Assembly | |--------|---------------| | N50 | High (relative to genome) | | L50 | Low | | Contigs | Few | | Misassemblies | 0 (with reference) | | BUSCO Complete | >95% | | BUSCO Duplicated | <5% (unless polyploid) |
bashconda install -c bioconda quast
bashquast.py assembly.fasta -o quast_output
bashquast.py assembly.fasta -r reference.fasta -o quast_output
bashquast.py assembly1.fa assembly2.fa assembly3.fa -o comparison
| Option | Description | |--------|-------------| | -o | Output directory | | -r | Reference genome | | -g | Gene annotations (GFF) | | -t | Threads | | -m | Min contig length (default: 500) | | --large | For large genomes (>100Mb) | | --fragmented | For highly fragmented assemblies | | --scaffolds | Input is scaffolds (includes N-gaps) |
bashquast.py assembly.fasta -r reference.fasta -g genes.gff -o quast_output
bashquast.py --large assembly.fasta -o quast_output -t 16
quast_output/
├── report.txt # Summary statistics
├── report.html # Interactive report
├── report.tsv # Tab-separated stats
├── icarus.html # Contig viewer
└── aligned_stats/ # If reference provided| Metric | Description | |--------|-------------| | Total length | Sum of contig lengths | | # contigs | Number of contigs (>= min length) | | Largest contig | Length of largest contig | | N50 | 50% of assembly in contigs >= this length | | N90 | 90% of assembly in contigs >= this length | | L50 | Number of contigs comprising N50 | | GC % | GC content | | # misassemblies | With reference: structural errors | | Genome fraction | With reference: % of reference covered |
bashconda install -c bioconda busco
bashbusco -i assembly.fasta -m genome -l bacteria_odb10 -o busco_output
| Option | Description | |--------|-------------| | -i | Input assembly | | -m | Mode: genome, proteins, transcriptome | | -l | Lineage dataset | | -o | Output name | | -c | CPU threads | | --auto-lineage | Auto-detect lineage | | --offline | Use downloaded datasets only | | --list-datasets | List available lineages |
bashbusco --list-datasets
| Lineage | Use For | |---------|---------| | bacteria_odb10 | Bacteria | | archaea_odb10 | Archaea | | eukaryota_odb10 | General eukaryote | | fungi_odb10 | Fungi | | metazoa_odb10 | Animals | | vertebrata_odb10 | Vertebrates | | mammalia_odb10 | Mammals | | viridiplantae_odb10 | Plants | | saccharomycetes_odb10 | Yeasts |
bashbusco -i assembly.fasta -m genome --auto-lineage -o busco_output
busco_output/
├── short_summary.txt # Quick summary
├── full_table.tsv # All BUSCO results
├── missing_busco_list.tsv # Missing genes
└── busco_sequences/ # BUSCO gene sequencesC:98.5%[S:97.0%,D:1.5%],F:0.5%,M:1.0%,n:4085
C - Complete (total)
S - Single-copy
D - Duplicated
F - Fragmented
M - Missing
n - Total BUSCO groups| Quality | Complete | Missing | |---------|----------|---------| | Excellent | >95% | <2% | | Good | >90% | <5% | | Acceptable | >80% | <10% | | Poor | <80% | >10% |
bash#!/bin/bash set -euo pipefail ASSEMBLY=$1 REFERENCE=${2:-} LINEAGE=${3:-bacteria_odb10} OUTDIR=${4:-assembly_qc} mkdir -p $OUTDIR echo "=== Assembly QC ===" # QUAST echo "Running QUAST..." if [ -n "$REFERENCE" ]; then quast.py $ASSEMBLY -r $REFERENCE -o ${OUTDIR}/quast -t 8 else quast.py $ASSEMBLY -o ${OUTDIR}/quast -t 8 fi # BUSCO echo "Running BUSCO..." busco -i $ASSEMBLY -m genome -l $LINEAGE -o busco_run -c 8 mv busco_run ${OUTDIR}/busco # Summary echo "" echo "=== QUAST Summary ===" cat ${OUTDIR}/quast/report.txt echo "" echo "=== BUSCO Summary ===" cat ${OUTDIR}/busco/short_summary*.txt echo "" echo "Reports saved to $OUTDIR"
bashquast.py \ spades_assembly.fa \ flye_assembly.fa \ canu_assembly.fa \ -r reference.fa \ -l "SPAdes,Flye,Canu" \ -o assembly_comparison
bash# Run BUSCO on each assembly for asm in spades.fa flye.fa canu.fa; do name=$(basename $asm .fa) busco -i $asm -m genome -l bacteria_odb10 -o busco_${name} done # Generate comparison plot generate_plot.py -wd . busco_spades busco_flye busco_canu
pythonimport pandas as pd def parse_quast(report_tsv): '''Parse QUAST report.tsv file.''' df = pd.read_csv(report_tsv, sep='\t', index_col=0) return df.T stats = parse_quast('quast_output/report.tsv') print(f"N50: {stats['N50'].values[0]}") print(f"Total length: {stats['Total length'].values[0]}") print(f"# contigs: {stats['# contigs'].values[0]}")
pythonimport re def parse_busco_summary(summary_file): '''Parse BUSCO short summary.''' with open(summary_file) as f: text = f.read() pattern = r'C:(\d+\.\d+)%\[S:(\d+\.\d+)%,D:(\d+\.\d+)%\],F:(\d+\.\d+)%,M:(\d+\.\d+)%,n:(\d+)' match = re.search(pattern, text) if match: return { 'complete': float(match.group(1)), 'single': float(match.group(2)), 'duplicated': float(match.group(3)), 'fragmented': float(match.group(4)), 'missing': float(match.group(5)), 'total': int(match.group(6)) } return None result = parse_busco_summary('busco_output/short_summary.txt') print(f"Complete: {result['complete']}%")
bashmetaquast.py metagenome_assembly.fa -o metaquast_output -t 16
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-06 | pass→pass | 7,546 | 2,622 | -65% | 1 | 1 | 0% | 1,209 | 2,729 | +126% | 0 | 0 | — |
case-07 | pass→pass | 2,062 | 2,085 | +1% | 1 | 1 | 0% | 350 | 2,673 | +664% | 0 | 0 | — |
case-08 | fail→pass | 4,870 | 3,455 | -29% | 1 | 1 | 0% | 874 | 2,952 | +238% | 0 | 0 | — |
case-09 | pass→pass | 4,324 | 2,723 | -37% | 1 | 1 | 0% | 825 | 2,742 | +232% | 0 | 0 | — |
case-10 | pass→pass | 2,992 | 1,773 | -41% | 1 | 1 | 0% | 471 | 2,583 | +448% | 0 | 0 | — |
case-11 | pass→pass | 4,447 | 2,853 | -36% | 1 | 1 | 0% | 673 | 2,764 | +311% | 0 | 0 | — |
case-02 | pass→pass | 3,895 | 2,404 | -38% | 1 | 1 | 0% | 541 | 2,731 | +405% | 0 | 0 | — |
case-03 | pass→pass | 7,033 | 2,771 | -61% | 1 | 1 | 0% | 1,163 | 2,758 | +137% | 0 | 0 | — |
case-04 | pass→pass | 8,981 | 5,117 | -43% | 1 | 1 | 0% | 1,618 | 3,279 | +103% | 0 | 0 | — |
case-05 | pass→pass | 5,806 | 2,408 | -59% | 1 | 1 | 0% | 1,025 | 2,639 | +157% | 0 | 0 | — |
case-16 | pass→pass | 2,752 | 2,036 | -26% | 1 | 1 | 0% | 455 | 2,620 | +476% | 0 | 0 | — |
case-17 | pass→pass | 3,657 | 2,107 | -42% | 1 | 1 | 0% | 584 | 2,598 | +345% | 0 | 0 | — |
case-18 | pass→pass | 3,456 | 2,533 | -27% | 1 | 1 | 0% | 625 | 2,759 | +341% | 0 | 0 | — |
case-01 | fail→pass | 10,055 | 8,452 | -16% | 1 | 1 | 0% | 2,017 | 2,964 | +47% | 0 | 0 | — |
case-12 | pass→pass | 2,838 | 1,180 | -58% | 1 | 1 | 0% | 427 | 2,451 | +474% | 0 | 0 | — |
case-13 | pass→pass | 6,257 | 1,986 | -68% | 1 | 1 | 0% | 985 | 2,607 | +165% | 0 | 0 | — |
case-14 | pass→pass | 3,552 | 2,829 | -20% | 1 | 1 | 0% | 638 | 2,829 | +343% | 0 | 0 | — |
case-15 | fail→pass | 9,892 | 5,986 | -39% | 1 | 1 | 0% | 2,172 | 3,645 | +68% | 0 | 0 | — |
case-19 | pass→pass | 11,228 | 6,493 | -42% | 1 | 1 | 0% | 1,773 | 3,182 | +79% | 0 | 0 | — |
case-20 | fail→pass | 11,825 | 9,284 | -21% | 1 | 1 | 0% | 2,093 | 4,030 | +93% | 0 | 0 | — |
case-21 | pass→pass | 4,008 | 3,431 | -14% | 1 | 1 | 0% | 749 | 2,942 | +293% | 0 | 0 | — |
case-22 | pass→pass | 10,438 | 12,505 | +20% | 1 | 1 | 0% | 1,814 | 4,617 | +155% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +18 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 7/24/2026 | -100% |
Other measured skills in the registry, with their headline benchmark lift.