bio-genome-assembly-assembly-qc
SkillDev toolsLets your agent run quality checks on genome assembly results in bioinformatics work.
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About this capability
The largest open-source medical AI skills library for OpenClaw🦞.
What this skill tells your AI
The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/bio-genome-assembly-assembly-qc/SKILL.md and read by ahel’s review.
name: bio-genome-assembly-assembly-qc description: 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. tool_type: cli primary_tool: QUAST measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Assembly QC
Evaluate genome assembly quality with contiguity metrics (QUAST) and gene completeness (BUSCO).
Key Metrics
| Metric | Good Assembly |
|---|---|
| N50 | High (relative to genome) |
| L50 | Low |
| Contigs | Few |
| Misassemblies | 0 (with reference) |
| BUSCO Complete | >95% |
| BUSCO Duplicated | <5% (unless polyploid) |
QUAST
Installation
conda install -c bioconda quast
Basic Usage
quast.py assembly.fasta -o quast_output
With Reference Genome
quast.py assembly.fasta -r reference.fasta -o quast_output
Compare Multiple Assemblies
quast.py assembly1.fa assembly2.fa assembly3.fa -o comparison
Key Options
| 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) |
With Gene Annotations
quast.py assembly.fasta -r reference.fasta -g genes.gff -o quast_output
For Large Genomes
quast.py --large assembly.fasta -o quast_output -t 16
Output Files
quast_output/
├── report.txt # Summary statistics
├── report.html # Interactive report
├── report.tsv # Tab-separated stats
├── icarus.html # Contig viewer
└── aligned_stats/ # If reference provided
Key Output Metrics
| 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 |
BUSCO
Installation
conda install -c bioconda busco
Basic Usage
busco -i assembly.fasta -m genome -l bacteria_odb10 -o busco_output
Key Options
| 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 |
List Available Lineages
busco --list-datasets
Common Lineages
| 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 |
Auto-Lineage Detection
busco -i assembly.fasta -m genome --auto-lineage -o busco_output
Output Files
busco_output/
├── short_summary.txt # Quick summary
├── full_table.tsv # All BUSCO results
├── missing_busco_list.tsv # Missing genes
└── busco_sequences/ # BUSCO gene sequences
Interpret Results
C: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 Thresholds
| Quality | Complete | Missing |
|---|---|---|
| Excellent | >95% | <2% |
| Good | >90% | <5% |
| Acceptable | >80% | <10% |
| Poor | <80% | >10% |
Complete QC Workflow
#!/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 $OUTDIR"
Compare Assemblies
QUAST Comparison
quast.py \
spades_assembly.fa \
flye_assembly.fa \
canu_assembly.fa \
-r reference.fa \
-l "SPAdes,Flye,Canu" \
-o assembly_comparison
BUSCO Comparison
# Run BUSCO on each assembly
for asm in spades.fa flye.fa canu.fa; do
name=$(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
Python: Parse QUAST Output
import 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]}")
Python: Parse BUSCO Output
import 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']}%")
MetaQUAST (Metagenomes)
metaquast.py metagenome_assembly.fa -o metaquast_output -t 16
Troubleshooting
Low N50
- Check coverage depth
- Consider longer reads
- Try different assembler
Low BUSCO Completeness
- Check input read quality
- Verify correct lineage dataset
- May indicate real gene loss (compare to relatives)
High Duplication in BUSCO
- Normal for polyploids
- May indicate contamination
- Check for collapsed haplotypes
Related Skills
- short-read-assembly - SPAdes assembly
- long-read-assembly - Flye/Canu assembly
- assembly-polishing - Improve accuracy
- metagenomics - Metagenome analysis
Signals
- GitHub stars
- 3k
- Forks
- 407
- Last commit
- Jul 2026
Advanced
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- Gateway key
bio-genome-assembly-assembly-qc-freedomintelligence- Source
- github.com/freedomintelligence/openclaw-medical-skills