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Assembly QC

bio-genome-assembly-assembly-qc

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.

SKILL.md

Full skill instructions

Assembly QC

Evaluate genome assembly quality with contiguity metrics (QUAST) and gene completeness (BUSCO).

Key Metrics

MetricGood Assembly
N50High (relative to genome)
L50Low
ContigsFew
Misassemblies0 (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

OptionDescription
-oOutput directory
-rReference genome
-gGene annotations (GFF)
-tThreads
-mMin contig length (default: 500)
--largeFor large genomes (>100Mb)
--fragmentedFor highly fragmented assemblies
--scaffoldsInput 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

MetricDescription
Total lengthSum of contig lengths
# contigsNumber of contigs (>= min length)
Largest contigLength of largest contig
N5050% of assembly in contigs >= this length
N9090% of assembly in contigs >= this length
L50Number of contigs comprising N50
GC %GC content
# misassembliesWith reference: structural errors
Genome fractionWith 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

OptionDescription
-iInput assembly
-mMode: genome, proteins, transcriptome
-lLineage dataset
-oOutput name
-cCPU threads
--auto-lineageAuto-detect lineage
--offlineUse downloaded datasets only
--list-datasetsList available lineages

List Available Lineages

busco --list-datasets

Common Lineages

LineageUse For
bacteria_odb10Bacteria
archaea_odb10Archaea
eukaryota_odb10General eukaryote
fungi_odb10Fungi
metazoa_odb10Animals
vertebrata_odb10Vertebrates
mammalia_odb10Mammals
viridiplantae_odb10Plants
saccharomycetes_odb10Yeasts

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

QualityCompleteMissing
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

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