bio-genome-assembly-assembly-qc

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

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