Contamination Detection

CheckM2 (Recommended)

Run CheckM2 on single genome

checkm2 predict --input assembly.fa --output-directory checkm2_output --threads 16

Run on multiple genomes (directory of FASTAs)

checkm2 predict --input genomes/ --output-directory checkm2_output
--threads 16 --extension fa

Output: quality_report.tsv with Completeness, Contamination, Coding_Density

Interpret CheckM2 Results

quality_report.tsv columns:

Name, Completeness, Contamination, Completeness_Model_Used,

Translation_Table_Used, Coding_Density, Contig_N50, Average_Gene_Length,

Genome_Size, GC_Content, Total_Coding_Sequences

Filter high-quality genomes (MIMAG standards)

awk -F'\t' 'NR==1 || ($2 > 90 && $3 < 5)' quality_report.tsv > high_quality_mags.tsv

Medium quality

awk -F'\t' 'NR==1 || ($2 >= 50 && $3 < 10)' quality_report.tsv > medium_quality_mags.tsv

CheckM (Original)

Run CheckM lineage workflow

checkm lineage_wf -t 16 -x fa genomes/ checkm_output/

Generate summary

checkm qa checkm_output/lineage.ms checkm_output/ -o 2 -f checkm_summary.tsv --tab_table

Extended report with marker genes

checkm qa checkm_output/lineage.ms checkm_output/ -o 2 --tab_table
-f checkm_extended.tsv

CheckM Plots

Completeness vs Contamination plot

checkm bin_qa_plot -x fa checkm_output/ genomes/ plots/

GC and coding density

checkm coding_plot -x fa checkm_output/ genomes/ plots/

Marker gene positions

checkm marker_plot -x fa checkm_output/ genomes/ plots/

GTDB-Tk Taxonomic Classification

Classify genomes

gtdbtk classify_wf --genome_dir genomes/ --out_dir gtdbtk_output
--extension fa --cpus 16

With species-level ANI

gtdbtk classify_wf --genome_dir genomes/ --out_dir gtdbtk_output
--extension fa --cpus 16 --skip_ani_screen

Output files:

gtdbtk.bac120.summary.tsv - bacterial classifications

gtdbtk.ar53.summary.tsv - archaeal classifications

GTDB-Tk De Novo Workflow

When genomes may include novel taxa

gtdbtk de_novo_wf --genome_dir genomes/ --out_dir gtdbtk_denovo
--bacteria --extension fa --cpus 16

GUNC Chimerism Detection

Run GUNC

gunc run -d genomes/ -o gunc_output -t 16 -e .fa

Output: GUNC.progenomes_2.1.maxCSS_level.tsv

Key columns: pass.GUNC (true/false), contamination_portion, clade_separation_score

Filter chimeric genomes

awk -F'\t' '$8 == "False"' GUNC.progenomes_2.1.maxCSS_level.tsv > chimeric_genomes.tsv

GUNC Interpretation

GUNC flags genomes as chimeric if:

- clade_separation_score (CSS) > 0.45

- contamination_portion > 0.05

- reference_representation_score > 0.5

Combine with CheckM2 for full QC

join -t$'\t' -1 1 -2 1
<(sort checkm2_output/quality_report.tsv)
<(sort gunc_output/GUNC.progenomes_2.1.maxCSS_level.tsv)
> combined_qc.tsv

Comprehensive QC Pipeline

#!/bin/bash GENOMES_DIR=$1 OUTPUT_DIR=$2 THREADS=${3:-16}

mkdir -p "$OUTPUT_DIR"

Run CheckM2

echo "Running CheckM2..." checkm2 predict --input "$GENOMES_DIR" --output-directory "$OUTPUT_DIR/checkm2"
--threads "$THREADS" --extension fa

Run GUNC

echo "Running GUNC..." gunc run -d "$GENOMES_DIR" -o "$OUTPUT_DIR/gunc" -t "$THREADS" -e .fa

Run GTDB-Tk

echo "Running GTDB-Tk..." gtdbtk classify_wf --genome_dir "$GENOMES_DIR" --out_dir "$OUTPUT_DIR/gtdbtk"
--extension fa --cpus "$THREADS"

echo "QC complete!"

Filter by Quality Standards

import pandas as pd

checkm = pd.read_csv('checkm2_output/quality_report.tsv', sep='\t') gunc = pd.read_csv('gunc_output/GUNC.progenomes_2.1.maxCSS_level.tsv', sep='\t')

merged = checkm.merge(gunc, left_on='Name', right_on='genome', how='left')

MIMAG High Quality: >90% complete, <5% contamination, not chimeric

hq = merged[(merged['Completeness'] > 90) & (merged['Contamination'] < 5) & (merged['pass.GUNC'] == True)]

MIMAG Medium Quality: >50% complete, <10% contamination

mq = merged[(merged['Completeness'] >= 50) & (merged['Contamination'] < 10)]

hq.to_csv('high_quality_genomes.tsv', sep='\t', index=False) mq.to_csv('medium_quality_genomes.tsv', sep='\t', index=False)

Remove Contamination

Use MAGpurify to remove contaminating contigs

magpurify phylo-markers genome.fa magpurify_output magpurify clade-markers genome.fa magpurify_output magpurify conspecific genome.fa magpurify_output magpurify tetra-freq genome.fa magpurify_output magpurify gc-content genome.fa magpurify_output magpurify known-contam genome.fa magpurify_output magpurify clean-bin genome.fa magpurify_output cleaned_genome.fa

Detect Foreign Contigs

Contig-level taxonomy with CAT

CAT contigs -c assembly.fa -d CAT_database -t CAT_taxonomy
-o cat_output -n 16

Parse results

CAT add_names -i cat_output.contig2classification.txt
-o cat_output.contig2classification.named.txt
-t CAT_taxonomy --only_official

Flag contigs with different taxonomy than majority

awk -F'\t' '{print $1, $NF}' cat_output.contig2classification.named.txt |
sort | uniq -c | sort -rn

Decontaminate with BlobTools

Create BlobDB

blobtools create -i assembly.fa -b aligned.bam -t blast_hits.txt
-o blobtools_output

Generate plots

blobtools plot -i blobtools_output.blobDB.json

Filter by taxonomy

blobtools view -i blobtools_output.blobDB.json -r all -o filtered

Related Skills

• genome-assembly/assembly-qc - BUSCO and other QC

• genome-assembly/long-read-assembly - Assembly methods

• metagenomics/taxonomic-profiling - Metagenome analysis