Bio Genome Assembly Contamination Detection Agent Skill
Detect contamination and assess genome quality using CheckM, CheckM2, GTDB-Tk, and GUNC for metagenome-assembled genomes and isolate assemblies. Use when checking assemblies for contamination.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill bio-genome-assembly-contamination-detectionWhat comes with it
3 950 bytes besides the instructionThe instruction itself
16 sections, as written by the authorVersion Compatibility
Reference examples tested with: pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Contamination Detection
"Check my assembly for contamination" → Evaluate genome completeness and detect contaminating sequences using marker gene sets or chimeric contig detection.
- CLI:
checkm2 predict --input assembly.fa,gunc run,gtdbtk classify_wf
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
Goal: Run a multi-tool quality assessment on genome assemblies combining completeness, contamination, chimerism, and taxonomic classification.
Approach: Execute CheckM2 for completeness/contamination, GUNC for chimerism detection, and GTDB-Tk for taxonomic assignment in sequence, producing complementary QC reports.
#!/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
Goal: Classify assembled genomes into MIMAG quality tiers (high/medium) by combining CheckM2 and GUNC results.
Approach: Merge CheckM2 completeness/contamination scores with GUNC chimerism flags, then apply MIMAG thresholds (>90% complete, <5% contamination, not chimeric for high quality).
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
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How to use it
Take biotender-max/bio-genome-assembly-contamination-detection from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.