bio-metagenomics-amr-detection

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot 74a0655b34b9

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

Version Compatibility

Reference examples tested with: AMRFinderPlus 3.12+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

Python: pip show <package> then help(module.function) to check signatures
CLI: <tool> --version then <tool> --help to 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.

AMR Detection

"Screen my isolates for antibiotic resistance genes" → Identify antimicrobial resistance determinants in bacterial genomes or metagenomes by searching against curated resistance gene databases.

CLI: amrfinder -n assembly.fasta --organism Escherichia (AMRFinderPlus)
CLI: ResFinder, CARD/RGI for alternative database searches

Identify antimicrobial resistance (AMR) genes in bacterial genomes and metagenomes.

Tool Comparison

Tool	Database	Best For
AMRFinderPlus	NCBI	Comprehensive, curated
ResFinder	CGE	Clinical isolates
CARD/RGI	CARD	Detailed resistance mechanisms
ABRicate	Multiple	Quick screening

AMRFinderPlus (NCBI)

Installation

conda install -c bioconda ncbi-amrfinderplus
amrfinder -u  # Update database

From Nucleotide Sequences

# Assembled contigs
amrfinder -n contigs.fasta -o amr_results.tsv --threads 8

# With organism for point mutations
amrfinder -n contigs.fasta -O Escherichia -o amr_results.tsv

# Include stress/virulence genes
amrfinder -n contigs.fasta -O Salmonella --plus -o amr_results.tsv

# If you have predicted proteins
amrfinder -p proteins.faa -o amr_results.tsv

# Combined nucleotide and protein
amrfinder -n contigs.fasta -p proteins.faa -g gff_annotation.gff \
    -O Escherichia -o amr_results.tsv

Column	Description
Gene symbol	AMR gene name
Sequence name	Contig/protein ID
Element type	AMR, STRESS, VIRULENCE
Element subtype	Specific class
Class	Drug class
Subclass	Specific drug
% Coverage	Query coverage
% Identity	Sequence identity

for fasta in assemblies/*.fasta; do
    sample=$(basename $fasta .fasta)
    amrfinder -n $fasta -O Escherichia --plus \
        -o results/${sample}_amr.tsv --threads 4
done

# Combine results
head -1 results/sample1_amr.tsv > combined_amr.tsv
for f in results/*_amr.tsv; do
    tail -n+2 $f >> combined_amr.tsv
done

conda install -c bioconda resfinder
# Or use web: https://cge.food.dtu.dk/services/ResFinder/

# Assembled genome
python -m resfinder -ifa contigs.fasta -o resfinder_output \
    -db_res /path/to/resfinder_db -acq

# With species for point mutations
python -m resfinder -ifa contigs.fasta -o resfinder_output \
    -db_res /path/to/resfinder_db \
    -db_point /path/to/pointfinder_db \
    -s "Escherichia coli" -acq

python -m resfinder -ifq reads_1.fq reads_2.fq -o resfinder_output \
    -db_res /path/to/resfinder_db -acq

conda install -c bioconda rgi
rgi load --card_json /path/to/card.json --local

# From contigs
rgi main --input_sequence contigs.fasta --output_file rgi_output \
    --input_type contig --local --clean

# From protein
rgi main --input_sequence proteins.faa --output_file rgi_output \
    --input_type protein --local

# Include loose hits (more sensitive)
rgi main --input_sequence contigs.fasta --output_file rgi_output \
    --input_type contig --include_loose --local

# Main results
cat rgi_output.txt

# JSON with full details
cat rgi_output.json

conda install -c bioconda abricate
abricate --setupdb  # Update databases

abricate --list
# ncbi, card, resfinder, argannot, megares, ecoh, ecoli_vf, plasmidfinder, vfdb

# Default (ncbi)
abricate contigs.fasta > abricate_results.tsv

# Specific database
abricate --db resfinder contigs.fasta > resfinder_results.tsv
abricate --db card contigs.fasta > card_results.tsv

# Multiple databases
for db in ncbi card resfinder; do
    abricate --db $db contigs.fasta > ${db}_results.tsv
done

# Run on multiple samples
abricate assemblies/*.fasta > all_results.tsv

# Generate summary matrix
abricate --summary all_results.tsv > summary_matrix.tsv

# Map reads to AMR markers
shortbred_quantify.py --markers amr_markers.faa \
    --wgs reads_1.fq reads_2.fq \
    --results amr_abundance.tsv \
    --threads 8

# Index database
groot index -m card.90 -i groot_index -p 8

# Align and report
groot align -i groot_index -f reads_1.fq,reads_2.fq -p 8 | \
    groot report > amr_report.tsv

#!/bin/bash
set -euo pipefail

ASSEMBLY=$1
ORGANISM=$2
OUTPUT_DIR=$3

mkdir -p $OUTPUT_DIR

echo "=== AMRFinderPlus ==="
amrfinder -n $ASSEMBLY -O $ORGANISM --plus \
    -o $OUTPUT_DIR/amrfinder.tsv --threads 8

echo "=== ABRicate (multiple databases) ==="
for db in ncbi card resfinder; do
    abricate --db $db $ASSEMBLY > $OUTPUT_DIR/abricate_${db}.tsv
done

echo "=== Summary ==="
echo "AMR genes found:"
cut -f6 $OUTPUT_DIR/amrfinder.tsv | sort | uniq -c | sort -rn | head -20

echo "=== Complete ==="
echo "Results in $OUTPUT_DIR/"

import pandas as pd

# Load AMRFinderPlus results
amr = pd.read_csv('amrfinder.tsv', sep='\t')

# Count by drug class
class_counts = amr['Class'].value_counts()
print(class_counts)

# Pivot for heatmap (multiple samples)
import glob
results = []
for f in glob.glob('results/*_amr.tsv'):
    sample = f.split('/')[-1].replace('_amr.tsv', '')
    df = pd.read_csv(f, sep='\t')
    df['Sample'] = sample
    results.append(df)

combined = pd.concat(results)
matrix = pd.crosstab(combined['Sample'], combined['Gene symbol'])

bio-metagenomics-amr-detection

Summary

SKILL.md

Version Compatibility

AMR Detection

Tool Comparison

AMRFinderPlus (NCBI)

Installation

From Nucleotide Sequences

From Protein Sequences

Output Fields

Batch Processing

ResFinder

Installation

Run ResFinder

From Raw Reads (KMA)

CARD/RGI

Installation

Run RGI

RGI Output

ABRicate (Quick Screening)

Installation

Available Databases

Run ABRicate

Batch Summary

Metagenome AMR Profiling

Using ShortBRED

Using GROOT

Complete Workflow

Summarize Results

Related Skills

Related skills