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name: bio-metagenomics-amr-detection description: Detect antimicrobial resistance genes using AMRFinderPlus, ResFinder, and CARD. Screen isolates and metagenomes for resistance determinants. Use when characterizing resistance profiles in clinical isolates, surveillance samples, or metagenomic data. tool_type: cli primary_tool: AMRFinderPlus measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

---

AMR Detection

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

From Protein Sequences

# 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

Output Fields

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

Batch Processing

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

ResFinder

Installation

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

Run 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

From Raw Reads (KMA)

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

CARD/RGI

Resistance Gene Identifier with detailed mechanism annotations.

Installation

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

Run RGI

# 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

RGI Output

# Main results
cat rgi_output.txt

# JSON with full details
cat rgi_output.json

ABRicate (Quick Screening)

Installation

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

Available Databases

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

Run ABRicate

# 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

Batch Summary

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

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

Metagenome AMR Profiling

Using ShortBRED

# 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

Using GROOT

# 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

Complete Workflow

#!/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/"

Summarize Results

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'])

Related Skills

• metagenomics/kraken-classification - Taxonomic context
• metagenomics/functional-profiling - Functional pathways
• genome-assembly/contamination-detection - Sample QC
• workflows/metagenomics-pipeline - Full metagenomics workflow

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