bio-metagenomics-amr-detection

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

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

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

Goal: Screen a bacterial assembly for antimicrobial resistance genes using multiple databases for comprehensive resistance profiling.

Approach: Run AMRFinderPlus with organism-specific point mutation detection, then ABRicate against NCBI/CARD/ResFinder databases, and summarize drug class counts.

#!/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