Detect and track antimicrobial resistance genes using AMRFinderPlus and ResFinder with epidemiological context. Monitor resistance trends and identify emerging resistance patterns. Use when screening genomes for AMR genes or tracking resistance in surveillance programs.
# Install AMRFinderPlus
conda install -c bioconda ncbi-amrfinderplus
# Update database
amrfinder -u
# Basic AMR detection from genome
amrfinder -n genome.fasta -o results.tsv
# With protein input (faster, more sensitive)
amrfinder -p proteins.faa -o results.tsv
# Specify organism for point mutations
amrfinder -n genome.fasta --organism Salmonella -o results.tsv
# Available organisms: Acinetobacter_baumannii, Campylobacter,
# Clostridioides_difficile, Enterococcus_faecalis, Enterococcus_faecium,
# Escherichia, Klebsiella, Neisseria, Pseudomonas_aeruginosa,
# Salmonella, Staphylococcus_aureus, Staphylococcus_pseudintermedius,
# Streptococcus_agalactiae, Streptococcus_pneumoniae, Streptococcus_pyogenes,
# Vibrio_cholerae
import pandas as pd
def parse_amrfinder(results_file):
'''Parse AMRFinderPlus output
Key columns:
- Gene symbol: AMR gene name
- Sequence name: Contig/protein where found
- Element type: AMR, STRESS, VIRULENCE
- Element subtype: AMR mechanism
- Class: Drug class affected
- Subclass: Specific drug affected
- % Coverage: Alignment coverage (>90% typical cutoff)
- % Identity: Sequence identity (>90% typical cutoff)
'''
df = pd.read_csv(results_file, sep='\t')
# Filter high-confidence hits
df = df[(df['% Coverage of reference sequence'] >= 90) &
(df['% Identity to reference sequence'] >= 90)]
return df
def summarize_amr_profile(results_df):
'''Summarize AMR profile by drug class'''
amr_only = results_df[results_df['Element type'] == 'AMR']
summary = {
'total_genes': len(amr_only),
'drug_classes': amr_only['Class'].nunique(),
'by_class': amr_only.groupby('Class')['Gene symbol'].apply(list).to_dict()
}
return summary
# ResFinder for acquired resistance genes
# Web: https://cge.cbs.dtu.dk/services/ResFinder/
# Command line via KMA
kma -i reads_1.fq reads_2.fq -o output -t_db resfinder_db -1t1
# Or use CGE Docker
docker run --rm -v $(pwd):/data cgetools/resfinder \
-i /data/genome.fasta -o /data/results -db_res /db/resfinder_db
def analyze_amr_trends(samples_df, date_col='collection_date', gene_col='Gene symbol'):
'''Analyze AMR gene prevalence over time
For surveillance programs tracking:
- Emergence of new resistance
- Increasing prevalence of known resistance
- Geographic spread patterns
'''
# Group by time period
samples_df['period'] = pd.to_datetime(samples_df[date_col]).dt.to_period('M')
# Calculate prevalence by period
prevalence = samples_df.groupby(['period', gene_col]).size().unstack(fill_value=0)
# Normalize to percentage
total_per_period = samples_df.groupby('period').size()
prevalence_pct = prevalence.div(total_per_period, axis=0) * 100
return prevalence_pct
def detect_emerging_resistance(historical_df, new_samples_df):
'''Flag novel or increasing resistance patterns
Alerts for:
1. New AMR gene not seen before
2. Significant increase in prevalence
3. New combinations of resistance
'''
historical_genes = set(historical_df['Gene symbol'].unique())
new_genes = set(new_samples_df['Gene symbol'].unique())
novel = new_genes - historical_genes
if novel:
print(f'ALERT: Novel resistance genes detected: {novel}')
return novel
# Drug-gene relationships for interpretation
AMR_INTERPRETATION = {
'bla_CTX-M': {
'class': 'Beta-lactam',
'affects': ['Cephalosporins (3rd gen)', 'Penicillins'],
'clinical': 'ESBL producer - avoid cephalosporins'
},
'bla_KPC': {
'class': 'Beta-lactam',
'affects': ['Carbapenems', 'Cephalosporins', 'Penicillins'],
'clinical': 'Carbapenemase - limited treatment options'
},
'mcr-1': {
'class': 'Polymyxin',
'affects': ['Colistin'],
'clinical': 'Plasmid-mediated colistin resistance - critical'
},
'vanA': {
'class': 'Glycopeptide',
'affects': ['Vancomycin', 'Teicoplanin'],
'clinical': 'VRE - infection control measures required'
}
}
def interpret_amr_profile(genes):
'''Generate clinical interpretation of AMR profile'''
interpretations = []
for gene in genes:
for pattern, info in AMR_INTERPRETATION.items():
if pattern in gene:
interpretations.append({
'gene': gene,
**info
})
break
return interpretations
def generate_surveillance_report(samples_df, period='month'):
'''Generate AMR surveillance summary report
Standard surveillance metrics:
- Prevalence by drug class
- Trends over time
- Geographic distribution
- Emerging threats
'''
report = {
'period': period,
'total_samples': len(samples_df['sample_id'].unique()),
'total_amr_genes': samples_df['Gene symbol'].nunique()
}
# Prevalence by class
class_counts = samples_df.groupby('Class')['sample_id'].nunique()
report['prevalence_by_class'] = (class_counts / report['total_samples'] * 100).to_dict()
# Critical resistance
critical = ['Carbapenem', 'Colistin', 'Vancomycin']
for drug in critical:
matching = samples_df[samples_df['Class'].str.contains(drug, case=False, na=False)]
report[f'{drug.lower()}_resistance'] = len(matching['sample_id'].unique())
return report