bio-epidemiological-genomics-amr-surveillance

## 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 Surveillance **"Screen my isolates for resistance genes and track AMR trends"** → Detect antimicrobial resistance determinants in bacterial genomes and monitor resistance patterns over time for surveillance programs. - CLI: `amrfinder -n assembly.fasta --plus --organism Klebsiella` ## AMRFinderPlus ```bash # 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 ``` ## Parse AMRFinder Results ```python 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 Alternative ```bash # 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 ``` ## Track Resistance Trends **Goal:** Monitor how AMR gene prevalence changes over time across a surveillance cohort. **Approach:** Group samples by time period, count AMR gene occurrences per period, and normalize to prevalence percentages for trend analysis. ```python 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 ``` ## Clinical Interpretation ```python # 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 ``` ## Surveillance Report **Goal:** Generate a summary report of AMR prevalence by drug class with alerts for critical resistance types. **Approach:** Aggregate AMR detections by drug class, calculate per-class prevalence as percentage of total samples, and flag carbapenem, colistin, and vancomycin resistance specifically. ```python 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 ``` ## Related Skills - metagenomics/amr-detection - AMR from metagenomic samples - epidemiological-genomics/pathogen-typing - Strain context for AMR - variant-calling/variant-annotation - Point mutation resistance