bio-epidemiological-genomics-pathogen-typing

## Version Compatibility Reference examples tested with: mlst 2.23+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scipy 1.12+ 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. # Pathogen Typing **"Type my bacterial isolates by MLST"** → Assign multi-locus sequence types to bacterial genomes for isolate characterization, outbreak clone identification, and strain tracking. - CLI: `mlst assembly.fasta` for 7-gene MLST typing - CLI: `chewBBACA.py AlleleCall` for core genome MLST (cgMLST) ## MLST with mlst Tool ```bash # Install mlst conda install -c bioconda mlst # Basic MLST typing mlst genome.fasta # Output: genome.fasta ecoli ST131 adk(53) fumC(40) gyrB(47) ... # Batch typing mlst *.fasta > typing_results.tsv # Specify scheme mlst --scheme senterica genome.fasta # List available schemes mlst --list # Include allele sequences in output mlst --csv genome.fasta > results.csv ``` ## Parse MLST Results ```python import pandas as pd import subprocess def run_mlst(fasta_files, scheme=None): '''Run MLST on multiple genomes Returns DataFrame with: - Sample name - Scheme (auto-detected or specified) - Sequence type (ST) - Allele profiles ST interpretation: - Known ST: Matches existing type in database - Novel allele: New allele combination, may be unreported ST - Failed: Unable to determine (poor assembly or wrong scheme) ''' cmd = ['mlst'] + fasta_files if scheme: cmd.extend(['--scheme', scheme]) result = subprocess.run(cmd, capture_output=True, text=True) lines = result.stdout.strip().split('\n') data = [line.split('\t') for line in lines] return pd.DataFrame(data, columns=['file', 'scheme', 'ST'] + [f'locus{i}' for i in range(1, len(data[0])-2)]) ``` ## Core Genome MLST (cgMLST) ```bash # chewBBACA for cgMLST pip install chewbbaca # Download or create schema chewBBACA.py DownloadSchema -sp "Salmonella enterica" -o schema_dir # Run cgMLST chewBBACA.py AlleleCall -i genomes/ -g schema_dir -o results/ # Analyze results chewBBACA.py ExtractCgMLST -i results/results_alleles.tsv \ -o cgmlst_results.tsv --threshold 0.95 ``` ## cgMLST Distance Analysis **Goal:** Compute pairwise allelic distances between isolates and cluster them to identify potential outbreak groups. **Approach:** Count allelic differences between each pair of isolate profiles (ignoring missing data), then apply single-linkage hierarchical clustering with a pathogen-specific distance threshold. ```python import pandas as pd import numpy as np def calculate_cgmlst_distance(profiles): '''Calculate allelic distances between isolates Distance interpretation (typical thresholds): - 0-5 allele differences: Same cluster (likely recent transmission) - 6-15 differences: Related (possible epidemiological link) - >15 differences: Different clones Note: Thresholds are pathogen-specific. Consult literature. ''' n = len(profiles) distances = np.zeros((n, n)) for i in range(n): for j in range(i+1, n): # Count allelic differences (excluding missing data) diff = sum(1 for a, b in zip(profiles.iloc[i], profiles.iloc[j]) if a != b and a != 0 and b != 0) distances[i, j] = distances[j, i] = diff return pd.DataFrame(distances, index=profiles.index, columns=profiles.index) def identify_clusters(distance_matrix, threshold=5): '''Identify cgMLST clusters Threshold values by organism: - E. coli: 10 alleles - Salmonella: 7 alleles - Listeria: 7 alleles - S. aureus: 24 alleles ''' from scipy.cluster.hierarchy import linkage, fcluster # Convert to condensed distance matrix condensed = distance_matrix.values[np.triu_indices(len(distance_matrix), k=1)] # Hierarchical clustering Z = linkage(condensed, method='single') clusters = fcluster(Z, t=threshold, criterion='distance') return dict(zip(distance_matrix.index, clusters)) ``` ## SNP-Based Typing ```python def snp_typing_from_vcf(vcf_file, reference_positions): '''Extract SNP profile for typing Some organisms use canonical SNP positions for typing (e.g., Mycobacterium tuberculosis lineages) ''' from cyvcf2 import VCF vcf = VCF(vcf_file) profile = {} for pos in reference_positions: chrom, position = pos.split(':') for variant in vcf(f'{chrom}:{position}-{position}'): profile[pos] = variant.ALT[0] if variant.ALT else variant.REF return profile ``` ## Enterobase Integration ```python import requests def query_enterobase(st, organism='ecoli'): '''Query Enterobase for ST metadata Enterobase provides: - Geographic distribution - Temporal trends - Associated serotypes - Virulence gene profiles ''' # Note: Requires API token url = f'https://enterobase.warwick.ac.uk/api/v2.0/{organism}/sts/{st}' # Would need authentication headers # response = requests.get(url, headers={'Authorization': f'Bearer {token}'}) print(f'Query Enterobase for ST{st}: {url}') return None # Placeholder - requires authentication ``` ## Related Skills - epidemiological-genomics/phylodynamics - Time-scaled trees from typed isolates - epidemiological-genomics/transmission-inference - Outbreak investigation - metagenomics/kraken-classification - Species identification