bio-epidemiological-genomics-pathogen-typing

name	bio-epidemiological-genomics-pathogen-typing
description	Perform multi-locus sequence typing (MLST), core genome MLST, and SNP-based strain typing for bacterial isolate characterization using mlst and chewBBACA. Use when identifying strain types, tracking outbreak clones, or characterizing bacterial isolates.
tool_type	cli
primary_tool	mlst

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

# 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

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)

# 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.

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=)
    clusters = fcluster(Z, t=threshold, criterion=)

     ((distance_matrix.index, clusters))

SNP-Based Typing

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

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