Bio Workflows Outbreak Pipeline

name: bio-workflows-outbreak-pipeline description: End-to-end outbreak investigation from pathogen isolates to transmission networks. Orchestrates MLST typing, AMR surveillance, phylodynamic dating, and transmission inference with TransPhylo. Use when investigating disease outbreaks or tracking pathogen transmission chains. tool_type: mixed primary_tool: mlst workflow: true depends_on: - epidemiological-genomics/pathogen-typing - epidemiological-genomics/amr-surveillance - epidemiological-genomics/phylodynamics - epidemiological-genomics/transmission-inference - epidemiological-genomics/variant-surveillance qc_checkpoints: - after_typing: "Valid ST assigned, cgMLST distance matrix computed" - after_amr: "AMR genes identified with >90% identity" - after_phylodynamics: "Root-to-tip R2 >0.5, clock rate plausible" - after_transmission: "Transmission pairs consistent with epi data" measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

Outbreak Pipeline

Complete workflow for genomic epidemiology: from pathogen isolates to transmission networks and outbreak characterization.

Workflow Overview

Pathogen Isolate Genomes (FASTA/FASTQ)
        |
        v
   +---------+---------+
   |                   |
   v                   v
[1a. MLST Typing]   [1b. AMR Detection]  <-- Parallel execution
   |                   |
   +--------+----------+
            |
            v
[2. Core Genome Alignment] --> snippy / ParSNP
            |
            v
[3. Phylodynamics] --> TreeTime / BEAST2
            |
            v
[4. Transmission Inference] --> TransPhylo
            |
            v
Transmission Network + R0 Estimates + Timeline

Prerequisites

conda install -c bioconda mlst abricate snippy iqtree fasttree

pip install treetime transphylo biopython pandas matplotlib

# R packages for TransPhylo
Rscript -e "install.packages('TransPhylo')"

Primary Path: Bacterial Outbreak Investigation

Step 1a: MLST Typing (Parallel)

#!/bin/bash
ISOLATES="isolate1.fasta isolate2.fasta isolate3.fasta"
OUTDIR="outbreak_results"
mkdir -p ${OUTDIR}/{mlst,amr,alignment,phylo,transmission}

# Run MLST on all isolates
echo "=== MLST Typing ==="
for fasta in $ISOLATES; do
    sample=$(basename $fasta .fasta)
    mlst $fasta > ${OUTDIR}/mlst/${sample}.mlst.txt
done

# Combine results
cat ${OUTDIR}/mlst/*.mlst.txt > ${OUTDIR}/mlst/all_mlst.tsv
echo "MLST complete: ${OUTDIR}/mlst/all_mlst.tsv"

Step 1b: AMR Detection (Parallel)

echo "=== AMR Detection ==="
for fasta in $ISOLATES; do
    sample=$(basename $fasta .fasta)
    abricate --db ncbi $fasta > ${OUTDIR}/amr/${sample}.amr.tsv
done

# Summary matrix
abricate --summary ${OUTDIR}/amr/*.amr.tsv > ${OUTDIR}/amr/amr_summary.tsv
echo "AMR summary: ${OUTDIR}/amr/amr_summary.tsv"

Step 2: Core Genome Alignment

echo "=== Core Genome Alignment ==="
REFERENCE="reference.gbk"  # Reference genome in GenBank format

# Run snippy for each isolate
for fasta in $ISOLATES; do
    sample=$(basename $fasta .fasta)
    snippy --outdir ${OUTDIR}/alignment/snippy_${sample} \
           --ref $REFERENCE \
           --ctgs $fasta \
           --cpus 8
done

# Core SNP alignment
snippy-core --ref $REFERENCE ${OUTDIR}/alignment/snippy_*

# Clean alignment (remove recombination, optional)
# run_gubbins.py core.full.aln

mv core.* ${OUTDIR}/alignment/
echo "Core alignment: ${OUTDIR}/alignment/core.aln"

Step 3: Phylodynamics with TreeTime

import subprocess
from Bio import Phylo, AlignIO
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path

outdir = Path('outbreak_results')

# Build ML tree
subprocess.run([
    'iqtree2', '-s', str(outdir / 'alignment/core.aln'),
    '-m', 'GTR+G', '-bb', '1000', '-nt', 'AUTO',
    '--prefix', str(outdir / 'phylo/outbreak')
], check=True)

# Prepare metadata with dates
# Format: name\tdate (YYYY-MM-DD or decimal year)
metadata = pd.DataFrame({
    'name': ['isolate1', 'isolate2', 'isolate3', 'isolate4', 'isolate5'],
    'date': ['2024-01-15', '2024-01-22', '2024-02-01', '2024-02-10', '2024-02-15']
})
metadata.to_csv(outdir / 'phylo/metadata.tsv', sep='\t', index=False)

# Run TreeTime
subprocess.run([
    'treetime',
    '--tree', str(outdir / 'phylo/outbreak.treefile'),
    '--aln', str(outdir / 'alignment/core.aln'),
    '--dates', str(outdir / 'phylo/metadata.tsv'),
    '--outdir', str(outdir / 'phylo/treetime_output'),
    '--coalescent', 'skyline',
    '--clock-filter', '3'  # Remove outliers >3 IQR from clock
], check=True)

# Check temporal signal
# Good signal: R2 > 0.5, clock rate ~1e-6 to 1e-7 subs/site/year for bacteria
print('TreeTime output:', outdir / 'phylo/treetime_output')

Step 4: Transmission Inference with TransPhylo

library(TransPhylo)
library(ape)

# Load dated tree from TreeTime
tree <- read.nexus("outbreak_results/phylo/treetime_output/timetree.nexus")

# Set parameters
# dateT: date when sampling stopped
# w.shape, w.scale: generation time distribution (Gamma)
# For many bacteria: mean ~14 days, shape=2, scale=7
dateT <- 2024.2  # Decimal year when sampling ended
w_shape <- 2     # Generation time shape (Gamma)
w_scale <- 7/365 # Generation time scale in years (~7 days mean)

# Run TransPhylo
res <- inferTTree(tree, dateT = dateT,
                   w.shape = w_shape, w.scale = w_scale,
                   mcmcIterations = 10000,
                   startNeg = 1, startPi = 0.5)

# Extract results
ttree <- extractTTree(res)

# Transmission network
medTTree <- medTTree(res)

# Plot transmission tree
pdf("outbreak_results/transmission/transmission_tree.pdf", width=10, height=8)
plotTTree(medTTree)
dev.off()

# Who infected whom matrix
wiw <- computeMatWIW(res)
write.csv(wiw, "outbreak_results/transmission/who_infected_whom.csv")

# R0 estimate
R0 <- getOffspringMulti(res)
cat("R0 estimate:", mean(R0), "(95% CI:", quantile(R0, 0.025), "-", quantile(R0, 0.975), ")\n")

Python Alternative: TransPhylo via rpy2

import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
import pandas as pd
from pathlib import Path

pandas2ri.activate()

transphylo = importr('TransPhylo')
ape = importr('ape')

outdir = Path('outbreak_results')

tree = ape.read_nexus(str(outdir / 'phylo/treetime_output/timetree.nexus'))

date_t = 2024.2
w_shape = 2
w_scale = 7/365

res = transphylo.inferTTree(tree, dateT=date_t, w_shape=w_shape, w_scale=w_scale,
                             mcmcIterations=10000, startNeg=1, startPi=0.5)

# Extract transmission pairs
med_tree = transphylo.medTTree(res)

ro.r(f'''
pdf("{outdir}/transmission/transmission_tree.pdf", width=10, height=8)
plotTTree(medTTree({res}))
dev.off()
''')

print(f'Transmission tree saved to {outdir}/transmission/')

Visualization: Outbreak Timeline

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime

metadata = pd.read_csv('outbreak_results/phylo/metadata.tsv', sep='\t')
metadata['date'] = pd.to_datetime(metadata['date'])

mlst = pd.read_csv('outbreak_results/mlst/all_mlst.tsv', sep='\t', header=None,
                    names=['file', 'scheme', 'ST'] + [f'locus{i}' for i in range(7)])
mlst['sample'] = mlst['file'].apply(lambda x: x.split('/')[-1].replace('.fasta', ''))

amr = pd.read_csv('outbreak_results/amr/amr_summary.tsv', sep='\t')

# Merge data
combined = metadata.merge(mlst[['sample', 'ST']], left_on='name', right_on='sample')

fig, ax = plt.subplots(figsize=(12, 6))

colors = {'ST11': 'red', 'ST258': 'blue', 'ST307': 'green'}
for st in combined['ST'].unique():
    subset = combined[combined['ST'] == st]
    ax.scatter(subset['date'], [1]*len(subset), label=f'ST{st}',
               s=100, c=colors.get(f'ST{st}', 'gray'), alpha=0.7)

ax.set_xlabel('Date')
ax.set_ylabel('')
ax.set_title('Outbreak Timeline by Sequence Type')
ax.legend()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('outbreak_results/outbreak_timeline.pdf')

Parameter Recommendations

Troubleshooting

Output Files

Related Skills

• epidemiological-genomics/pathogen-typing - MLST and cgMLST details
• epidemiological-genomics/amr-surveillance - AMRFinderPlus, ResFinder
• epidemiological-genomics/phylodynamics - TreeTime, BEAST2 parameters
• epidemiological-genomics/transmission-inference - TransPhylo configuration
• epidemiological-genomics/variant-surveillance - Nextclade for viral outbreaks
• phylogenetics/modern-tree-inference - IQ-TREE2 model selection