Bio Workflows Outbreak Pipeline

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