Version Compatibility

Reference examples tested with: ncbi-amrfinderplus 4.0+, hamronization 1.1+, tb-profiler 6.2+, mlst 2.23+, chewBBACA 3.3+, pangolin 4.3+ (pangolin-data 1.30+), nextclade 3.8+, snippy 4.6+, gubbins 3.3+, clonalframeml 1.13+, IQ-TREE 2.3.6+, TreeTime 0.11+, BEAST 2.7.6+ (BDSKY 1.5+, MASCOT 3.0+, BICEPS), TransPhylo 1.4+ (R), outbreaker2 1.2+ (R), bactdating 1.1+ (R), freyja 1.4+, mob_suite 3.1+, BioPython 1.84+, pandas 2.2+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package>; CLI: <tool> --version then --help
• R: packageVersion('<pkg>') then ?function_name
• Pangolin: pangolin --all-versions (records pangolin + pangolin-data + scorpio + constellations)
• Nextclade: nextclade dataset list --tag latest sars-cov-2
• TB-Profiler: tb-profiler list_db (verify WHO catalogue edition)

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Outbreak Pipeline

"Characterize a pathogen outbreak from my isolate sequences" -> Orchestrate MLST typing, SNP phylogeny, TreeTime time-scaled tree construction, TransPhylo transmission inference, AMR profiling, and variant surveillance for genomic epidemiology.

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

Workflow Overview

Pathogen Isolate Genomes (FASTA/FASTQ) + collection dates + (optional) contact data
        |
        v
   +---------+---------+
   |                   |
   v                   v
[1a. MLST + cgMLST     [1b. AMR + species mode:
     + serotyping +         AMRFinderPlus --organism,
     Pangolin/UShER        TB-Profiler for Mtb,
     for SARS-CoV-2]       hAMRonization across tools]
   |                   |
   +--------+----------+
            |
            v
[2. snippy + snippy-core (bacteria) -> Gubbins on core.full.aln to mask recombination
    (mandatory for bacteria; skip only for clonal Mtb)]
            |
            v
[3. IQ-TREE on recombination-masked alignment + TempEst R^2 >= 0.3 + date-randomisation;
    TreeTime --coalescent skyline --clock-filter 4 OR BactDating;
    BEAST2 BDSKY (origin > rootHeight, multi-chain) for posterior R_e]
            |
            v
[4. Transmission inference: outbreaker2 (dense + contact data) OR TransPhylo (sparse,
    from dated tree) OR transcluster (pair-level probability); pathogen-specific SNP
    threshold for cluster definition -- NEVER a universal cutoff]
            |
            v
Transmission tree posterior + R_e(t) + lineage / clone context + AMR phenotype

Prerequisites

conda install -c bioconda mlst chewbbaca ncbi-amrfinderplus hamronization tb-profiler \
    snippy snp-dists gubbins clonalframeml iqtree treetime pangolin nextclade freyja \
    mob_suite plasmidfinder sistr_cmd seqsero2 kleborate kaptive seroba

conda install -c bioconda beast2
packagemanager -add BDSKY BEASTLabs feast ORC MASCOT BICEPS

Rscript -e "install.packages(c('TransPhylo', 'outbreaker2', 'BactDating', 'bdskytools', 'coda', 'ape'))"

amrfinder -u
tb-profiler update_tbdb

Primary Path: Bacterial Outbreak Investigation

Step 1a: MLST Typing (Parallel)

Goal: Assign 7-locus PubMLST sequence types to all isolates for clonal-context interpretation.

Approach: Run Seemann's mlst per assembly; auto-detect scheme; concatenate the per-isolate output into a cohort TSV.

#!/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) -- AMRFinderPlus with species mode

Goal: Produce per-isolate AMR calls with species-specific point-mutation panel activated, then harmonise across the cohort to the PHA4GE schema for cross-lab comparison.

Approach: AMRFinderPlus -n for nucleotide assembly with --organism and --plus; pipe each per-isolate TSV through hamronize amrfinderplus with mandatory PHA4GE metadata; hamronize summarize merges to a cohort table. For M. tuberculosis, switch to TB-Profiler -- AMRFinderPlus has no Mtb organism mode.

echo "=== AMR Detection ==="
SPECIES="Klebsiella_pneumoniae"
for fasta in $ISOLATES; do
    sample=$(basename $fasta .fasta)
    amrfinder -n $fasta --organism $SPECIES --plus --threads 8 \
        -o ${OUTDIR}/amr/${sample}.amrfinder.tsv

    hamronize amrfinderplus \
        --analysis_software_version $(amrfinder -V | awk '/Software/{print $NF}') \
        --reference_database_version $(amrfinder -V | awk '/Database/{print $NF}') \
        --input_file_name ${sample} \
        ${OUTDIR}/amr/${sample}.amrfinder.tsv > ${OUTDIR}/amr/${sample}.hamr.tsv
done

hamronize summarize -t tsv -o ${OUTDIR}/amr/cohort.hamr.tsv ${OUTDIR}/amr/*.hamr.tsv
echo "AMR summary: ${OUTDIR}/amr/cohort.hamr.tsv"

For M. tuberculosis, route to TB-Profiler instead -- AMRFinderPlus has no Mtb organism mode. For colistin / mcr surveillance and any plasmid-mobility claim, follow with MOB-suite (mob_recon + mob_typer) to determine plasmid context. See epidemiological-genomics/amr-surveillance for the full decision tree.

Step 2: Core Genome Alignment + Recombination Masking (Bacteria)

Goal: Build a recombination-aware core-genome alignment that is safe for downstream clock inference.

Approach: Snippy per isolate against the reference; snippy-core to merge into the core alignment; Gubbins on core.full.aln (NOT core.aln) to mask recombinant tracts. Skipping recombination masking inflates the clock rate 2-5x for recombining bacteria (S. pneumoniae, N. gonorrhoeae, E. coli, Klebsiella, Campylobacter, H. pylori); the date-randomisation test is NOT a sufficient guard.

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 --prefix core ${OUTDIR}/alignment/snippy_*

# Mandatory for recombining bacteria (S. pneumoniae, N. gonorrhoeae, E. coli, Klebsiella,
# Campylobacter, H. pylori). Skip ONLY for clonal Mtb cross-lineage analyses where
# recombination is documented to be rare; even then a recombination check is defensible.
# Input MUST be core.full.aln (full positions including invariant); core.aln (variable-only)
# gives wrong recombination calls because Gubbins cannot estimate background SNP density.
run_gubbins.py --prefix gubbins core.full.aln

mv core.* gubbins.* ${OUTDIR}/alignment/
echo "Recombination-masked alignment: ${OUTDIR}/alignment/gubbins.filtered_polymorphic_sites.fasta"

Step 3: Phylodynamics with TreeTime

Goal: Time-scale the recombination-masked phylogeny with a global clock-rate estimate, gated by temporal-signal QC.

Approach: IQ-TREE on the recombination-masked alignment with +ASC ascertainment correction; TreeTime with coalescent skyline prior and --clock-filter 4; inspect root_to_tip_regression.pdf BEFORE trusting downstream output (R^2 >= 0.3 minimum per Rambaut 2016 TempEst).

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 on the recombination-masked alignment with ascertainment-bias correction
# +ASC is required because the input contains variable positions only post-Gubbins
subprocess.run([
    'iqtree2', '-s', str(outdir / 'alignment/gubbins.filtered_polymorphic_sites.fasta'),
    '-m', 'GTR+G+ASC', '-B', '1000', '-bnni', '-T', '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/gubbins.filtered_polymorphic_sites.fasta'),
    '--dates', str(outdir / 'phylo/metadata.tsv'),
    '--outdir', str(outdir / 'phylo/treetime_output'),
    '--coalescent', 'skyline',
    '--clock-filter', '4',  # SD multiplier; TreeTime convention (Boskova 2017)
    '--confidence',
    '--reroot', 'best'
], check=True)

# Temporal-signal QC: inspect root_to_tip_regression.pdf BEFORE trusting any downstream output.
# R^2 >= 0.3 minimum (Rambaut 2016 TempEst convention). If R^2 < 0.3, time-scaling is not
# supported -- report uncertainty and consider extending the sampling window. The
# date-randomisation test is a secondary check; it can pass with narrow sampling windows
# (false negative).
print('TreeTime output:', outdir / 'phylo/treetime_output')

Step 4: Transmission Inference with TransPhylo

Goal: Reconstruct the posterior who-infected-whom transmission tree and R_e from the dated phylogeny.

Approach: Convert the TreeTime dated tree to TransPhylo ptree; supply pathogen-tuned generation-time and sampling-time Gamma priors; run MCMC at >=1e5 iterations (10k is smoke-test only); summarise via medoid transmission tree and per-pair WIWS probability. For dense outbreaks with contact-tracing data, outbreaker2 with ctd is preferred over TransPhylo (genomic-only).

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 with enough iterations for posterior convergence; 10k is a smoke-test only.
# For publication, run >=1e5 (small outbreaks) to >=1e6+ iterations and inspect trace plots.
res <- inferTTree(tree, dateT = dateT,
                   w.shape = w_shape, w.scale = w_scale,
                   mcmcIterations = 1e5,
                   startNeg = 1, startPi = 0.5)

# Extract results
med_ctree <- medTTree(res)

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

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

# R_e estimate (effective reproduction number under current immunity / interventions).
# This is NOT R_0 (basic reproduction number in a fully susceptible population);
# the phylodynamics literature is explicit about this distinction.
Re <- getOffspringMulti(res)
cat("R_e estimate:", mean(Re), "(95% CI:", quantile(Re, 0.025), "-", quantile(Re, 0.975), ")\n")

Python Alternative: TransPhylo via rpy2

Goal: Drive the same TransPhylo workflow from Python pipelines that prefer not to fork into R.

Approach: rpy2 bridges into the TransPhylo R package with named-argument passing; same priors and MCMC iteration discipline apply.

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

Goal: Plot isolates over time coloured by sequence type to communicate cluster expansion and clonal context.

Approach: Merge collection-date metadata with MLST output; plot per-isolate timestamps as a strip chart with per-ST colour.

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

Step	Parameter	Value	Rationale
snippy	--mincov	10	Minimum coverage for variant call
Gubbins	input	core.full.aln	Full positions required to estimate background SNP density; core.aln is wrong
IQ-TREE	-m	GTR+G+ASC	+ASC ascertainment correction for SNP-only post-Gubbins input
TreeTime	--clock-filter	4	SD multiplier on root-to-tip residual; TreeTime convention
TreeTime	R^2 minimum	0.3	Below this, temporal signal insufficient (Rambaut 2016 TempEst)
TransPhylo	w.shape, w.scale	2, 7/365	Generation time ~7 days for many bacteria; cite the pathogen-specific literature
TransPhylo	mcmcIterations	1e5-1e6+	10k is a smoke-test only; inspect trace and ESS before reporting
BEAST2 BDSKY	origin	> rootHeight	Initialise to ~(tMRCA + 0.1*tMRCA); origin == rootHeight biases R_e upward (Stadler 2013)
BEAST2 chains	independent runs	>=3-4	Single-chain ESS >=200 is necessary but not sufficient; combine after marginal overlap
Pangolin	--analysis-mode	usher	pangoLEARN deprecated mid-2023; UShER default since v4 (Pongmoragot 2024)

Pathogen-Specific SNP / cgMLST Cluster Thresholds

Cluster definition is pathogen- AND population-specific. NEVER apply a universal cutoff. See epidemiological-genomics/transmission-inference for full table with citations.

Pathogen	Cluster threshold	Source
M. tuberculosis (core SNP)	<=12 SNPs (likely transmission); <=5 (recent)	Walker 2013 Lancet Infect Dis 13:137 (UK low-transmission setting -- inflates 2-5x in high-burden)
S. aureus (core SNP)	<=15 SNPs (within hospital)	Coll 2017 Clin Infect Dis 65:1781
K. pneumoniae (KPC outbreak)	<=21 SNPs	Snitkin 2012 Sci Transl Med 4:148ra116
C. difficile (recombination-masked core SNP)	<=2 SNPs (likely direct)	Eyre 2013 NEJM 369:1195
Salmonella (cgMLST, EnteroBase)	<=5 alleles	EnteroBase / EFSA convention
Listeria (PulseNet cgMLST)	<=4 alleles	PulseNet protocol
SARS-CoV-2	NOT defined by SNP alone	0-2 SNPs + epi link + sampling window
HIV-1 subtype B	1.5% TN93 distance	HIV-TRACE US-CDC default (re-tune for non-B subtypes)

Troubleshooting

Issue	Likely Cause	Solution
No MLST match	Novel ST or poor assembly	Check assembly quality, submit novel ST
Poor temporal signal	Insufficient sampling, recombination	Remove recombination with Gubbins, check dates
TreeTime clock-filter removes many	Wrong root, contamination	Re-root tree, check sample quality
TransPhylo non-convergence	Wrong generation time	Adjust w.shape/w.scale, increase iterations
Missing AMR genes	Database mismatch	Try multiple databases (ncbi, card, resfinder)

Output Files

File	Description
mlst/all_mlst.tsv	Sequence types for all isolates
amr/amr_summary.tsv	AMR gene presence/absence matrix
alignment/core.aln	Core genome SNP alignment
phylo/outbreak.treefile	ML phylogenetic tree
phylo/treetime_output/	Dated tree and molecular clock
transmission/transmission_tree.pdf	Inferred transmission network
transmission/who_infected_whom.csv	Transmission probability matrix

Related Skills

• database-access/sra-data - Download outbreak FASTQ from SRA / ENA
• database-access/ncbi-datasets-cli - Bulk-pull pathogen reference genomes (e.g. datasets download virus)
• 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