FridrichMethod Version Compatibility
Reference examples tested with: BEAST 2.7.6+, BDSKY 1.5+, BEASTLabs 2.0+, feast 9.5+, ORC 1.1.2+, MASCOT 3.0+, BEAGLE 4.0+, TreeTime 0.11+, IQ-TREE 2.3.6+, Gubbins 3.3+, ClonalFrameML 1.13+, UShER 0.6+, matUtils 0.6+, BactDating 1.1+ (R), bdskytools 1.1+ (R), coda 0.19+ (R), ape 5.8+ (R), ggplot2 3.5+, BioPython 1.84+, dendropy 4.6+, baltic 0.2+.
Before using code patterns, verify installed versions match. If versions differ:
- BEAST:
beast -version;packagemanager -listfor BEAST2 packages and versions - Python:
pip show treetime;help(treetime.TreeTime) - R:
packageVersion('bdskytools');?bdskytools::bdskytools_plot - CLI:
gubbins --version;iqtree --version;clonalframeml --version
If BEAST2 throws IllegalArgumentException on XML load, the BDSKY / feast / BEASTLabs minor version probably moved; check the XML against the installed package's example XML in ~/beast/examples/. BEAST2 XML is NOT robust across minor releases; pin the BEAST package version in any published analysis.
Phylodynamics
"How fast is this outbreak growing, and when did it start?" -> Combine a dated set of pathogen genomes with a molecular-clock model to time-scale the phylogeny, then fit a population-dynamic model (constant / exponential / Bayesian Skyline / BICEPS / Birth-Death-Skyline) to read off R_e, growth rate, and origin date. Choice of clock (strict vs UCLN vs ORC) and tree prior (BSP coalescent vs BICEPS vs BDSKY birth-death) is load-bearing; the same data can yield different R_e under different priors. For bacterial pathogens, recombination MUST be masked first; running BEAST on a Streptococcus pneumoniae or E. coli core-genome alignment without Gubbins / ClonalFrameML inflates the clock rate 2-5x and the date-randomisation test is NOT a sufficient guard.
- CLI:
treetime --tree raw.nwk --aln aln.fasta --dates dates.tsv --coalescent skyline --clock-filter 4-- fast ML phylodynamics - Java/CLI: BEAST2 with BDSKY XML (BEAUti-generated) -- full Bayesian birth-death-skyline with R_e per epoch
- CLI:
gubbins --prefix gubbins core.full.aln-- recombination masking before bacterial clock inference - R:
bdskytools::bdskytools_plotfor BDSKY post-processing;BactDatingfor fast Bayesian dating after Gubbins
The Single Most Important Modern Insight -- Recombination passed unmasked into clock inference inflates the clock rate 2-5x and breaks every downstream estimate
The date-randomisation test is NOT a guard against unmasked recombination. For any recombining bacterium (S. pneumoniae, N. gonorrhoeae, E. coli, Klebsiella pneumoniae, Campylobacter, Helicobacter pylori), run Gubbins (Croucher 2015 NAR 43:e15) or ClonalFrameML (Didelot & Wilson 2015 PLoS Comput Biol 11:e1004041) on the core-SNP alignment FIRST, rebuild the tree on the recombination-masked alignment, THEN run TreeTime / BEAST. Only M. tuberculosis and a handful of clonal pathogens are exempt -- and even those benefit from a recombination check on cross-lineage analyses. The Mostowy 2017 Mol Biol Evol 34:1167 fastGEAR paper documents the limits of mask-based approaches for highly recombinogenic species; for those (N. gonorrhoeae, S. pneumoniae lineages with strong recombination), residual signal persists post-masking and biases downstream R_e estimates downward. Second-order insight: Volz & Frost 2014 J R Soc Interface 11:20140945 showed that BEAST coalescent priors are biased under realistic preferential sampling; BDSKY models the sampling proportion explicitly and is the correct tool when sampling rate varies; MASCOT-Skyline / MASCOT-GLM (Müller 2018 Bioinformatics 34:3843) further correct for sampling-deme covariation. Third-order insight: Featherstone & Duchêne 2023 Mol Biol Evol 40:msad132 quantified that for shallow trees with many samples, sampling times dominate over sequence information for R_e inference -- biased sampling drives biased R_e estimates regardless of how much sequence data is added.
Algorithmic Taxonomy
| Tool / model | Mechanism | Outputs | Strength | Fails when |
|---|---|---|---|---|
| TreeTime ML (Sagulenko 2018 Virus Evol 4:vex042) | ML joint optimisation of clock + dates with optional coalescent skyline prior | Time-scaled tree + clock rate + root-to-tip regression | 100-1000x faster than BEAST; ideal for outbreak-scale data | Strict-clock assumption; no posterior; no R_e directly |
| BEAST2 + BICEPS (Bouckaert 2022 Syst Biol 71:1549) | Bayesian skyline with analytic Ne integration per epoch and new tree-flexing operators | Ne(t) posterior | Modern default skyline; weeks-of-BSP becomes hours | Replaces BSP for many use cases; check current BEAST2 tutorials |
| BEAST2 + BDSKY (Stadler 2013 PNAS 110:228) | Birth-death-skyline with explicit sampling | R_e(t), become-uninfectious rate, sampling proportion | Direct R_e estimation | origin vs rootHeight confusion; rho-and-turnover unidentifiability with flat priors (Legried & Terhorst 2022 PNAS 119:e2119513119) |
| BEAST2 + MASCOT (Müller 2018 Bioinformatics 34:3843) | Marginal-approximation structured coalescent | Per-deme Ne + migration | Correct for structured sampling; replaces biased DTA (De Maio 2015 PLoS Genet 11:e1005421) | Migration unidentifiable with <20 sequences per deme |
| BEAST2 + MASCOT-Skyline / MASCOT-GLM | Time-varying migration with covariates | Migration-rate trajectories tied to predictors | Sampling-aware; addresses Volz & Frost 2014 sampling bias | ~10x slower than DTA; many users still default to DTA |
| BEAST2 + Sampled-Ancestor BDSKY | BDSKY with internal-node sampling | R_e + ancestral / longitudinal samples | Right for ancient-DNA, within-host longitudinal sampling | Specialist parameterisation |
| BactDating (Didelot 2018 NAR 46:e134) | Bayesian Poisson / mixedgamma / relaxedgamma clock on a fixed tree | Time-scaled tree + clock rate posterior | Fast Bayesian dating after Gubbins; right for large bacterial trees | mixedgamma mixes poorly; poisson is the cleaner default |
| Gubbins (Croucher 2015 NAR 43:e15) | Sliding-window elevated SNP density detection | Recombination-masked alignment + recombination GFF | Standard for clonal bacterial alignments | Cannot detect ancient recombination; mis-masks mutation hotspots |
| ClonalFrameML (Didelot & Wilson 2015 PLoS Comput Biol 11:e1004041) | Coalescent-with-recombination model on a fixed tree | Recombination-masked alignment + r/m | Model-based alternative to Gubbins | Slow on large trees |
| UShER + matUtils (Turakhia 2021 Nat Genet 53:809) | Parsimony placement on a daily-updated mutation-annotated tree | Subtrees, lineage assignments, RIPPLES recombination calls | Pandemic-scale (millions of genomes) | Parsimony branch lengths systematically shorter than ML; re-estimate branch lengths before downstream R_e |
| TempEst (Rambaut 2016 Virus Evol 2:vew007) | Root-to-tip linear regression | Clock signal R^2 | First-line temporal-signal diagnostic | Slope can be artificially good with biased sampling |
| Date randomisation (Ramsden 2009 Mol Biol Evol 26:143; Duchêne 2015 Mol Biol Evol 32:1895) | Shuffle dates, compare clock-rate estimate | Pass / fail | Detects spurious clock signal | Can "pass" with narrow sampling windows (false negative) |
Decision Tree by Scenario
| Scenario | Recommended | Why wrong choices fail |
|---|---|---|
| "Estimate Ne(t) for this virus" | BEAST2 + BICEPS (or Skygrid if BICEPS unavailable) | Constant coalescent without checking flatness; BSP if BICEPS available (BSP mixes poorly) |
| "Estimate R_e from sequences" | BEAST2 + BDSKY with sampling-process explicit; document sampling proportion per epoch | BSP-style Ne -> R_e conversion via Wallinga-Lipsitch loses sampling information |
| "Multi-deme analysis with migration" | BEAST2 + MASCOT for <=10 demes with >=20 sequences each | Exact structured coalescent (intractable >5 demes); BEAST DTA (Lemey 2009) is sampling-biased |
| "Pandemic-scale (>10k sequences)" | UShER + matUtils for placement; TreeTime for dates; BDSKY on lineage-specific subsets | Full BEAST on full dataset is intractable; using UShER branch lengths directly biases R_e |
| "Date a bacterial tree" | Snippy -> Gubbins -> IQ-TREE -> BactDating OR BEAST2; recombination mask FIRST | Skipping recombination masking inflates the clock rate 2-5x |
| "Date a fast-evolving virus" | TreeTime (Nextstrain pipeline) for routine; BEAST2 + UCLN for headline analyses | Strict clock by default underestimates rate variation on shallow trees |
| "Test for temporal signal" | TempEst root-to-tip first (R^2 >= 0.3 minimum); date-randomisation as secondary check | Skipping the diagnostic; trusting date-randomisation alone (can pass with narrow window) |
| "Reconcile phylodynamic R_e with case R_t" | Report both with explicit assumptions; investigate disagreement (sampling bias, lineage-specific signal) | Reporting one as "the" R_e |
| "Bacterial outbreak phylogenetics start-to-finish" | snippy + snippy-core -> Gubbins (on core.full.aln) -> IQ-TREE -> BactDating or BEAST2 + BDSKY |
Skipping Gubbins; running Gubbins on core.aln instead of core.full.aln |
| "Migration / phylogeography source-attribution" | MASCOT or MASCOT-GLM (sampling-aware); never BEAST DTA for attribution claims | BEAST DTA inherits Lemey 2009 sampling bias; published source-attribution remains biased toward heavily-sampled locations |
Methodology evolves; before any high-stakes phylodynamic analysis, web-search "BEAST2 BDSKY tutorial 2025" and "MASCOT-Skyline benchmark" for current best practice.
Time-Scaling With TreeTime
Goal: Produce a time-scaled phylogeny with per-node date estimates and a global clock rate, ready for downstream R_e estimation or visualisation -- in minutes rather than hours.
Approach: Build the raw topology with IQ-TREE 2 (Minh 2020 Mol Biol Evol 37:1530) for outbreak-scale data (RAxML-NG for larger trees); pass to TreeTime jointly optimising the molecular clock and date assignments with a coalescent skyline prior; inspect root_to_tip_regression.pdf BEFORE trusting any downstream output; apply --clock-filter 4 to drop tips with root-to-tip residuals exceeding 4 SDs.
iqtree -s aln.fasta -m GTR+G -B 1000 -T AUTO -pre raw_tree
treetime \
--tree raw_tree.treefile \
--aln aln.fasta \
--dates dates.tsv \
--coalescent skyline \
--clock-filter 4 \
--confidence \
--reroot best \
--outdir timetree
treetime clock \
--tree raw_tree.treefile \
--dates dates.tsv \
--reassign-dates \
--outdir date_randomisation
Outputs: timetree.nexus is the time-scaled tree; dates.tsv records per-tip filter status; root_to_tip_regression.pdf is the temporal-signal diagnostic. If R^2 < 0.3, the data do not support time-scaled inference; report uncertainty and consider extending the sampling window. For published clock rates, run the date-randomisation analysis (Duchêne 2015 Mol Biol Evol 32:1895) and report the clock-rate distribution under shuffled dates.
BDSKY in BEAST2 With Recombination Masking First
Goal: Estimate R_e through time from a bacterial outbreak alignment with explicit handling of recombination, sampling proportion, and convergence diagnostics.
Approach: Snippy + snippy-core to build the core-genome alignment; Gubbins on core.full.aln (full positions including invariant) to mask recombinant tracts; IQ-TREE on the masked alignment; BEAUti to set up BDSKY XML in BEAST 2 (Bouckaert 2019 PLoS Comput Biol 15:e1006650 for BEAST 2.5+) with origin -- NOT rootHeight; sampling proportion per epoch; become-uninfectious rate fixed from epi knowledge; run with 3-4 independent chains from different seeds; combine chains only after marginal posteriors overlap.
snippy-core --ref reference.fa --prefix core snippy_out/*
run_gubbins.py --prefix gubbins core.full.aln
iqtree -s gubbins.filtered_polymorphic_sites.fasta -m GTR+G+ASC -B 1000 -T AUTO -pre masked_tree
beast -threads 4 -beagle -seed 42 bdsky_analysis.xml
beast -threads 4 -beagle -seed 17 bdsky_analysis.xml
beast -threads 4 -beagle -seed 99 bdsky_analysis.xml
beast -threads 4 -beagle -seed 7 bdsky_analysis.xml
logcombiner -log run_42.log -log run_17.log -log run_99.log -log run_7.log -burnin 10 -o combined.log
logcombiner -log run_42.trees -log run_17.trees -log run_99.trees -log run_7.trees -burnin 10 -o combined.trees -decimalPlaces 6
loganalyser -burnin 10 combined.log
run_gubbins.py input MUST be core.full.aln (full alignment with reference). Passing core.aln (variable-only) gives wrong recombination calls because Gubbins cannot estimate background SNP density without invariant positions. IQ-TREE +ASC is the ascertainment-bias correction required for SNP-only input.
MASCOT Structured Coalescent
Goal: Infer per-deme effective population size and migration rates from sequences sampled in multiple subpopulations (countries, hospitals, ward types) without inheriting the Lemey 2009 BEAST DTA sampling bias.
Approach: BEAUti -> MASCOT template; one trait per deme; require >=20 sequences per deme for migration identifiability; consider MASCOT-GLM if migration rates plausibly depend on observable covariates (travel volume, geographic distance); pre-2024 default is MASCOT, but MASCOT-Skyline / MASCOT-GLM is preferred when sampling intensity varies over time.
beast -threads 4 -beagle -seed 42 mascot_analysis.xml
loganalyser -burnin 10 mascot.log
Per-Method Failure Modes
Recombination passed unmasked into clock inference
Trigger: BEAST2 or TreeTime run on a core-genome alignment of a recombining bacterium (S. pneumoniae, N. gonorrhoeae, E. coli, Klebsiella, Campylobacter, Helicobacter pylori).
Mechanism: Recombination imports SNPs from a divergent donor lineage in a single event. The clock model interprets these as accumulated point mutations across the branch, inflating the apparent clock rate and distorting node-date estimates. Recombination is non-clocklike, so date-randomisation tests may still pass -- the problem is silent.
Symptom: Estimated clock rate is 2-5x the literature consensus for the species (S. pneumoniae core clock ~1.5e-6 subs/site/year per literature; rates >5e-6 indicate unmasked recombination). Per-branch dN/dS profile is wildly heterogeneous. Some branches show implausibly recent divergence dates.
Fix: Mask recombinant regions before clock inference. Build initial tree with IQ-TREE on the core-SNP alignment; run Gubbins or ClonalFrameML to detect recombinant tracts; rebuild tree on the recombination-masked alignment; THEN run TreeTime or BEAST. For M. tuberculosis (rare recombination), masking is optional but defensible for cross-lineage analyses.
Date-randomisation test passes despite no real temporal signal
Trigger: Outbreak with narrow sampling window (e.g. all isolates collected within 3 months of a year-long outbreak).
Mechanism: Date randomisation (Ramsden 2009 Mol Biol Evol 26:143; Duchêne 2015 Mol Biol Evol 32:1895) tests whether shuffling dates degrades the clock-rate estimate. With insufficient temporal sampling, the true clock estimate is also poorly informed, so randomised and true estimates overlap by chance.
Symptom: TempEst root-to-tip regression R^2 < 0.1; date-randomisation test still "passes" (HPD overlap); 95% HPD on clock rate spans an order of magnitude.
Fix: Inspect root-to-tip regression FIRST (TempEst). If R^2 < 0.3 and there is no strong a-priori clock-rate prior from the literature, the data do not support time-scaled inference. Options: (1) use a strong informative prior on clock rate from outside data; (2) extend the sampling window before re-running; (3) report a tree without time-scaling and discuss uncertainty.
BDSKY origin specified as the root height
Trigger: BEAST2 BDSKY XML where origin is set to the same value as rootHeight (often because the user inferred from the tutorial that "origin = tree depth").
Mechanism: Stadler 2013 PNAS 110:228 defines origin as the time from the start of the epidemic to the most recent sample -- strictly larger than the tree root height (tMRCA). Setting origin = tMRCA causes the MCMC to start in an inconsistent state and systematically biases R_e estimates upward (because turnover is forced into a shorter time window).
Symptom: R_e estimates implausibly high in early epochs; chains mix poorly; origin-date estimate clusters at the lower bound of the prior.
Fix: Initialise origin to (tMRCA + 0.1*tMRCA) or use prior knowledge (e.g., for SARS-CoV-2 within a country, the documented import date). BEAUti default is sensible; hand-edited XML often gets this wrong.
BSP / BDSKY ESS < 200 reported as a result
Trigger: Single BEAST chain reaching the planned MCMC length; some parameters with ESS in the 50-150 range; user reports the posterior anyway.
Mechanism: Tracer's ESS calculation is a single-chain effective sample size. For phylogenetic posteriors, parameters may be "mixed within chain" but "unmixed across chains" -- multiple independent chains can converge to different parts of the posterior. ESS > 200 is necessary but not sufficient.
Symptom: Reported HPD intervals from a single chain; reviewers from Stadler / Bouckaert / Suchard schools reject the analysis.
Fix: Run >=3-4 chains from different starting trees / seeds; examine marginal posteriors per chain; only after they overlap can chains be combined (logcombiner). Report Gelman-Rubin diagnostic via R coda::gelman.diag on the per-chain log files.
MASCOT migration with too few sequences per deme
Trigger: MASCOT analysis with <20 sequences per deme; user reports migration-rate posterior.
Mechanism: MASCOT migration rates are jointly identifiable only with sufficient sequences per deme to inform within-deme coalescent. With few sequences, migration rate and Ne become confounded; the posterior reflects the prior more than the data.
Symptom: Migration-rate 95% HPD spans 2+ orders of magnitude; estimates implausibly extreme.
Fix: Pool nearby demes; accept wide HPD intervals; report MASCOT-GLM if covariates are available; cite Müller 2018 for the identifiability requirement.
DTA used for phylogeography source-attribution
Trigger: BEAST DTA (Lemey 2009 PLoS Comput Biol 5:e1000520) used to claim a geographic source for an outbreak.
Mechanism: De Maio 2015 PLoS Genet 11:e1005421 demonstrated DTA is biased toward heavily-sampled locations; the example was Ebola DTA implausibly concluding humans seeded outbreaks (truth: sylvatic reservoir spillover). DTA treats sampling as random; in reality, source-attribution-relevant locations are often the most under-sampled.
Symptom: Inferred "source" is the heavily-sampled location; conclusion is sensitive to sub-sampling; reviewers familiar with MASCOT push back.
Fix: Use MASCOT or MASCOT-Skyline / MASCOT-GLM for source-attribution claims; if infeasible, frame results as "consistent with" rather than "demonstrates" and quantify sampling bias.
UShER branch lengths used directly for R_e estimation
Trigger: Downstream BDSKY analysis on a UShER-placed subtree using UShER's parsimony branch lengths.
Mechanism: Turakhia 2021 Nat Genet 53:809 UShER places sequences on the MAT via parsimony; branch lengths under parsimony are systematically shorter than maximum-likelihood or Bayesian branch lengths (because parsimony minimises substitutions). R_e estimates that use UShER branch lengths directly are biased.
Symptom: Implausibly high R_e from a UShER-derived subtree; estimates inconsistent with case-based R_t.
Fix: Use UShER for placement only; re-estimate branch lengths with TreeTime or BEAST before downstream R_e estimation. This caveat is in the UShER documentation but routinely ignored.
R_e reported as R_0
Trigger: Phylodynamic R_e estimate (current immunity / intervention context) described in text as R_0 (fully susceptible population).
Mechanism: Phylodynamic methods estimate the effective reproduction number R_e (or R_t), not the basic reproduction number R_0. The two diverge substantially: R_0 for ancestral SARS-CoV-2 was ~5-8; R_e during Omicron waves was 1.0-1.4.
Symptom: Figure legends say R_e; discussion text says R_0; readers conflate the two; comparisons to non-phylodynamic R_0 estimates inappropriate.
Fix: Use R_e (or R_t) consistently. Explicit footnote: "R_e estimated in the current epidemic context; the basic reproduction number R_0 was higher and is not what BDSKY estimates."
Reconciliation: When Methods Disagree
| Pattern | Likely cause | Action |
|---|---|---|
| Phylodynamic R_e and case-based R_t disagree | Sampling bias on one side; lineage-specific signal in phylodynamic; under-ascertainment in case data | Report both with explicit assumptions; investigate sampling profile |
| TreeTime R^2 = 0.5 but BEAST clock rate confidence wide | TreeTime point estimate vs Bayesian posterior with prior; informative prior may collapse | Compare BEAST clock to literature; check for prior dominance |
| BDSKY R_e implausibly high in early epochs | origin mis-specified; sampling proportion too high for early epoch |
Re-check origin definition; allow sampling proportion to vary per epoch |
| Gubbins and ClonalFrameML give different recombination masks | Different model assumptions; both approximate | Either is defensible; run sensitivity by re-doing clock inference with the alternate mask |
| MASCOT and BEAST DTA disagree on migration | DTA is sampling-biased; MASCOT is sampling-aware | Trust MASCOT; report DTA only with caveat |
| UShER MAT subtree vs full BEAST disagree on TMRCA | Parsimony branch lengths shorter than ML | Re-estimate branch lengths on the UShER subtree with TreeTime before downstream |
| BICEPS Ne(t) trajectory differs from BSP on the same data | BICEPS analytic Ne integration vs BSP segment uniform | Trust BICEPS; BSP suffered from edge artifacts and slow mixing |
Quantitative Thresholds
| Quantity | Threshold | Source / rationale |
|---|---|---|
| TempEst root-to-tip R^2 minimum | >=0.3 | Rambaut 2016 Virus Evol 2:vew007 convention |
| BEAST ESS per parameter (single chain) | >=200 | Standard convention; necessary but not sufficient |
| BEAST burn-in | 10% of chain length | Convention; visually verify trace |
| MASCOT minimum sequences per deme | >=20 | Müller 2018 Bioinformatics 34:3843 identifiability requirement |
| BDSKY MCMC length (>=100 tips) | 10^7-10^8 states | Stadler 2013 PNAS 110:228; BDSKY mixes slowly |
TreeTime --clock-filter default |
4 (SD multiplier on root-to-tip residual) | TreeTime convention; tighter for outlier-sensitive analyses |
| Gubbins input | core.full.aln (full positions, NOT core.aln) |
Cannot estimate background SNP density from variable positions only |
| IQ-TREE ascertainment bias correction | +ASC for SNP-only alignment |
IQ-TREE convention |
| S. pneumoniae core clock (recombination-masked) | ~1.5e-6 subs/site/year | Croucher et al Science 2013 literature; >5e-6 indicates unmasked recombination |
| SARS-CoV-2 clock rate (ancestral) | ~8e-4 subs/site/year | SARS-CoV-2 substitution-rate literature (varies by lineage) |
| M. tuberculosis clock rate | ~0.3-0.5 SNPs/genome/year | Walker 2013 Lancet Infect Dis 13:137 literature (varies by lineage) |
Common Errors
| Error / symptom | Cause | Solution |
|---|---|---|
| Clock rate 3-5x literature | Unmasked recombination | Run Gubbins / ClonalFrameML first |
| BEAST chain stuck at single tree topology | Operator-weight imbalance or extreme prior | Check operator schedule; relax prior |
treetime --clock-filter False rejected |
--clock-filter takes a numeric SD multiplier |
Pass a number (typically 4) or omit |
beast --threads 4 rejected |
BEAST uses single-dash flags | -threads 4 |
| BDSKY origin same as rootHeight | Tutorial confusion | Set origin > rootHeight per Stadler 2013 |
| MASCOT migration HPD spans 2+ orders | <20 seqs per deme | Pool demes; accept uncertainty |
| Single-chain ESS reported as proof of convergence | ESS necessary but not sufficient | Run multi-chain; combine post-overlap |
run_gubbins.py core.aln (not core.full.aln) |
Variable-only alignment | Use core.full.aln; cite Croucher 2015 |
| UShER branch lengths fed directly to BDSKY | Parsimony branch lengths biased low | Re-estimate via TreeTime or BEAST first |
| BEAST2 XML breaks on minor-release upgrade | XML fragile across versions | Pin BEAST + every BEAST2 package version |
--reroot best produces implausible root |
Sampling-biased outliers | Cross-check with TempEst; consider --reroot oldest or manual root |
Anticipated Reviewer Pushback
| Pushback | Response |
|---|---|
| "Was temporal signal checked?" | TempEst R^2 reported; date-randomisation test run; both interpreted (date-randomisation can pass with narrow windows) |
| "Was recombination masked?" | Gubbins on core.full.aln; rebuilt tree on masked alignment; cite Croucher 2015 |
| "Why BDSKY and not BSP?" | BICEPS / BDSKY model sampling explicitly; BSP / Skygrid assume uniform sampling, biased under preferential surveillance |
| "How many BEAST chains?" | 3-4 independent chains; marginal posterior overlap checked; logcombiner only after agreement |
| "Was MASCOT or DTA used for migration?" | MASCOT (or MASCOT-GLM with covariates); DTA inherits Lemey 2009 sampling bias for source attribution |
| "BDSKY origin vs rootHeight?" | origin > rootHeight by Stadler 2013 convention; documented |
| "Is the clock rate consistent with the literature?" | Compared to species-specific reference; recombination-masked S. pneumoniae expected ~1.5e-6 |
| "Was the case-based R_t reconciled?" | Reported both; disagreement attributed to sampling bias (phylodynamic is lineage-specific; case data is population mean) |
References
- Stadler T, Kühnert D, Bonhoeffer S, Drummond AJ (2013) Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV). Proc Natl Acad Sci USA 110(1):228-233. doi:10.1073/pnas.1207965110
- Sagulenko P, Puller V, Neher RA (2018) TreeTime: maximum-likelihood phylodynamic analysis. Virus Evol 4(1):vex042. doi:10.1093/ve/vex042
- Rambaut A, Lam TT, Carvalho LM, Pybus OG (2016) Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol 2(1):vew007. doi:10.1093/ve/vew007
- Duchêne S, Duchêne D, Holmes EC, Ho SY (2015) The performance of the date-randomization test in phylogenetic analyses of time-structured virus data. Mol Biol Evol 32(7):1895-1906. doi:10.1093/molbev/msv056
- Ramsden C, Holmes EC, Charleston MA (2009) Hantavirus evolution in relation to its rodent and insectivore hosts: no evidence for codivergence. Mol Biol Evol 26(1):143-153. doi:10.1093/molbev/msn234
- Bouckaert R, Vaughan TG, Barido-Sottani J et al (2019) BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol 15(4):e1006650. doi:10.1371/journal.pcbi.1006650
- Bouckaert RR (2022) An Efficient Coalescent Epoch Model for Bayesian Phylogenetic Inference. Syst Biol 71(6):1549-1560. doi:10.1093/sysbio/syac015
- Müller NF, Rasmussen DA, Stadler T (2018) MASCOT: parameter and state inference under the marginal structured coalescent approximation. Bioinformatics 34(22):3843-3848. doi:10.1093/bioinformatics/bty406
- Lemey P, Rambaut A, Drummond AJ, Suchard MA (2009) Bayesian phylogeography finds its roots. PLoS Comput Biol 5(9):e1000520. doi:10.1371/journal.pcbi.1000520
- De Maio N, Wu CH, O'Reilly KM, Wilson D (2015) New routes to phylogeography: A Bayesian structured coalescent approximation. PLoS Genet 11(8):e1005421. doi:10.1371/journal.pgen.1005421
- Volz EM, Frost SDW (2014) Sampling through time and phylodynamic inference with coalescent and birth-death models. J R Soc Interface 11(101):20140945. doi:10.1098/rsif.2014.0945
- Croucher NJ, Page AJ, Connor TR et al (2015) Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res 43(3):e15. doi:10.1093/nar/gku1196
- Didelot X, Wilson DJ (2015) ClonalFrameML: Efficient inference of recombination in whole bacterial genomes. PLoS Comput Biol 11(2):e1004041. doi:10.1371/journal.pcbi.1004041
- Mostowy R, Croucher NJ, Andam CP et al (2017) Efficient inference of recent and ancestral recombination within bacterial populations. Mol Biol Evol 34(5):1167-1182. doi:10.1093/molbev/msx066
- Didelot X, Croucher NJ, Bentley SD, Harris SR, Wilson DJ (2018) Bayesian inference of ancestral dates on bacterial phylogenetic trees. Nucleic Acids Res 46(22):e134. doi:10.1093/nar/gky783
- Turakhia Y, Thornlow B, Hinrichs AS et al (2021) Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic. Nat Genet 53(6):809-816. doi:10.1038/s41588-021-00862-7
- Minh BQ, Schmidt HA, Chernomor O et al (2020) IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 37(5):1530-1534. doi:10.1093/molbev/msaa015
- Legried B, Terhorst J (2022) A class of identifiable phylogenetic birth-death models. Proc Natl Acad Sci USA 119(35):e2119513119. doi:10.1073/pnas.2119513119
- Featherstone LA, Duchêne S (2023) Decoding the fundamental drivers of phylodynamic inference. Mol Biol Evol 40(6):msad132. doi:10.1093/molbev/msad132
- Walker TM, Ip CLC, Harrell RH et al (2013) Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect Dis 13(2):137-146. doi:10.1016/S1473-3099(12)70277-3
Related Skills
- pathogen-typing - Defines isolates and clusters that feed phylodynamic inference
- transmission-inference - Phylodynamic R_e + transmission tree are complementary
- variant-surveillance - Lineage assignment runs upstream of skygrid / BDSKY by lineage
- phylogenetics/divergence-dating - Calibrated trees for non-pathogen contexts; deeper review of clock models
- phylogenetics/bayesian-inference - BEAST / RevBayes / MrBayes details beyond BDSKY
- phylogenetics/modern-tree-inference - IQ-TREE / RAxML topology before time-scaling
- phylogenetics/tree-io - Tree parsing and format conversion
- comparative-genomics/whole-genome-alignment - Core-genome alignment input for bacterial phylodynamics
- variant-calling/vcf-basics - Per-isolate variant calls feeding core-SNP alignment
- read-alignment/bwa-alignment - Read mapping upstream of variant calling
- data-visualization/multipanel-figures - Skyline / R_e trajectory plotting
- workflows/somatic-variant-pipeline - End-to-end orchestration patterns