Estimates time-scaled phylogenies, molecular-clock rates, effective reproduction number R_e, and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict/relaxed clocks; coalescent, Bayesian-Skyline, Skygrid, Birth-Death-Skyline, and sampled-ancestor priors; structured coalescent via MASCOT). Covers root-to-tip clock QC via TempEst, date-randomisation tests, recombination masking via Gubbins/ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction, multi-chain convergence diagnostics, and reconciling phylodynamic R_e with case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline models, diagnosing temporal-signal failure, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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 -version; packagemanager -list for BEAST2 packages and versionspip show treetime; help(treetime.TreeTime)packageVersion('bdskytools'); ?bdskytools::bdskytools_plotgubbins --version; iqtree --version; clonalframeml --versionIf 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.
"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.
treetime --tree raw.nwk --aln aln.fasta --dates dates.tsv --coalescent skyline --clock-filter 4 -- fast ML phylodynamicsgubbins --prefix gubbins core.full.aln -- recombination masking before bacterial clock inferencebdskytools::bdskytools_plot for BDSKY post-processing; BactDating for fast Bayesian dating after GubbinsThe 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.
| 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) |
| 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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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."
| 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 |
| 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) |
| 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 |
| 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) |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
日本語の概要は準備中です。原文の説明を表示しています。
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
日本語の概要は準備中です。原文の説明を表示しています。
Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
日本語の概要は準備中です。原文の説明を表示しています。
Unified analysis pipeline for affinity-based proteomics platforms — Olink (PEA, NPX) and SomaLogic SomaScan (SOMAmer, RFU). Platform-aware QC, normalisation, differential abundance, volcano plots, heatmaps, and PCA.
日本語の概要は準備中です。原文の説明を表示しています。