bio-phylo-modern-tree-inference

name	bio-phylo-modern-tree-inference
description	Infers maximum-likelihood phylogenetic trees with IQ-TREE2 and RAxML-NG -- model selection (ModelFinder), branch support (UFBoot2, SH-aLRT), concordance factors (gCF/sCF), partitioning, topology tests, and long-branch-attraction control. Covers why an ML tree inherits every flaw of the assumed model and the fixed alignment, why reported support measures repeatability under resampling and not correctness, why UFBoot uses a >=95 cutoff and not the bootstrap-70 rule, and why a node with UFBoot 100 but gCF ~35 is essentially unresolved ILS rather than a clade. Use when inferring an ML tree, selecting a substitution or partition model, choosing or interpreting support measures, testing an a-priori topology, or diagnosing LBA. Routes model-free distance trees to distance-calculations, posteriors to bayesian-inference, and species trees under ILS to species-trees.
origin	openai4s
category	bioskills/phylogenetics
metadata	{"tool_type":"cli","primary_tool":"IQ-TREE2","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: IQ-TREE 2.2+ / 2.3+, RAxML-NG 1.2+.

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

• CLI: iqtree2 --version then iqtree2 --help to confirm flags
• CLI: raxml-ng --version then raxml-ng --help to confirm flags

If code throws an unrecognized-argument or model-parse error, introspect the installed tool and adapt the example to match the actual API rather than retrying.

IQ-TREE2 uses single-dash documented forms (-alrt, -bnni, -B); -B/-T are v2.x (v1.x used -bb/-nt). Do NOT write --alrt. The likelihood site-concordance flag --scfl requires IQ-TREE 2.2.2+ (older builds have only the parsimony --scf).

Modern ML Tree Inference -- ML Support Measures Repeatability, Not Correctness

"Build a maximum-likelihood tree with support from my alignment" -> Select a substitution model, search topology and branch lengths that maximize the likelihood, then attach support that quantifies repeatability and concordance that quantifies genealogical agreement.

• CLI: iqtree2 -s aln.fasta -m MFP -B 1000 -bnni -alrt 1000 (model selection + dual support, all built in)
• CLI: raxml-ng --all --msa aln.fasta --model GTR+G --bs-metric fbp,tbe (very large trees, transfer bootstrap, precise branch lengths)

Scope: ML estimation of topology, branch lengths, model selection, branch support, concordance factors, partitioning, topology tests, and LBA control. Model-free distance/NJ trees and distance correction -> distance-calculations. Posterior distributions, MCMC, and CAT-GTR -> bayesian-inference. Per-locus gene trees summarized into a species tree under ILS -> species-trees. Time-scaled trees -> divergence-dating.

The Single Most Important Modern Insight

An ML tree is the topology and branch lengths that maximize the likelihood under an ASSUMED substitution model, conditioned on a FIXED multiple-sequence alignment. It inherits every flaw of both: a misaligned column is a fabricated character the model dutifully fits, and a misspecified model biases the point estimate toward a wrong topology that more data only sharpens. The reported support measures REPEATABILITY under resampling, not correctness. Three load-bearing facts:

1. High support is consistent with being wrong. Bootstrap, UFBoot, SH-aLRT, and aBayes all ask whether a branch reappears when the data or tree is perturbed. Under model misspecification every replicate reproduces the same bias, so support climbs toward 100% precisely as the inference becomes more wrong. At genome scale, sampling error vanishes and UFBoot ~100 on most branches is the default, not a signal.
2. More data fixes variance, not bias. Adding sites shrinks sampling error and makes the estimate more confident, but the systematic error from saturation, compositional heterogeneity, and ILS is bias that CONCENTRATES with scale. The cure for confident-wrong trees is better models, better alignments, and better diagnostics -- never more bootstrap replicates.
3. Concordance factors are the honest measure at genome scale. gCF/sCF ask what FRACTION of genes or sites actually contains a branch. A node with UFBoot 100 and gCF 35 means the concatenated likelihood is certain but only ~35% of loci endorse that branch -- biologically unresolved (ILS or introgression), and the bootstrap answered a question nobody should have asked. Report CFs on every phylogenomic tree; treat bootstrap as necessary-not-sufficient.

Tool Taxonomy

Tool	Citation	Role	When
IQ-TREE2	Minh 2020	ML search + ModelFinder + UFBoot2 + SH-aLRT + gCF/sCF + AU test + C60/PMSF, all built in	the default for almost all work
RAxML-NG	Kozlov 2019	ML search, transfer bootstrap, terrace-aware, MPI/checkpointing	very large trees, TBE, precise branch lengths, long HPC runs
PhyML	Guindon 2010	ML search, origin of SH-aLRT	legacy/teaching; SH-aLRT is now in IQ-TREE2
FastTree	Price 2010	approximate ML, single-pass NNI/SPR	a fast first-pass tree on thousands of sequences; not for final support

IQ-TREE2 vs RAxML-NG (both hill-climb the same likelihood; they differ in built-in features and scaling, not correctness):

Need	Use
Model selection, UFBoot2, SH-aLRT, concordance factors, AU test, mixture/PMSF	IQ-TREE2 (built in; nothing else bundles all of this)
Very large trees (thousands of taxa), low memory, MPI	RAxML-NG
Transfer bootstrap (TBE) for rogue-taxon mega-trees	RAxML-NG (--bs-metric tbe)
Most precise branch lengths for downstream dating	RAxML-NG
Robust checkpoint/restart on long runs	RAxML-NG

Common production pattern: model-select, compute CFs, and topology-test in IQ-TREE2; do heavy bootstrap on a mega-tree in RAxML-NG with --bs-metric fbp,tbe.

Model Selection

ModelFinder (Kalyaanamoorthy 2017) scores the substitution matrix and the rate-heterogeneity model jointly, including FreeRate categories the old jModelTest/ProtTest generation never tested, and ranks by BIC (the default; k*ln(n) penalty favors simpler models that generalize on large n).

• -m MFP -- ModelFinder Plus: test all models by BIC, then search with the winner. The standard. (-m MF selects only; avoid -m TEST/-m TESTONLY, the legacy jModelTest-style limited set.)
• +G vs +R. +G (discrete Gamma, one alpha) is the workhorse for single short genes. +R (FreeRate) freely estimates each category's rate AND weight, capturing the non-Gamma, often multimodal rate distributions of concatenated phylogenomic data; expect +R3 to +R6 to win on BIC at scale.
• The +I+G trap. The invariant-sites proportion (+I) and the Gamma shape (+G) describe overlapping rate distributions, so the likelihood surface has a flat ridge: the two estimates are individually near-meaningless and start-dependent. Prefer +R (its slowest category absorbs near-invariant sites); use +G alone unless BIC genuinely demands +I+G.
• Partition model selection. -m MFP+MERGE fits per-partition models AND greedily merges partitions that fit the same model (BIC-chosen scheme, the successor to PartitionFinder greedy). Pair with -rcluster 10 (relaxed clustering: only test the top 10% most-similar pairs) for many partitions.
• Site-heterogeneous mixtures for deep data. Empirical matrices (LG, WAG) assume one residue-frequency vector for the whole alignment; real proteins do not, and that across-site compositional heterogeneity is the chief driver of deep LBA. The ML answer is the C10..C60 profile-mixture series (LG+C60+F+G), made tractable by PMSF (Wang 2018): a guide-tree pass computes one posterior-mean profile per site, and the real search uses those frozen profiles. PMSF is the standard recommendation for deep / LBA-prone protein phylogenomics.

Branch Support -- the Heart

Five measures, three different questions; the cardinal sin is cross-comparing their cutoffs.

Metric	What it perturbs / measures	Strong cutoff	Tool / flag	Failure mode
Standard bootstrap (FBP)	resample sites; clade frequency (binary)	>=70 (folklore)	RAxML-NG --bs-metric fbp; IQ-TREE -b	slow; crushed by rogue taxa in big trees
Ultrafast bootstrap 2 (UFBoot)	RELL-resampled log-Ls; ~unbiased clade prob	>=95 (NOT 70)	IQ-TREE2 -B 1000 (+-bnni)	inflates under model violation -> use -bnni
SH-aLRT	local NNI likelihood ratio (no resampling)	>=80	IQ-TREE2 -alrt 1000	conservative on very short branches
aBayes	posterior from 3 NNI Ls, flat prior	>=0.95	IQ-TREE2 -abayes	anti-conservative; never the sole criterion
Transfer bootstrap (TBE)	gradual transfer distance under resampling	no fixed cutoff; > FBP	RAxML-NG --bs-metric tbe	permissive; "fuzzy" branch identity

UFBoot2 (Hoang 2018) uses the RELL trick (resample site log-likelihoods, reuse a candidate tree set) to run hundreds of times faster than the Felsenstein bootstrap (1985), and its values are CLOSER to unbiased clade probabilities -- which is exactly why the strong-support cutoff is 95, not the conservative-bootstrap 70. Treating UFBoot 70 as "good" is a category error. -bnni re-optimizes each replicate tree by NNI to rein in the inflation that model violation causes; use -B 1000 -bnni routinely. UFBoot is on a DIFFERENT scale from the standard bootstrap -- never read it with the BP-70 rule.

SH-aLRT (Guindon 2010) does not resample data; for each branch it tests whether the ML likelihood beats its two best NNI rearrangements. UFBoot (data perturbation) and SH-aLRT (tree perturbation) have different failure modes, so the community-standard joint criterion requires both:

A branch is strongly supported iff SH-aLRT >= 80% AND UFBoot >= 95%.

For large rogue-taxon-prone trees, the binary Felsenstein bootstrap lets a single wandering tip crush an otherwise-recovered deep branch; transfer bootstrap (TBE, Lemoine 2018, RAxML-NG --bs-metric tbe) replaces the in/out indicator with a gradual transfer distance and rescues those branches, at the cost of being more permissive.

Concordance Factors

Bootstrap quantifies statistical confidence given the concatenated data; concordance factors (Minh 2020) quantify how much of the actual data carries a branch.

• gCF (gene concordance factor): the percentage of decisive single-locus gene trees that contain the exact branch. Needs per-locus gene trees.
• sCF (site concordance factor): the percentage of decisive sites supporting the branch, from sampled quartets; works on a single concatenated alignment with no gene trees.
• sCFL (likelihood sCF, Mo 2023): uses ancestral-state likelihoods rather than parsimony quartet counting, substantially reducing (not abolishing) homoplasy and taxon-sampling bias. Prefer --scfl over the old --scf on IQ-TREE 2.2.2+.

# one gene tree per locus from a directory of locus alignments (-S = separate, no concatenation)
iqtree2 -S loci_dir -m MFP -B 1000 -T AUTO --prefix loci   # -B 1000 = UFBoot per gene tree, needed to contract weak branches before ASTRAL

# gene + likelihood site concordance against a fixed concatenated tree (-te fixes the tree)
iqtree2 -te concat.treefile -s concat.fasta --gcf loci.treefile --scfl 100 -T 4 --prefix concord
#  --gcf loci.treefile   per-locus gene trees for gCF
#  --scfl 100            100 sampled quartets per branch for likelihood sCF (higher = more stable)

Outputs concord.cf.tree (Newick with gCF/sCF labels) and concord.cf.stat (per-branch gCF, gDF1, gDF2, gDFP, sCF). A node with UFBoot 100 but gCF ~35 (gDF1 ~33, gDF2 ~30) is genes split three ways: the concatenated point estimate barely edges the alternatives and the node is biologically unresolved -- the signature of ILS or introgression, not a clade. Report CFs alongside support on every phylogenomic tree.

Topology Tests

For testing an a-priori hypothesis ("can I reject that X and Y are monophyletic?") against the ML tree, by comparing a set of fixed trees. Build the constrained tree with -g constraint.tree, then evaluate both trees:

# trees.nex holds the unconstrained ML tree + the constrained/alternative trees
iqtree2 -s aln.fasta -m <model> -z trees.nex -n 0 -zb 10000 -au --prefix autest
#  -z trees.nex   trees to compare        -n 0   no fresh search, just evaluate
#  -zb 10000      RELL replicates (>=1000) -au    add the AU test (must accompany -zb)

The AU test (Shimodaira 2002) uses multiscale bootstrap resampling to correct both the selection bias of the KH test (invalid on the data-selected ML tree) and the over-conservatism of the SH test (which rejects less as the candidate set is padded). Use AU by default. p-AU < 0.05 means that tree is REJECTED; p-AU >= 0.05 means it is in the 95% confidence set (failure to reject is not acceptance -- weak data fails to reject many trees). Report SH/KH only for completeness.

Partitioning

When splitting an alignment into partitions, the branch-length linkage choice is what people get wrong:

Mode	Flag	Branch lengths	Use when
Edge-equal	-q part.nex	identical across partitions	partitions share rate (rare, restrictive)
Edge-linked proportional	-p part.nex	shared topology, per-partition rate multiplier	DEFAULT -- genes evolve at different speeds, share history
Edge-unlinked	-Q part.nex	fully independent per partition	genuine heterotachy; parameter-hungry, overfits

-p (edge-linked proportional) is the standard: one rate multiplier per partition over a shared topology. Over-partitioning spends degrees of freedom without bias reduction and inflates variance; the antidote is to start fine (gene x codon position) and let -m MFP+MERGE -rcluster 10 find the coarsest BIC-justified scheme. Prefer a merged scheme over a hand-picked maximal one; prefer -p over -Q.

Per-Method Failure Modes

Long-Branch Attraction

Trigger: Two or more independently fast-evolving lineages on long branches separated by a short internode. Mechanism: Convergent/homoplastic substitutions on the long branches look like shared ancestry; a site-homogeneous model cannot separate convergence from homology and groups them -- bias that GROWS with more sites. Symptom: Fast taxa group with the outgroup or each other at 100% bootstrap; the grouping collapses under a better model or when a long-branch taxon is removed. Fix: Site-heterogeneous model (C60/PMSF) first; remove the fastest sites and watch the node; drop the long-branch taxon or use a closer outgroup; cross-check with SR4/Dayhoff recoding. Believe a deep node only when it survives all of these, not when it merely has UFBoot 100 under LG+G.

Model Underspecification

Trigger: A single inadequate model on heterogeneous data; the best site-homogeneous model by BIC still inadequate at depth. Mechanism: Wrong matrix, missing rate heterogeneity, or no partitioning biases the topology while support stays high. Symptom: Biologically implausible nodes with full support that move under a richer model. Fix: -m MFP (+MERGE for multi-locus), FreeRate +R, and at amino-acid depth a C60/PMSF mixture. Best-by-BIC among site-homogeneous models is not sufficient deep in the tree.

Over-Partitioning

Trigger: Hundreds of hand-defined partitions, especially under -Q. Mechanism: Each partition's model is estimated from too little data; parameter and branch-length estimates get noisy without reducing bias. Symptom: Slow runs, noisy estimates, degraded support. Fix: -m MFP+MERGE -rcluster 10 to the coarsest BIC-justified scheme; use -p, not -Q.

The Support-Accuracy Gap

Trigger: UFBoot/bootstrap ~100 everywhere, including implausible or conflicting nodes. Mechanism: Support measures repeatability of a possibly-biased estimate; concatenation pools conflicting gene signals so the likelihood is certain while the loci disagree. Symptom: Full support but gCF ~33 / sCF near its ~33% floor on contested nodes. Fix: Compute gCF/sCFL; treat UFBoot 100 + gCF ~33 as UNRESOLVED; require SH-aLRT >=80 AND UFBoot >=95; use -bnni. If most genes reject the ML resolution, route to a coalescent species tree -> species-trees.

Quantitative Thresholds

Quantity	Threshold	Source
UFBoot strong support	>=95 (NOT the bootstrap 70)	Hoang 2018
SH-aLRT strong support	>=80	Guindon 2010
Joint rule	SH-aLRT >=80 AND UFBoot >=95	community standard
Standard bootstrap "good"	>=70 (different metric, folklore)	Felsenstein 1985 / Hillis 1993
aBayes strong	>=0.95 (anti-conservative; never alone)	Anisimova 2011
Bootstrap replicates	UFBoot -B >=1000; SH-aLRT -alrt >=1000; AU -zb 10000	IQ-TREE docs
gCF reading	>~75 agree; ~50 conflicted; ~33 effective polytomy; <33 with higher alternative = possible wrong resolution	Minh 2020
sCF floor	~33% (three quartet resolutions); ~33 = no site signal	Minh 2020
AU test	p-AU < 0.05 => tree REJECTED	Shimodaira 2002
Model selection	rank by BIC (-m MFP default), k*ln(n) penalty	Kalyaanamoorthy 2017

Common Errors

Error / symptom	Cause	Solution
Unknown argument --alrt	wrote the GNU double-dash form	IQ-TREE2 uses single-dash -alrt, -bnni, -B
-bb / -nt not recognized	v1.x flags on a v2.x binary	use -B (bootstrap) and -T (threads) in 2.x
Reading UFBoot 80 as "supported"	applied the bootstrap-70 rule to a different scale	use UFBoot >=95 AND SH-aLRT >=80
Fully-supported deep node distrusted by reviewer	no concordance factors reported	compute gCF/sCFL; treat high-support/low-CF as unresolved
--scfl unrecognized	IQ-TREE older than 2.2.2	upgrade, or fall back to parsimony --scf
Concatenated tree confidently wrong on a rapid radiation	ILS; concatenation is inconsistent in the anomaly zone	infer per-locus gene trees and a coalescent species tree -> species-trees
AU test "fails to reject" the alternative	weak data, or candidate set padded	do not pad the set; failure to reject is not acceptance

References

Felsenstein J. 1978. Cases in which parsimony or compatibility methods will be positively misleading. Systematic Zoology 27(4):401-410. Felsenstein J. 1985. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39(4):783-791. Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel O. 2010. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Systematic Biology 59(3):307-321. Price MN, Dehal PS, Arkin AP. 2010. FastTree 2: approximately maximum-likelihood trees for large alignments. PLoS ONE 5(3):e9490. Anisimova M, Gil M, Dufayard J-F, Dessimoz C, Gascuel O. 2011. Survey of branch support methods demonstrates accuracy, power, and robustness of fast likelihood-based approximation schemes. Systematic Biology 60(5):685-699. Shimodaira H. 2002. An approximately unbiased test of phylogenetic tree selection. Systematic Biology 51(3):492-508. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. 2017. ModelFinder: fast model selection for accurate phylogenetic estimates. Nature Methods 14(6):587-589. Wang H-C, Minh BQ, Susko E, Roger AJ. 2018. Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation. Systematic Biology 67(2):216-235. Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. 2018. UFBoot2: improving the ultrafast bootstrap approximation. Molecular Biology and Evolution 35(2):518-522. Lemoine F, Domelevo Entfellner J-B, Wilkinson E, Correia D, Davila Felipe M, De Oliveira T, Gascuel O. 2018. Renewing Felsenstein's phylogenetic bootstrap in the era of big data. Nature 556(7702):452-456. Kozlov AM, Darriba D, Flouri T, Morel B, Stamatakis A. 2019. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35(21):4453-4455. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, Lanfear R. 2020. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Molecular Biology and Evolution 37(5):1530-1534. Minh BQ, Hahn MW, Lanfear R. 2020. New methods to calculate concordance factors for phylogenomic datasets. Molecular Biology and Evolution 37(9):2727-2733. Mo YK, Lanfear R, Hahn MW, Minh BQ. 2023. Updated site concordance factors minimize effects of homoplasy and taxon sampling. Bioinformatics 39(1):btac741.

Related Skills

• distance-calculations - model-corrected distances and fast NJ trees as a model-free alternative
• bayesian-inference - posteriors, MCMC convergence, and CAT-GTR site-heterogeneous models
• species-trees - coalescent species-tree estimation when concordance factors reveal ILS
• divergence-dating - time-scaled trees from the ML topology
• tree-manipulation - rooting, pruning, and collapsing low-support nodes
• alignment/alignment-io - the alignment whose homology assumption the ML tree trusts as fixed