Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
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name
bio-comparative-genomics-ancestral-reconstruction
description
Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
Before using code patterns, verify installed versions match. If versions differ:
R: packageVersion('corHMM') then ?ancRECON, ?make.simmap, ?ace
CLI: codeml (no --version; check paml -h or examine Phylip.tre example), iqtree2 --version, revbayes --version
Python: pip show biopython; check Bio.Phylo.PAML.codeml API
If code throws AttributeError, ImportError, missing slot errors on R S4 objects, or PAML mlc parsing failures, introspect the installed package (? in R, help() in Python) and adapt the example rather than retrying. PAML output formats are stable across 4.9 -> 4.10; IQ-TREE's --ancestral flag replaced -asr in v2.0+.
Ancestral State Reconstruction
"What did this gene / trait / genome look like at an internal node?" -> Choose the reconstruction framework that matches the data class (sequence / discrete trait / continuous trait / gene content) and the inference question (point estimate vs full posterior; marginal vs joint vs scaled-conditional). The single most common mistake is reconstructing under a site-independent or trait-stationary model when the underlying biology demands a hidden-rate or epistatic model -- the resulting "ancestor" is mathematically optimal under the wrong model and is silently wrong (Beaulieu & O'Meara 2016 Syst Biol 65:583; Boyko & Beaulieu 2021 MEE 12:468).
Ancestral gene content (presence/absence): ape::ace(type='discrete', model='ARD'); Dollo parsimony in phangorn; ALE/GeneRax for full DTL (see [[gene-tree-species-tree-reconciliation]])
Algorithmic Taxonomy
Framework
Data class
Inference
Strength
Fails when
ML marginal (codeml RateAncestor=1; IQ-TREE --ancestral)
Sequence / discrete
Site-by-site MAP + posterior
Per-site uncertainty; fast (Yang 1995 Genetics 141:1641; Pupko 2000 MBE 17:890)
Handles indel evolution explicitly; supports protein engineering
Slower; limited model families
Methodology evolves; verify the latest corHMM / phytools vignettes before locking on a single approach. For continuous-trait macroevolution, consult Cooper 2016 model-adequacy reviews.
Decision Tree by Experimental Scenario
Scenario
Recommended method
Why
Protein resurrection (~50-500 Myr divergences)
IQ-TREE2 --ancestral + GRASP indel reconstruction
Per-site marginal probabilities for alt-construct design; GRASP fixes indel ambiguity that PAML treats as missing data
Codon-level sequence ASR with selection inference
PAML codeml RateAncestor=1, model M0 (single omega), seqtype=1
Codon model native; integrates with branch reconstruction; produces rst with BEB-style site probs
Deep eukaryote / archaeal ASR (> 1 Bya)
Bayesian (RevBayes / PhyloBayes-MPI CAT-GTR)
Site-heterogeneous CAT model corrects compositional LBA (Szánthó 2023 Syst Biol 72:767); ML site-homogeneous models fail at this depth
Relative evolutionary rates linked to a binary or categorical phenotype
Per-Tool Failure Modes
Long-branch attraction (LBA) at deep nodes
Trigger: Tree with two long terminal branches separated by a short internal branch; mixed amino-acid compositions across taxa.
Mechanism: Site-homogeneous models (LG, WAG, JTT) assume constant amino-acid equilibrium frequencies across the tree. When real compositions differ (e.g. thermophiles vs mesophiles), models underestimate the probability of convergent substitutions at compositionally-constrained sites, producing apparent shared derived states between long branches (Szánthó 2023 Syst Biol 72:767). The reconstructed ancestor at the deep node is biased toward whichever long-branch composition the model favors.
Symptom: Bootstrap support at the contested node remains high under site-homogeneous models but collapses under CAT-GTR / CAT-PMSF. Posterior predictive checks for compositional homogeneity reject the model (Foster 2004 Syst Biol 53:485).
Fix: Move to PhyloBayes-MPI with CAT-GTR or IQ-TREE2 with CAT-PMSF (-m LG+C60+F+R then -ft <tree> for posterior mean site frequencies). For ASR specifically, use ancestral reconstruction only when the model passes compositional adequacy. Slow-fast site removal (recoded amino acids; Susko & Roger 2007 MBE 24:2139) is an alternative.
Epistasis breaking site-independent ASR
Trigger: Multiple sites in the same protein evolve under coupled constraints (compensatory pairs in RNA secondary structure; buried-residue covariance; allosteric networks).
Mechanism: ML/Bayesian ASR assumes sites are independent given the tree; the joint ancestral sequence is the product of per-site posteriors. Real proteins evolve through compensatory substitutions where a destabilizing mutation at site i is compensated by a mutation at site j (Pollock 2012 PNAS 109:E1352; Shah 2015 PNAS 112:E3226). The ML ancestral sequence can contain a never-tested combination of states.
Symptom: The reconstructed protein fails to fold or is non-functional when expressed; positions flagged ambiguous (P < 0.9) are non-random and cluster in 3D space when mapped to structure.
Fix: Use GRASP indel-aware reconstruction; design 4-8 alternative constructs varying ambiguous positions (P < 0.9), prioritizing residues that are structurally coupled to high-confidence ML states; experimentally test each construct; report the range of functional reconstructions, not the single ML sequence. Hochberg & Thornton 2017 Annu Rev Biophys 46:247 review epistasis strategies.
Root placement error cascading to deep ancestors
Trigger: Trees rooted by midpoint, outgroup with very long branch, or --prefix auto-root.
Mechanism: Marginal ASR posteriors at internal nodes depend on the root's position because the root defines the time direction of substitution. A wrong root flips state inferences for deep nodes (especially when ancestral state is asymmetric, e.g. presence -> absence is more common than reverse).
Symptom: Re-rooting the tree changes the inferred ancestral state at the deepest node by > 0.2 posterior probability; STRIDE rooting (Emms 2017 MBE 34:3267) disagrees with outgroup rooting.
Fix: Run ASR over a set of candidate roots; report robust nodes (state invariant) and root-sensitive nodes separately. For phylogenomic-scale data, use STRIDE / MAD rooting (Tria 2017 Nat Eco Evo 1:0193) or ALE-rooting (Williams 2017 PNAS 114:E4602) and document the rooting strategy.
BiSSE false-positive in state-dependent diversification
Trigger: Testing whether a discrete trait influences speciation/extinction using BiSSE (Maddison 2007 Syst Biol 56:701).
Mechanism: BiSSE attributes ALL rate variation to the focal trait. When real diversification heterogeneity is caused by a hidden character correlated with the focal trait, BiSSE reports a spurious significant association (Rabosky-Goldberg 2015 Syst Biol 64:340 -- ~40% Type-I rate at moderate trees).
Symptom: BiSSE LRT highly significant but biological mechanism unclear; HiSSE rejects BiSSE in favor of a hidden-state model with the focal trait neutral.
Fix: Run HiSSE as the required null model (Beaulieu & O'Meara 2016 Syst Biol 65:583). Report BiSSE only if HiSSE-null is rejected. For traits with deep clade structure, use FiSSE (Rabosky & Goldberg 2017 Evolution 71:1432) which is robust to model misspecification by design.
Parsimony vs ML on asymmetric rates (Felsenstein bias)
Trigger: Trait has strongly asymmetric forward vs reverse rates (e.g. gene loss > gene gain).
Mechanism: Parsimony minimizes total changes, implicitly assuming symmetric rates. ML/Bayesian methods estimate the rate matrix from the data and reconstruct accordingly. Under strong asymmetry, parsimony over-reconstructs the rarer state at ancestors (Cunningham 1999 Syst Biol 48:665).
Symptom: Parsimony and ML reconstructions disagree at > 30% of nodes; ML rates fit AIC-better with ARD (all-rates-different) than ER (equal-rates).
Fix: Always run ER vs SYM vs ARD model comparison via ape::ace(model='...') AIC; use ARD when asymmetry is supported. For gene-content evolution, Dollo parsimony (gain rare, loss common) is often the better prior than equal-rates ML.
Continuous-trait BM-only model with non-BM evolution
Trigger: Fitting phytools::fastAnc() (which assumes BM) to a trait with strong directional or stabilizing selection.
Mechanism: fastAnc returns the BM-MLE ancestral state, which is a weighted mean of descendant values with weights from the BM covariance matrix. If the trait evolved under OU (stabilizing), real ancestor values were closer to the optimum than fastAnc returns; if under EB (early burst), real ancestors were more variable than fastAnc returns.
Symptom: BM model fits with geiger::fitContinuous(model='BM') give AIC > 4 above OU or EB; phylogenetic signal Pagel's lambda < 0.5; Blomberg's K significantly < 1 (Blomberg 2003 Evolution 57:717).
Fix: Run fitContinuous with multiple models (BM/OU/EB/lambda/kappa/delta); use the best AIC model's ancestral reconstruction. For OU, use OUwie::ace(); for regime shifts, bayou::bayou.mcmc. Always report Pagel's lambda alongside ancestral estimates as a phylogenetic-signal indicator. Boettiger 2012 Evolution 66:2240 and Cooper 2016 Biol J Linn Soc 118:64 detail model-adequacy testing.
Alignment error propagating to ASR
Trigger: Using MUSCLE or default MAFFT alignment on highly diverged sequences (< 30% identity).
Mechanism: Misaligned columns place non-homologous residues into the same column. ML ASR treats those residues as states of the same character, producing impossible ancestral inferences (Vialle 2018 MBE 35:1783).
Symptom: Ambiguous regions of the alignment correspond to low-confidence ASR sites; gappy columns dominate the low-confidence set; alignment scoring (TCS, Guidance2) marks the same regions as poorly aligned.
Fix: Filter alignment with HmmCleaner (Di Franco 2019 BMC Evol Biol 19:21) or PREQUAL (Whelan et al 2018 Bioinformatics 34:3929) before ASR. Segment-level filtering outperforms block-level filtering (Gblocks, trimAl) for downstream evolutionary inference. For ASR specifically, mask ambiguous columns (treat as missing) rather than removing them, to preserve coordinates.
Quantitative Thresholds
Quantity
Threshold
Source / Rationale
ASR site high confidence
posterior >= 0.95
Standard convention (Yang 1995); above this treat state as fixed
ASR site moderate confidence
0.80 <= posterior < 0.95
Worth alternative-construct testing in resurrection studies
ASR site ambiguous
posterior < 0.80
Design alternative constructs; cluster against structure
Pagel's lambda interpretation
lambda > 0.7 strong signal; 0.3-0.7 moderate; < 0.3 weak (ad-hoc operational convention; Pagel 1999 introduced lambda but did not prescribe these cutoffs)
Pagel 1999 Nature 401:877 (method); community convention (thresholds)
Goal: Reconstruct ancestral codon or protein states at internal nodes using ML under a stationary codon/protein model.
Approach: Build a codon-aware alignment (PRANK or MACSE) -> infer rooted ML tree with the same model intended for ASR -> create codeml control file with RateAncestor=1 -> run codeml -> parse rst for per-site posteriors and ancestral sequences.
'''PAML codeml ancestral reconstruction with site-confidence parsing'''import subprocess
import re
import os
from collections import defaultdict
defwrite_codeml_ctl(alignment, tree, out_dir, seqtype='codon', model='M0'):
'''Write codeml control file for ancestral reconstruction.
seqtype: 1=codon, 2=aa, 3=codon translated
For protein resurrection use seqtype=2 with model=3 (empirical+gamma).
RateAncestor=1 produces rst file with per-site posteriors.
'''if seqtype == 'codon':
ctl = f'''
seqfile = {alignment}
treefile = {tree}
outfile = {out_dir}/mlc
runmode = 0
seqtype = 1
CodonFreq = 2
model = 0
NSsites = 0
kappa = 2
fix_omega = 0
omega = 0.4
RateAncestor = 1
cleandata = 0
getSE = 1
'''else:
ctl = f'''
seqfile = {alignment}
treefile = {tree}
outfile = {out_dir}/mlc
runmode = 0
seqtype = 2
model = 3
aaRatefile = lg.dat
alpha = 0.5
ncatG = 4
RateAncestor = 1
cleandata = 0
'''
ctl_path = os.path.join(out_dir, 'codeml.ctl')
open(ctl_path, 'w').write(ctl)
return ctl_path
defparse_rst_posteriors(rst_file):
'''Parse rst file. Returns per-node, per-site (state, prob) records.
PAML rst section headers: "Prob distribution at node X" and "List of extant and reconstructed sequences".
'''
text = open(rst_file).read()
node_posteriors = defaultdict(list)
for block in re.finditer(r'Prob distribution at node (\d+)(.*?)(?=Prob distribution at node|\Z)', text, re.DOTALL):
node = int(block.group(1))
for ln in block.group(2).splitlines():
m = re.match(r'\s*(\d+)\s+\S+:\s*(\w)\((\d\.\d+)\)', ln)
if m:
site, state, prob = int(m.group(1)), m.group(2), float(m.group(3))
node_posteriors[node].append({'site': site, 'state': state, 'prob': prob})
return node_posteriors
defsummarize_node_confidence(node_posteriors, ambig_cutoff=0.8):
'''Per-node confidence summary for protein-resurrection construct design.'''
summary = {}
for node, sites in node_posteriors.items():
probs = [s['prob'] for s in sites]
n_high = sum(p >= 0.95for p in probs)
n_amb = sum(p < ambig_cutoff for p in probs)
summary[node] = {
'mean_post': sum(probs) / len(probs),
'frac_high': n_high / len(probs),
'n_ambiguous': n_amb,
'ambiguous_sites': [s['site'] for s in sites if s['prob'] < ambig_cutoff]
}
return summary
IQ-TREE2 Marginal Reconstruction
Goal: Faster ASR with native model selection, for protein/DNA alignments where PAML is too slow.
Approach: Run IQ-TREE with -m TEST to pick model -> --ancestral produces .state table of per-site posteriors; output rooted by -o <outgroup>.
iqtree2 -s alignment.fasta -m MFP --ancestral -o outgroup_taxon -B 1000 -nt AUTO --prefix asr_iqtree
# .state columns: Node | Site | State | p_A | p_C | p_G | p_T (DNA)# or : Node | Site | State | p_A p_R p_N ... (protein)
import pandas as pd
defload_iqtree_state(state_file):
'''Returns dict[node] -> DataFrame[site, state, max_post, all_post].'''
df = pd.read_csv(state_file, sep='\t', comment='#')
df.columns = [c.strip() for c in df.columns]
state_cols = [c for c in df.columns if c.startswith('p_')]
df['max_post'] = df[state_cols].max(axis=1)
return df.groupby('Node')
Stochastic Mapping (phytools::make.simmap)
Goal: Sample full character histories along branches for a discrete trait; quantify ancestral state probabilities with proper uncertainty.
Approach: Fit Mk rate matrix -> sample nsim simulated maps under the posterior -> summarize state probabilities per node and transitions per branch.
library(phytools)
library(corHMM)
tree <- read.tree('species_tree.nwk')
traits <- read.csv('traits.csv', row.names =1)
x <- setNames(traits$state, rownames(traits))# Fit Mk model and pick ARD vs SYM vs ER by AIC
fit_er <- fitMk(tree, x, model ='ER')
fit_sym <- fitMk(tree, x, model ='SYM')
fit_ard <- fitMk(tree, x, model ='ARD')
aic <- sapply(list(fit_er, fit_sym, fit_ard), AIC)
best_model <-c('ER','SYM','ARD')[which.min(aic)]# Stochastic mapping under best model, nsim >= 1000 (raise for asymmetric rates)
smaps <- make.simmap(tree, x, model = best_model, nsim =1000,pi='fitzjohn')
node_pp <- summary(smaps)$ace # posterior probabilities per state per node# Hidden-rate alternative for clade-rate heterogeneity
hmm_fit <- corHMM(phy = tree, data = data.frame(species =names(x), trait = x),
rate.cat =2, model ='ARD', node.states ='marginal')
hmm_fit$states # marginal ancestral state matrix (rows: nodes)
pi='fitzjohn' uses the Fitzjohn 2009 Syst Biol 58:595 root prior (root-state prior weighted by the likelihood of the observed tip data given each root state), preferable to pi='estimated' which fixes the prior to the estimated equilibrium distribution and can over-fit, or pi='equal' which can bias toward the rarer state when data are asymmetric.
Continuous-Trait ASR with Model Adequacy
Goal: Reconstruct ancestral values for a continuous trait while honestly reporting which model class the data support.
Approach: Fit BM/OU/EB/lambda models -> AIC compare -> reconstruct under best model -> report Pagel's lambda and Blomberg's K as signal quality.
library(phytools); library(geiger)
trait <- setNames(traits$mass, rownames(traits))
tree_p <- multi2di(tree)# phytools/geiger require fully bifurcating
models <-c('BM','OU','EB','lambda','kappa','delta')
fits <- lapply(models,function(m) fitContinuous(tree_p, trait, model = m))
aic_tbl <- data.frame(model = models, AIC = sapply(fits,function(f) f$opt$aic))
best <- aic_tbl$model[which.min(aic_tbl$AIC)]# Phylogenetic signal
lambda_fit <- phylosig(tree_p, trait, method ='lambda', test =TRUE)
K_fit <- phylosig(tree_p, trait, method ='K', test =TRUE)if(best =='BM'){
asr <- fastAnc(tree_p, trait, CI =TRUE)# BM ML ancestral with 95% CI}elseif(best =='OU'){# Use OUwie for proper OU ancestral with regime
library(OUwie)
df <- data.frame(species =names(trait), regime =rep(1,length(trait)), trait = trait)
asr_ou <- OUwie.anc(OUwie(tree_p, df, model ='OU1'), data = df)}else{# phytools::contMap can use lambda/EB rescaled tree
asr_contmap <- contMap(tree_p, trait, method ='fastAnc', plot =FALSE)}
Report Pagel's lambda alongside ancestral values: lambda > 0.7 indicates BM-like, signal supports tree-based reconstruction; lambda < 0.3 indicates ecology rather than phylogeny drives variance and ASR is poorly constrained.
GRASP Indel-Aware Sequence ASR (Protein Resurrection Workflow)
Goal: Reconstruct ancestral protein sequence INCLUDING indel states, for experimental resurrection.
Approach: GRASP (Foley 2022 PLoS Comp Biol 18:e1010633) uses a partial-order alignment graph to model indels probabilistically; outputs alternative reconstructions and explicit indel uncertainty.
grasp -aln alignment.fasta -tree species.nwk -out grasp_run --inference joint --threads 8
# Produces:# grasp_run/asr.fasta ancestral sequences at every internal node# grasp_run/asr_posterior per-site, per-state posterior# grasp_run/asr_indels indel posterior per node per gap-block
Construct-design protocol: (1) Take ML sequence as primary construct. (2) For each site with max posterior < 0.8 and a second state with posterior > 0.2, build a single-mutant alternative. (3) For each indel block with posterior < 0.8, build a present and an absent alternative. (4) Order constructs by structural compactness (avoid surface loops first). Typical batch: 4-8 constructs per ancestral node. Hochberg & Thornton 2017 Annu Rev Biophys 46:247 review the operational pipeline.
Reconciliation: When Methods Disagree
Pattern
Likely cause
Action
Parsimony and ML disagree at > 30% nodes
Strongly asymmetric rates
Trust ML with ARD model after AIC support; check Felsenstein 1978 LBA zone
BiSSE highly significant, HiSSE neutral
Hidden trait drives diversification
Report HiSSE as primary (Rabosky-Goldberg 2015)
Codeml marginal and joint disagree at deep node
Strong epistasis or model misspecification
Test alternative constructs; consider GRASP and BAli-Phy joint inference
Site-homogeneous LG agrees with PhyloBayes CAT-GTR at all nodes
No deep compositional heterogeneity
Site-homogeneous is fine; report both as confirmation
Site-homogeneous LG and CAT-GTR disagree at deep node
Compositional LBA
CAT-GTR is correct; report CAT-PMSF for fast follow-up
Marginal and stochastic-map states disagree
Asymmetric rate matrix; root prior matters
Use pi='fitzjohn'; trust stochastic map for asymmetric cases
BM ASR vs OU ASR disagree on direction of trait change at root
Trait under stabilizing selection
OU model is correct if AIC supports; reconstruct toward the optimum, not the BM weighted mean
Reconstruction at deepest node flips under re-rooting
Insufficient outgroup support; LBA
Run STRIDE / MAD rooting; report deep-node state with uncertainty
GRASP indel and PAML "missing-data" reconstructions disagree
PAML ignores indels
GRASP is correct for protein resurrection; PAML codon-only is fine for selection analysis
Operational rule for publication: Reconstruct under multiple models (at minimum: ER/SYM/ARD for discrete; BM/OU/EB for continuous; site-homogeneous + CAT-PMSF or PhyloBayes for sequence ASR at deep nodes); report only nodes whose state is invariant across models OR explicitly flag model-sensitive nodes. Single-model claims should be downgraded.
Cohort Gotchas
Polyploid species in continuous-trait analyses: body size, genome size, gene count are confounded with ploidy; assign subgenomes (see [[whole-genome-duplication]]) and treat as separate tips, or use multilabel-tree methods.
Hybrid taxa break the bifurcating-tree assumption: ape and phytools assume strict bifurcations; for hybrids, use phylogenetic networks (phangorn, RevBayes admixture) or remove hybrid tips before ASR.
Tip-dated trees from molecular clock require careful root prior: RevBayes / BEAST2 with calibrated tip dates produce trees in absolute time; PAML/IQ-TREE work in relative substitutions. Match the time unit when integrating across tools.
OrthoFinder species trees from gene-tree summary (STAG): branch lengths are coalescent-units when used for ASR; convert to substitutions via concatenated alignment if downstream tools require it.
Anticipated Reviewer Pushback
Pushback
Standard response
"Why marginal not joint reconstruction?"
Marginal exposes per-site uncertainty necessary for resurrection construct design; joint is internally consistent but hides ambiguity (Pupko 2000 MBE 17:890)
"How was epistasis handled?"
Designed and tested N alternative constructs at ambiguous (P < 0.8) sites; report functional range, not just ML sequence (Hochberg & Thornton 2017)
"Why these models?"
AIC compared ER/SYM/ARD for discrete; BM/OU/EB/lambda for continuous; site-homogeneous + CAT-PMSF for deep sequence ASR; reported ancestral state only at model-invariant nodes
"Phylogenetic signal?"
Pagel's lambda = X; Blomberg's K = Y; signal supports tree-based ASR (or: signal weak, ASR exploratory only)
"Effect of rooting?"
Reconstructed under multiple rootings; state at root invariant across STRIDE / MAD / outgroup, or explicit caveat for root-sensitive nodes
"Multiple-testing across nodes?"
Ancestral states are estimated quantities, not tested hypotheses; report posteriors per node, no multiplicity correction needed
"Why not BiSSE for trait-diversification correlation?"
HiSSE replaces BiSSE per Rabosky-Goldberg 2015; BiSSE Type-I rate ~40% on simulated data
Common Errors
Error / symptom
Cause
Solution
codeml rst file missing posteriors section
RateAncestor set to 0 or not set
Set RateAncestor=1 in control file
IQ-TREE --ancestral empty .state file
Tree not rooted
Specify outgroup with -o; IQ-TREE requires rooting for ancestral output
make.simmap chains never mix
Asymmetric rate matrix with sparse data
Increase nsim to 5000+; switch root prior to pi='fitzjohn'
ape::ace fails with NA/NaN/Inf in foreign function call
Polytomies in tree
multi2di() to resolve to bifurcating; or use ape::ace(method='ML') with phytools
GRASP runs but produces empty asr.fasta
Alignment includes stop codons or X characters
Strip non-canonical residues; check that the alignment passes Bio.SeqIO validation
fitContinuous(model='OU') returns lambda = 0
OU collapsed to white noise (no signal)
Trait variance unexplained by tree; reconsider whether continuous-trait ASR is meaningful
Stochastic mapping returns all-or-nothing at deep nodes
Insufficient signal; pi='equal' default
Use pi='fitzjohn'; check that taxa span both states
HiSSE convergence failures
bound.par too restrictive; hessian singular
Use starting.vals=NULL, restart with output.liks=TRUE; switch to BiSSE-ness (Magnuson-Ford & Otto 2012 Am Nat 180:225) as fallback
codeml omega = 0 or 999 at branch
Saturation or alignment artifact
Increase model complexity (M0 -> M3); check dS at branch; if dS > 3, ASR unreliable on that branch
Ancestral genome content reconstruction inflated
Assembly fragmentation produces false absences
Use BUSCO-completeness-corrected presence/absence; or run [[gene-tree-species-tree-reconciliation]] which handles loss-vs-missing
Tool Installation Notes
# CLI
conda install -c bioconda paml iqtree
# RevBayes via source build (https://revbayes.github.io/download) or homebrew on macOS# GRASP from https://github.com/bodenlab/GRASP (Java)# FastML web at fastml.tau.ac.il or CLI binary# R
install.packages(c('ape', 'phytools', 'geiger', 'corHMM', 'phangorn', 'OUwie', 'bayou', 'RERconverge'))
remotes::install_github('thej022214/hisse')
# Python
pip install biopython ete4
For protein resurrection workflows, GRASP is the modern standard; for selection-context codon ASR, PAML remains the reference; for trait macroevolution, phytools + corHMM + OUwie are the working tier; for Bayesian rigor with model uncertainty, RevBayes is required.
References
Yang Z et al 1995 Genetics 141:1641 (marginal ASR likelihood framework)
Pupko T et al 2000 MBE 17:890 (joint ASR efficient algorithm)
Nielsen R 2002 Syst Biol 51:729 (stochastic mapping)
Huelsenbeck JP et al 2003 Syst Biol 52:131 (Bayesian stochastic mapping)
Felsenstein J 1985 Am Nat 125:1 (phylogenetic independent contrasts)
Felsenstein J 2012 Am Nat 179:145 (threshold model)
Pagel M 1999 Nature 401:877 (lambda phylogenetic signal)
Blomberg SP et al 2003 Evolution 57:717 (K statistic)
Beaulieu JM et al 2013 Syst Biol 62:725 (hidden Markov state-rate decoupling)
Beaulieu JM & O'Meara BC 2016 Syst Biol 65:583 (HiSSE)
