Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.
Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.
If code throws branch-site test LRT non-positive, omega2 hit upper bound 999, MEME ML mixed gradient, the most common cause is alignment error or saturated dS -- inspect alignment with TCS / Guidance2 and dS-vs-divergence-time. PAML 4.10 changed several control-file keywords from 4.9 (getSE = 1 syntax tightened).
Positive Selection Analysis
"Is this gene / branch / site under positive selection?" -> dN/dS (omega = nonsynonymous-to-synonymous substitution rate ratio) framework with explicit choice of WHICH question is being asked (gene-wide / branch-specific / site-specific / episodic) and WHICH null is being rejected. The "test failed because of selection" claim has more known confounders than any other comparative-genomics inference; mandatory pre-screens are: recombination (GARD), alignment errors (PREQUAL or HmmCleaner), saturation (dS distribution), and gBGC (W->S substitution bias). Skipping any one inflates Type-I error to ~20-50% (Anisimova & Yang 2007 MBE 24:1219; Pond 2006 Mol Biol Evol 23:1891).
For noncoding elements (CNEs); convergent rate shifts
CDS analyses prefer codon-based methods
phyloP (Pollard 2010 GR 20:110)
Per-site noncoding rate test
--
Simple; widely used for noncoding
No convergence; site-by-site
PRANK + codeml pipeline
Codon-aware MSA + codeml
--
Standard publication-grade workflow
Slow for large datasets
Methodology evolves; verify the latest HyPhy / PAML manuals and the Álvarez-Carretero "Beginner's Guide" (Álvarez-Carretero et al 2023 MBE 40:msad041) before locking on a single method. The BUSTED-MH and FUBAR-MH (multi-hit) extensions specifically address known Type-I inflation from multi-nucleotide substitutions and are now recommended over basic BUSTED / FUBAR.
Decision Tree by Experimental Scenario
Scenario
Recommended approach
Why
Single gene, mammalian (~60 Myr), pre-specified foreground branch
codeml branch-site mod A AND HyPhy aBSREL on foreground
Mutual validation; mod A LRT df=1 + aBSREL adaptive site classes
Single gene, deep eukaryote (~500+ Myr), no foreground hypothesis
GARD pre-screen -> BUSTED-MH gene-wide -> MEME for sites
Episodic-selection-only methods; saturation-aware (HyPhy under MG94 codon model)
Genome-wide scan, vertebrates
codeml M7 vs M8 OR HyPhy FUBAR-MH per gene; FDR-correct
Pervasive-selection sites; multi-hit correction critical at scale
Episodic selection scan
HyPhy MEME genome-wide (per gene); FDR-correct
Site-level episodic detection
Branch-specific selection on unspecified branches
HyPhy aBSREL
Adaptive per-branch test with built-in multiple-testing
Comparing selection regimes between two phenotypes
HyPhy BUSTED-PH or RELAX
Phenotype-specific or relaxation-detection
Recently diverged species (low divergence)
MK / asymptotic alpha (population genetics)
Codon dN/dS unreliable at low divergence; SFS-based instead
Within-species, dense polymorphism + divergence
polyDFE / GRAPES / asymptotic-MK
Full DFE + alpha jointly; preferred for adaptive-substitution rate
Coding selection genome-wide, with SFS available
grapes -m AUTO_ALL
Demography-aware alpha; standard population-genetics-aware adaptive-substitution scan
TDG09 detects site-specific shifts in selective constraint between trait-defined lineage groups; PCOC detects convergent amino-acid substitution
Drug-target evolution screen
aBSREL on candidate genes; cross-validate with MEME
Recent positive selection at drug-target loci
Pathogen / immune-evasion gene with high dS variation
BUSTED-S (synonymous rate variation aware)
dS variation across sites violates basic BUSTED assumptions
Plasmodium / Trypanosoma / Plasmid analysis
BUSTED-MH (multi-hit aware)
Multi-nucleotide substitutions common in these; basic BUSTED inflates false positives
Per-Method Failure Modes
Recombination producing false positive selection
Trigger: Running codeml or BUSTED on a gene with recombination breakpoints (viral genes, immune genes, paralog families).
Mechanism: All single-tree codon models assume one phylogeny across all sites. Recombination produces different trees for different segments; treating them as one tree forces the model to invent rate variation that mimics positive selection (Anisimova et al 2003 Genetics 164:1229).
Symptom: PAML M8 strongly rejects M7 (LRT > 50), with omega2 = 999 (PAML upper bound) at several "selected sites"; HyPhy BUSTED highly significant; sites clustered in specific gene regions.
Fix:MANDATORY: run GARD before any positive selection test. If GARD detects breakpoints (p < 0.05), partition the alignment at breakpoints and analyze each segment separately, or use the recombination-aware MEME with the partitioned tree set. RDP5 (Martin 2021 Virus Evol 7:veaa087) is an alternative for viral genomes. GARD output .json lists breakpoint positions and posterior support.
Alignment errors producing false positives
Trigger: Using default MAFFT or MUSCLE alignment on divergent CDS sequences; skipping codon-aware aligner.
Mechanism: Frame-shifted or misaligned codons introduce apparent non-synonymous substitutions at every position; codon-aware tools see these as positive selection (Schneider 2009 GBE 1:114; Markova-Raina & Petrov 2011 GR 21:863).
Symptom: "Selected sites" cluster in alignment regions with > 30% gaps; per-site posteriors in BEB / FUBAR concentrate in ambiguous columns; PREQUAL or Guidance2 marks these regions as poorly aligned; protein alignment shows obvious mismatches.
Fix: Use codon-aware aligner: PRANK (Loytynoja 2014 Methods Mol Biol 1079:155) is the standard for selection analysis (correctly models insertions); MACSE V2 (Ranwez 2018 MBE 35:2582) handles frameshifts and pseudogenes natively; OMM_MACSE wrapper combines them. After alignment, filter with PREQUAL (segment-level) or HmmCleaner (Di Franco 2019 BMC Evol Biol 19:21); do NOT use block-filtering (Gblocks, trimAl) which removes informative sites. Segment-level filtering preferred for selection (Di Franco 2019).
Saturated synonymous sites
Trigger: Comparing distantly related taxa (deep eukaryotic divergence, > 100 Myr); dS > 3 across most pairs.
Mechanism: Synonymous sites have undergone multiple substitutions; the observed dS underestimates true dS. The model can't recover the true rate; omega = dN/dS becomes unstable at the upper bound or low (depending on which direction the bias goes).
Symptom: PAML M0 omega = 999 or near-zero; per-branch dS variance huge; sites with omega > 1 in M8 BEB are at conserved residues (paradox).
Fix: Reduce taxon sampling to species with dS < 2 on internal branches. For deep selection inference on conserved residues, use protein-level methods (BUSTED with --model GTR AA codon translation; aBSREL with protein model option) or restrict to subclade with reasonable saturation. Yang 2007 PAML manual recommends dS < 1.5 per branch.
gBGC inflating apparent positive selection
Trigger: Mammalian / vertebrate gene with W->S substitution bias on a fast-evolving lineage.
Mechanism: GC-biased gene conversion fixes A/T -> G/C alleles preferentially in regions of high recombination, independent of selection (Galtier & Duret 2007 Trends Genet 23:273; Capra 2013 PLoS Genet 9:e1003684). Standard codon models attribute this to positive selection because nonsynonymous substitutions are unequally distributed across codon positions.
Symptom: Branch with apparent positive selection sits in high-recombination region; W->S / S->W substitution ratio > 1.5; selected sites concentrate at non-degenerate codon positions; HyPhy MEME-MH and BUSTED-MH attribute signal to multi-hit rather than positive selection.
Fix: Test for gBGC: W->S substitution rates on selected branch / S->W rates; report ratio. Re-run selection analysis with HyPhy BUSTED-MH (multi-hit aware); if signal vanishes, the original "selection" was gBGC + multi-hit substitutions. For genome-wide scans, mask sub-telomeric / high-recombination regions.
Branch-site test foreground specification
Trigger: Running codeml branch-site mod A after looking at the data to choose foreground branch.
Mechanism: The branch-site test is designed for a single a priori foreground; post hoc specification inflates Type-I by ~5x because the choice was informed by the data.
Symptom: Branch-site test highly significant for the "interesting" branch; aBSREL on same data shows no significant branch (aBSREL has built-in multiple-testing correction).
Fix: Specify foreground branches in registered protocol before looking at data. For exploratory branch-wise analysis, use aBSREL (Smith 2015 MBE 32:1342) which adaptively assigns branch-specific omega classes with multiple-testing built in. If branch-site test was post hoc, apply Bonferroni correction across all branches tested + report explicitly.
LRT critical value confusion
Trigger: Computing branch-site test p-value using standard chi-square df=2.
Mechanism: The branch-site test compares mod A (4 omega classes) against mod A1 (omega2 fixed at 1). The LRT statistic distribution is a 50:50 mixture of point-mass-at-0 and chi-square(df=1), not chi-square(df=2) (Self & Liang 1987 JASA 82:605; Zhang 2005 MBE 22:2472; Wong 2004 Genetics 168:1041). Using df=2 makes the test conservative; using df=1 standard makes it anticonservative.
Symptom: Branch-site p-values incorrectly inflated or deflated; users report finding selection at very stringent thresholds.
Fix: Use the 50:50 mixture critical value: 2.71 at p=0.05 (NOT 3.84). PAML's chi2 1 LRT command applies the mixture. Many published applications use chi-square df=2 conservatively, which loses power but doesn't inflate; chi-square df=1 directly is wrong and inflates Type-I.
omega2 hitting upper bound (999)
Trigger: PAML codeml output shows omega2 = 999 for an "under selection" site class.
Mechanism: PAML codeml uses an internal upper bound of 999 (= "infinity" in single precision). Hitting it indicates numerical issue: extremely few synonymous sites in the selected class, dS underestimation, or numerical optimization failure.
Symptom: Sites flagged as positive selection have omega2 = 999; BEB posteriors for those sites are weirdly distributed.
Fix: Re-run with multiple starting values of omega (fix_omega=0, vary omega = 0.1, 0.5, 1.0, 2.0, 5.0 across runs); check that all converge to same omega. Inspect alignment at flagged sites for unusual residue conservation. If omega = 999 persists, the gene may have rare-substitution patterns; switch to BUSTED-MH which accounts for multi-hit substitutions.
Multiple-testing burden in genome scans
Trigger: Running selection tests across thousands of genes without correction.
Mechanism: With ~5000 protein-coding genes in a typical analysis, 250 will be significant at p=0.05 under H0. The false-discovery rate without correction is 50%.
Symptom: Implausibly large gene lists "under selection"; functional categories enriched are non-specific (e.g. all immune genes by FDR).
Fix: Apply FDR correction (Benjamini-Hochberg). Genes in syntenic regions are non-independent; use Benjamini-Yekutieli for stronger control under dependence. For HyPhy site-level methods, the per-site p < 0.1 default is a starting point; multiple-test correction within a gene is typically not applied (sites within a gene are dependent), but cross-gene correction is necessary. Holm-Bonferroni for strict Type-I.
Convergent substitution misinterpreted as positive selection
Trigger: Lineage-specific selection found at a residue that has independently changed in multiple unrelated lineages.
Mechanism: Convergent substitutions at the same site in independent lineages produce signals in branch-site and other tests; this is convergence, not adaptive evolution per se (though convergent residues often ARE adaptive).
Symptom: Same residue flagged in multiple unrelated lineages by branch-site test; alignment shows convergent substitutions.
Fix: Switch from selection test to convergence test: CSUBST (Fukushima & Pollock 2023 Nat Eco Evo 7:155) for combinatorial substitution analysis; RERconverge (Redlich 2024 MBE 41:msae210) for relative-rate-vs-phenotype across categorical traits; PCOC (Rey 2018) for biophysical convergence. Report both convergence test and selection test results.
Quantitative Thresholds
Quantity
Threshold
Source / Rationale
dN/dS interpretation
omega < 1 purifying; omega = 1 neutral; omega > 1 positive (per site, branch, or gene depending on model)
'''Aggregate genome-wide selection scan results; FDR-correct.'''import json, glob, pandas as pd
from scipy.stats import false_discovery_control
defparse_busted(p):
d = json.load(open(p))
return {'p_value': d.get('test results', {}).get('p-value'),
'LRT': d.get('test results', {}).get('LRT'),
'omega_DH': d.get('fits', {}).get('Unconstrained model', {}).get('omega3')}
defcount_meme_sig(p, alpha=0.1):
d = json.load(open(p))
mle = d.get('MLE', {}).get('content', {}).get('0', {})
headers = [h[0] for h in d.get('MLE', {}).get('headers', [[]])]
pi = headers.index('p-value') if'p-value'in headers else -1returnsum(1for v in mle.values() if pi >= 0and v[pi] < alpha)
rows = []
for path in glob.glob('busted_mh/*.json'):
gene = path.split('/')[-1].replace('.json', '')
rows.append({'gene': gene, **parse_busted(path),
'meme_sig_sites': count_meme_sig(f'meme/{gene}.json')})
df = pd.DataFrame(rows)
df['busted_fdr'] = false_discovery_control(df['p_value'].fillna(1.0), method='bh')
df['adaptive'] = (df['busted_fdr'] < 0.05) & (df['meme_sig_sites'] > 0)
df.sort_values('busted_fdr').to_csv('selection_results.tsv', sep='\t', index=False)
PAML Branch-Site Test (Operational)
Goal: Test for episodic positive selection on a pre-specified foreground branch.
Approach: Mark foreground in newick (#1) -> codeml branch-site mod A vs A1 -> LRT against 50:50 mixture chi^2(0):chi^2(1).
# Mark foreground branch: use ete4 or manually
python -c "
from ete4 import Tree
t = Tree('species_tree.nwk', format=1)
target = t.search_nodes(name='target_species')[0]
target.name = target.name + ' #1'
print(t.write(format=1))
" > foreground.nwk
# Branch-site mod A (alternative)cat > codeml_modA.ctl << 'EOF'
seqfile = alignment.phy
treefile = foreground.nwk
outfile = mod_A.mlc
runmode = 0
seqtype = 1
CodonFreq = 2
model = 2
NSsites = 2
fix_kappa = 0
kappa = 2
fix_omega = 0
omega = 0.4
RateAncestor = 1
cleandata = 0
EOF
codeml codeml_modA.ctl
# Null model A1 (omega_2 = 1)cp codeml_modA.ctl codeml_modA1.ctl
sed -i 's/^omega = 0.4/omega = 1/' codeml_modA1.ctl
sed -i 's/^fix_omega = 0/fix_omega = 1/' codeml_modA1.ctl
sed -i 's/outfile = mod_A.mlc/outfile = mod_A1.mlc/' codeml_modA1.ctl
codeml codeml_modA1.ctl
'''Branch-site test LRT with 50:50 mixture critical value.'''from scipy.stats import chi2
defbranch_site_lrt(lnL_alt, lnL_null):
lrt = 2 * (lnL_alt - lnL_null)
if lrt <= 0:
return {'LRT': lrt, 'p_value': 0.5}
# 50:50 mixture of chi^2(0) and chi^2(1)
p = 0.5 * (1 - chi2.cdf(lrt, df=1))
return {'LRT': lrt, 'p_value': p}
Foreground branch must be specified before viewing data; for genome-wide screens with no a priori branch, use aBSREL instead. Bayes Empirical Bayes (BEB) sites with posterior > 0.95 on positive-selection class are the per-site call.
Approach: Compute counts of synonymous and nonsynonymous polymorphisms (P) and divergences (D); fit asymptotic alpha by binning by minor-allele frequency and extrapolating.
# Standard MK
mk_alpha <-function(Dn, Ds, Pn, Ps){1-(Ds * Pn)/(Dn * Ps)}# Asymptotic alpha via the Messer-Petrov 2013 web tool# (https://benhaller.com/messerlab/asymptoticMK.html) or the impMKT R package# (Murga-Moreno 2022 G3 12:jkac206) which wraps the asymptotic computation.
library(impMKT)# Inputs: per-frequency-bin (Pn, Ps) plus genome-wide (Dn, Ds)
freq_bins <- seq(0.01,0.5,0.01)
pn_by_freq <-c(...)# nonsyn polymorphism count per bin
ps_by_freq <-c(...)# syn polymorphism count per bin
fit <- asymptoticMK(
Dn = total_dn, Ds = total_ds,
Pn = pn_by_freq, Ps = ps_by_freq,
x = freq_bins
)
fit$alpha_asymptotic # adaptive substitution rate, corrected
fit$alpha_original # original MK (biased)
For full DFE inference (alpha + distribution of fitness effects), use polyDFE (Tataru-Bataillon 2019):
polyDFE -d data.txt -m C -i estimates.init -o output_basename
DFE-alpha (Eyre-Walker 2009) and GRAPES (Galtier 2016) are alternatives; GRAPES is most robust for genome-wide adaptive-substitution scans.
RERconverge for Trait-Correlated Rate Shifts
Goal: Identify genes whose evolutionary rate correlates with a binary or categorical phenotype across the species tree.
Approach: Compute per-gene relative evolutionary rates -> correlate against phenotype -> Bonferroni or FDR-correct across genes.
For categorical traits (more than binary), Redlich 2024 MBE 41:msae210 extends RERconverge.
Reconciliation: When Methods Disagree
Pattern
Likely cause
Action
codeml M8 significant, BUSTED null
M8 vs M7 inflated by relaxed constraint mimicking selection
Trust BUSTED; check M8a vs M8 instead (stricter null)
codeml branch-site significant, aBSREL null
Branch-site test foreground post hoc
aBSREL with built-in multiple-testing is correct; downgrade claim
BUSTED significant, MEME no sites
Episodic at sites BUSTED can't pinpoint; or basic BUSTED detected SRV not selection
Run BUSTED-S; if signal vanishes, was SRV; if persists, gene-wide episodic
MEME positive, FEL null
Episodic selection (MEME-specific)
Trust MEME for episodic; FEL only detects pervasive
Multiple tests positive at same site
High-confidence site under selection
Report; consider experimental validation
Test positive but PREQUAL flagged 20% of alignment
Alignment artifact
Re-filter (HmmCleaner); re-test; downgrade if positive site is in filtered region
Test positive but in high-recombination region
gBGC
W->S substitution test; if gBGC-attributable, downgrade
BUSTED-MH null where BUSTED significant
Multi-hit substitutions misattributed
Trust BUSTED-MH; original positive was multi-hit pattern
RELAX k > 1 with branch-site test null
Selection regime intensification (more purifying)
RELAX captures regime shift; branch-site missed because foreground different
Branch-site significant on Drosophila branch but no signal in mammals
Lineage-specific adaptation; or dS saturation in mammals
Inspect dS distribution; if mammals dS < 0.5 across branch, signal is real; if dS > 2, saturation explanation
asymptotic alpha < 0
DFE has high deleterious load; or demographic violation
Check polyDFE / GRAPES with demographic correction
Operational rule for publication: GARD pre-screen documented as negative + PREQUAL/HmmCleaner filtering applied + dS < 1.5 per branch + W->S ratio not elevated + BUSTED-MH significant (gene-wide) + MEME flags sites + aBSREL flags branches with consistent direction = publication-ready evidence. Single-method significance (especially M8 vs M7 alone) should be downgraded.
Cohort Gotchas
Immune / MHC loci: intra-genic recombination is high; GARD pre-screen mandatory; high apparent positive selection often reflects gene conversion between alleles, not adaptive change
Viral genomes: rapid evolution + recombination + multi-hit substitutions common; BUSTED-MH and FUBAR-MH essential; use RDP5 for recombination detection
Plasmodium / Trypanosoma: high codon-usage bias and multi-hit substitutions; use BUSTED-MH and BUSTED-S
Mammalian X-chromosome: higher dS than autosomes (male-driven evolution); gBGC asymmetry by chromosome; reduce dS threshold for X-linked genes
Recent human / population genetics: dS dramatically underestimated at recent divergence; use SFS-based methods (asymptotic alpha, polyDFE)
Convergent evolution traits (echolocation, marine): RERconverge / CSUBST / PhyloAcc-noncoding designed for these; codon-based methods alone miss the convergent signal
Anticipated Reviewer Pushback
Pushback
Standard response
"GARD pre-screen?"
Yes; no breakpoints (or partitioned at p < 0.05 breakpoints); per-segment results consistent
For genome-wide scans (> 5000 genes), parallelize per-gene analyses with Snakemake / Nextflow.
References
Yang Z 1997 CABIOS 13:555 (PAML codeml)
Yang Z et al 2000 Genetics 155:431 (codon models M0-M8)
Yang Z & Bielawski JP 2000 TREE 15:496 (codon model framework)
Zhang J et al 2005 MBE 22:2472 (branch-site mod A); Wong WSW et al 2004 Genetics 168:1041 (LRT mixture); Self SG & Liang K-Y 1987 JASA 82:605 (LRT boundary)
Swanson WJ et al 2003 MBE 20:18 (M8a null); Bielawski JP & Yang Z 2004 J Mol Evol 59:121 (clade models)
Anisimova M & Yang Z 2007 MBE 24:1219 (multiple-testing / branch-site power); Anisimova M et al 2003 Genetics 164:1229 (recombination FP); Anisimova M, Bielawski JP & Yang Z 2001 MBE 18:1585 (LRT power)
Pond SLK et al 2006 MBE 23:1891 (GARD); Martin DP et al 2021 Virus Evol 7:veaa087 (RDP5)
Kosakovsky Pond SL & Frost SDW 2005 MBE 22:1208 (FEL); Murrell B et al 2012 PLoS Genet 8:e1002764 (MEME); Murrell B et al 2013 MBE 30:1196 (FUBAR)
Murrell B et al 2015 MBE 32:1365 (BUSTED); Wisotsky SR et al 2020 MBE 37:2430 (BUSTED-S); Lucaci AG et al 2023 MBE 40:msad150 (BUSTED-MH)
Smith MD et al 2015 MBE 32:1342 (aBSREL); Wertheim JO et al 2015 MBE 32:820 (RELAX)
McDonald JH & Kreitman M 1991 Nature 351:652 (MK); Smith NGC & Eyre-Walker A 2002 Nature 415:1022 (alpha); Messer PW & Petrov DA 2013 PNAS 110:8615 (asymptotic alpha)
Murga-Moreno J et al 2022 G3 12:jkac206 (impMKT); Tataru P & Bataillon T 2019 Bioinformatics 35:2868 (polyDFE); Eyre-Walker A & Keightley PD 2009 MBE 26:2097 (DFE-alpha); Galtier N 2016 PLoS Genet 12:e1005774 (GRAPES)
Galtier N & Duret L 2007 Trends Genet 23:273 (gBGC); Capra JA et al 2013 PLoS Genet 9:e1003684 (gBGC genome-scale)
Schneider A et al 2009 GBE 1:114 + Markova-Raina P & Petrov D 2011 GR 21:863 (alignment-error FP)
Loytynoja A 2014 Methods Mol Biol 1079:155 (PRANK); Ranwez V et al 2018 MBE 35:2582 (MACSE V2); Whelan S et al 2018 Bioinformatics 34:3929 (PREQUAL); Di Franco A et al 2019 BMC Evol Biol 19:21 (HmmCleaner)
Yang Z 2007 PAML manual; Álvarez-Carretero S et al 2023 MBE 40:msad041 (Beginner's Guide PAML)
Kowalczyk A et al 2019 Bioinformatics 35:4815 + Redlich R et al 2024 MBE 41:msae210 (RERconverge)
Fukushima K & Pollock DD 2023 Nat Eco Evo 7:155 (CSUBST); Hu Z et al 2019 MBE 36:1086 (PhyloAcc); Pollard KS et al 2010 GR 20:110 (phyloP); Rey C et al 2018 MBE 35:2296 (PCOC)
Related Skills
comparative-genomics/ortholog-inference - Single-copy ortholog alignments as input
