Whole genome duplication

Version Compatibility

Reference examples tested with: wgd v2.0.31+ (heche-psb/wgd; Chen & Zwaenepoel 2024 Bioinformatics 40:btae272), KsRates 1.1.3+ (VIB-PSB/ksrates; Sensalari 2022 Bioinformatics 38:530), DupGen_finder (Qiao 2019 Genome Biol 20:38), MAPS 1.0 (Li 2018), POInT (Conant lab), SLEDGe (bioRxiv 2024.01.17.574559), Whale.jl 2.0+, ksrates pip 1.1+, MCScanX 1.0+, PAML 4.10+ (yn00/codeml for Ks), BLAT 36+, DIAMOND 2.1+, R 4.4+, mclust 6.1+ (for mixture models). Python 3.10+ required for wgd v2.

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

• CLI: wgd --version; ksrates --version; wgd ksd --help
• Python: pip show wgd ksrates
• R: packageVersion('mclust')

If code throws wgd ksd: cannot find PAML output, KsRates: insufficient sister species, MAPS: missing tree, these tools have specific input expectations: wgd needs codon-aware MAFFT/MUSCLE alignment; KsRates needs configured config_ksrates.txt; MAPS needs nucleotide tree. The deprecated arzwa/wgd v1 is replaced by heche-psb/wgd v2.

Whole Genome Duplication Analysis

"Are there WGD events in this lineage and when did they occur?" -> WGD detection combines Ks distributions (synonymous-substitution rates between gene paralog pairs, showing peaks at past polyploidy events) and synteny block analysis (parallel collinear blocks within a genome). Modern best practice uses wgd v2 (Chen & Zwaenepoel 2024 Bioinformatics 40:btae272) as an integrated pipeline. KsRates (Sensalari 2022 Bioinformatics 38:530) is mandatory for cross-lineage comparison because substitution rates vary across the tree -- ignoring this places WGDs incorrectly relative to speciation events. The fundamental tradeoff: Ks plot peaks are visually obvious but biologically ambiguous between (1) small-scale tandem duplications, (2) segmental duplications, and (3) true WGD; combining Ks with synteny anchors disambiguates these.

• CLI: wgd dmd and wgd ksd -- paranome construction + Ks distribution
• CLI: wgd syn -- synteny-anchored WGD signal extraction
• CLI: ksrates init then ksrates wgd-paralogs ortho -- substitution-rate-corrected positioning
• CLI: MCScanX -h then dupgen_finder -- duplication-class assignment
• CLI: mapsR -- gene-tree-based WGD phylogenetic placement

Algorithmic Taxonomy

Tool	Approach	Output	Strength	Fails when
wgd v2 (Chen & Zwaenepoel 2024 Bioinformatics 40:btae272)	Integrated paranome + Ks + synteny + WGD dating	Ks distributions, collinearity plots, GMM/ELMM mixture fits, dating	Standard 2024 pipeline; replaces deprecated arzwa/wgd v1	Saturation at Ks > 2; single-lineage substitution rate variation
KsRates (Sensalari 2022 Bioinformatics 38:530)	Substitution-rate correction via outgroup pairs	Adjusted Ks ages of focal-species paralogs vs orthologs	MANDATORY when comparing WGDs across lineages with different rates	Requires at least 2 outgroups for rate calibration
DupGen_finder (Qiao 2019 Genome Biol 20:38)	Classifies duplications by genomic context	Per-gene class: tandem, proximal, dispersed, segmental, WGD	Disambiguates duplication type	Class assignment depends on intervening-gene-count windows
MAPS (Li 2018)	Phylogenomic placement of WGD via gene-tree topology mapping	WGD position on species tree	Detects WGDs from gene-tree-species-tree discordance	Computationally heavy; requires many gene trees
POInT (Conant 2008 Genome Res 18:1597)	Order-aware reconstruction of WGD chromosomes	Reconstructed ancestral WGD genome	Strong inference for syntenic-block ages	Lineage-specific tuning required
SLEDGe (bioRxiv 2024.01.17.574559)	ML classifier on Ks plot features	WGD vs no-WGD binary call + confidence	Reduces visual-peak-fitting subjectivity	Newer; less validated
wgd v1 (arzwa/wgd; DEPRECATED)	Predecessor of v2	--	Historical	Use v2 (heche-psb/wgd)
dupHMM (Zwaenepoel 2019 different MBE paper)	HMM on Ks for WGD/SSD attribution	Per-pair WGD/SSD classification	Probabilistic disambiguation	Less integrated than wgd v2
Whale.jl (Zwaenepoel & Van de Peer 2019 MBE 36:1384)	Bayesian DTL + WGD reconciliation	WGD posterior at species-tree nodes	Native WGD modeling; integrates with [[gene-tree-species-tree-reconciliation]]	Julia ecosystem
WGDexploreR (legacy)	Visualize Ks plots	Plots only	Visualization aid	Not for inference
McLuster-WGD (custom workflows)	Mixture-model fitting on Ks	GMM / ELMM components	For custom peak fitting	Not a standard tool
ksrates web (sensalari 2022)	Web interface to ksrates	Same as CLI	User-friendly	Manual config; not scriptable for genome-wide
FastKaKs (Kang 2017)	Fast Ks computation for many pairs	Ks values per pair	Faster than PAML yn00	Slightly less accurate; for screening
wgs2pep (Vandepoele/wgs)	Pep-to-WGD ortholog identification	Per-paralog WGD assignment	Plant-focused	Less general

Methodology evolves; verify the current wgd v2 manual and the Cheng & Zwaenepoel 2024 review chapter (in Polyploidy: Methods and Protocols) before locking on a single workflow. The 2R vertebrate / 3R teleost / Ss4R salmonid WGDs are well-established; novel WGD claims require concordance across Ks, synteny, and phylogenomic placement.

Decision Tree by Experimental Scenario

Scenario	Recommended approach	Why
Plant comparative genomics; suspect WGD	wgd v2 + KsRates	Modern standard pipeline; plants are WGD-prone
Vertebrate 2R or 3R WGD analysis	wgd v2 + MAPS phylogenomic placement	Vertebrate-specific; combines Ks + tree topology
Salmonid Ss4R WGD	Whale.jl with WGD node in species tree	Native WGD modeling in Bayesian framework
Distinguish WGD from sequential small duplications	Ks GMM fit + synteny block analysis (wgd syn)	Ks peak alone insufficient; synteny confirms
Date WGD relative to a speciation event	KsRates with outgroup speciation calibration	Required when substitution rates differ
Identify duplicates retained vs lost after WGD	DupGen_finder + Ks distribution	Classification + age estimation
Detect novel WGD in non-model organism	wgd dmd -> wgd ksd -> visual GMM/ELMM mixture	Standard discovery workflow
Reconstruct ancestral WGD genome architecture	POInT	Order-aware ancestral reconstruction
Test post-WGD bias in fractionation (dosage-balance)	DupGen_finder + per-gene functional annotation	Compare retained vs lost genes
Distinguish recent vs ancient WGD	KsRates + Ks peak location after correction	Ks peak < 0.1 recent; 0.5-1.5 ancient
WGD in clade with rapid evolution (e.g. fish)	KsRates mandatory	Without rate correction, Ks peaks displaced
ML classification of WGD signal	SLEDGe	Newer ML-based alternative to manual fitting
Integrate WGD with DTL inference	Whale.jl OR ALE with WGD branch	See [[gene-tree-species-tree-reconciliation]]
Pan-clade WGD survey (e.g. all angiosperms)	wgd v2 batch + ksrates aggregated	Standard for sustained phylogenomic surveys
Polyploid genome with subgenomes	DupGen_finder for tandem/segmental + AnchorWave proali for subgenome-aware synteny	Subgenome assignment first
Recently diverged species pair, possible recent WGD	minimap2 -x asm5 + SyRI + Ks distribution	High-resolution recent-WGD detection

Per-Tool Failure Modes

Saturation at Ks > 2

Trigger: Computing Ks for ancient WGD candidates from distantly related taxa.

Mechanism: Synonymous substitutions saturate after Ks ~2; each site has undergone multiple substitutions. Observed Ks underestimates true Ks; the relationship between Ks and time becomes non-monotonic above 1.5. WGD peaks at age 200+ Myr in vertebrates / 100+ Myr in plants are at Ks > 2 and unreliable (Vanneste 2013 GR 23:1304).

Symptom: wgd ksd output shows peak at Ks > 1.5 with broad distribution; KsRates rate-corrected Ks even more uncertain; visual fitting yields ambiguous components.

Fix: Restrict Ks-based WGD inference to Ks < 1.5; for older WGDs, use phylogenomic methods (MAPS, POInT, Whale.jl) which don't rely on Ks alone. Report Ks-based dating with explicit saturation caveat. For 2R vertebrate WGD (~500 Myr), MAPS / Whale.jl is required because Ks is saturated.

Substitution-rate variation across lineages

Trigger: Comparing WGD position in two lineages with different molecular evolutionary rates.

Mechanism: If lineage A evolves twice as fast as lineage B, the same biological time is at twice the Ks in lineage A. A WGD at Ks = 0.5 in lineage A and Ks = 0.25 in lineage B might be the same event biologically.

Symptom: WGD claimed at "different times" in different lineages; speciation-vs-WGD relative timing flips depending on which lineage is the focal species.

Fix: Always use KsRates (Sensalari 2022) when comparing WGDs across lineages. KsRates uses outgroup-species speciation events to calibrate the relative substitution rates, then rescales Ks to a common scale. Single-lineage Ks dating is unreliable for inter-lineage comparison.

Tandem duplication masquerading as WGD peak

Trigger: Ks distribution shows a peak at low Ks (< 0.5); user concludes "recent WGD."

Mechanism: Tandem duplications create paralog pairs with low Ks; these accumulate in clusters (especially in NLR genes in plants, OR genes in mammals). The Ks peak reflects clustered tandem origins, not WGD.

Symptom: "WGD" peak Ks-distribution is dominated by paralogs from tandem clusters; synteny analysis shows few cross-chromosome parallel blocks.

Fix: Use DupGen_finder to classify each paralog pair as tandem / proximal / dispersed / segmental / WGD. The true WGD signal comes from segmental + WGD classes. Recompute Ks distribution restricted to WGD-classified pairs (or anchor-pair restricted to synteny blocks). wgd v2 integrates this filtering.

Synteny block age inconsistency

Trigger: Different synteny blocks from the same suspected WGD have different Ks distributions.

Mechanism: Real WGD blocks should share a synchronized Ks distribution centered at the WGD age; if blocks have very different ages, the inferred "WGD" was probably a series of segmental duplications.

Symptom: Per-block Ks median varies widely; some blocks show Ks ~ 0.3 and others Ks ~ 1.0; not consistent with single WGD.

Fix: Compute per-block Ks distribution; require synchronization (interquartile range overlapping across blocks). wgd v2 reports per-block age dispersion. If dispersion is high, downgrade to "potentially WGD-like" or attribute to ancient segmental duplication history.

Mixture model under/overfitting components

Trigger: Fitting GMM (gaussian mixture model) or ELMM (exponential-lognormal mixture model) to Ks distribution.

Mechanism: Mixture models with too few components miss real WGD signal; too many components find spurious peaks. BIC-based model selection is standard but sensitive to bin choice in histograms.

Symptom: Different runs with different bin counts give different component numbers; visual peaks don't match component means.

Fix: Use BIC-based component selection with 5-fold cross-validation; report uncertainty in component count. wgd v2 fits both GMM and ELMM and reports BIC for each model up to 5 components. Visually validate against synteny block analysis.

Comparing wgd v1 (deprecated) vs v2 outputs

Trigger: Mixing scripts written for arzwa/wgd v1 with heche-psb/wgd v2.

Mechanism: wgd v1 was deprecated 2023; v2 has different command-line interface, default parameters, and output file structure. Scripts from v1 fail or produce different results in v2.

Symptom: Older scripts using wgd ksd v1 syntax don't work in v2; output paths differ.

Fix: Update all scripts to wgd v2 syntax. The v1 wgd is no longer maintained; v2 is the current standard.

KsRates failing with insufficient species

Trigger: Running KsRates with one focal species and one outgroup.

Mechanism: KsRates calibrates substitution-rate variation via at least two outgroup speciation events; one outgroup species provides only one rate point and cannot resolve rate heterogeneity.

Symptom: KsRates errors or produces unreliable rate-corrected Ks.

Fix: Include >= 2 outgroup species at different phylogenetic distances; for plants, a model angiosperm + gymnosperm pair; for vertebrates, lamprey + invertebrate outgroup. Document outgroup sampling.

Polyploid genome confusion

Trigger: Running wgd v2 on an unassigned-subgenome polyploid (e.g. wheat hexaploid without A/B/D subgenome labels).

Mechanism: WGD signal is muddled when subgenomes are not separated; orthologs across subgenomes appear as paralogs, intermixing tandem / segmental / WGD / homeolog signal.

Symptom: Ks distribution shows multiple overlapping peaks at varying intensities; component fits are unstable.

Fix: Assign subgenomes first using k-mer methods (KMC2 + SubPhaser; Jiao 2017 GR 27:778) or synteny-based assignment (GENESPACE; Lovell 2022). Then run wgd v2 on each subgenome separately. Document subgenome assignment.

Confusion of homeologous (WGD) vs orthologous (speciation) pairs

Trigger: Computing Ks across recent WGD species without explicit homeolog labeling.

Mechanism: In recent WGD lineages (e.g. salmonid Ss4R), the "ortholog" between species and the "homeolog within species" can have similar Ks; standard ortholog detection (OrthoFinder) lumps both.

Symptom: wgd v2 reports Ks distributions but doesn't distinguish homeologs from orthologs; downstream dating confused.

Fix: Use synteny-anchored ortholog detection (GENESPACE, ProteinOrtho-synteny) to separate cross-species orthologs from within-species homeologs. Run wgd v2 separately on each class. Report each Ks distribution explicitly.

Quantitative Thresholds

Quantity	Threshold	Source / Rationale
Ks saturation upper limit	Ks < 1.5 for reliable inference; >= 2 saturated	Vanneste 2013 GR 23:1304
Recent WGD Ks range	Ks 0.1-0.5	Standard convention
Ancient WGD Ks range	Ks 0.5-1.5	Standard convention
WGD synteny block minimum	>= 5 anchors per block	wgd v2 / GENESPACE default
Tandem duplicate window for DupGen_finder	5 genes default; species-tunable	Qiao 2019
Proximal duplicate window	5-25 genes	Standard
Number of mixture components to consider	1-5 with BIC selection	wgd v2 default
Cross-block age synchrony for WGD	per-block Ks IQR overlap	Visual + statistical
KsRates minimum outgroups	>= 2 species at different distances	Sensalari 2022
KsRates substitution rate correction valid range	Ks < 1.5 in original; correction can extend slightly	KsRates docs
MAPS minimum gene trees	>= 1000 single-copy ortholog trees	Li 2018
Whale.jl WGD detection power	depends on retention rate; > 0.3 retention typically detectable	Zwaenepoel 2019
Vertebrate 2R Ks	saturated; estimated 500-700 Myr	Dehal 2005
Teleost 3R Ks	saturated; estimated 250-350 Myr	Glasauer 2014
Salmonid Ss4R Ks	80-100 Myr; Ks ~0.1-0.2	Lien 2016
Plant 1R / 2R	varies clade; angiosperm zeta ~120 Myr	Soltis 2009
Synonymous codon site count	dS reliable when >= 30 synonymous sites per pair	Yang 2007 PAML
Per-pair gene length minimum	>= 300 bp CDS	wgd v2 default
Codon-aware MSA aligner	MAFFT --auto or MUSCLE; for distant pairs PRANK	wgd v2 supports all
Bootstrap support for MAPS	>= 80 on gene-tree branches	Li 2018

wgd v2 Standard Workflow

Goal: Detect and date WGD events from a focal-species proteome and CDS, with synteny-anchored Ks distribution.

Approach: Build paranome -> compute Ks distribution -> identify synteny anchors -> mixture model fit -> visualize.

# Install (Python 3.10+ required)
pip install wgd

# 1. Build paranome (all-vs-all paralog identification)
wgd dmd cds.fasta -o output/paranome.tsv -t 16

# 2. Compute Ks distribution
# Verify exact flags with `wgd ksd --help` (the wgd v2 CLI evolves; --pairwise / --ks-method spelling differs across versions).
wgd ksd output/paranome.tsv cds.fasta -o output/ksd \
    --aligner mafft --n-threads 16

# 3. Synteny anchors (intra-genome)
wgd syn output/paranome.tsv gff.bed cds.fasta -o output/syn \
    --feature gene --gene-attribute Name --min-block-size 5

# 4. Mixture model on synteny-anchored Ks
wgd mix output/ksd/ks_distributions.tsv -o output/mix \
    --model both --components 1 2 3 4 5

# 5. Visualize
wgd viz output/mix/mix_results.tsv -o output/viz \
    --type histogram --bins 50

'''Parse wgd v2 outputs and identify WGD peak from Ks distribution.'''
import pandas as pd
from scipy import stats


def load_ks_distribution(ksd_file):
    '''Load wgd ksd output: pair  Ks  Ka  ...'''
    df = pd.read_csv(ksd_file, sep='\t')
    # Filter saturated
    df_filt = df[(df['Ks'] > 0) & (df['Ks'] < 2)]
    return df_filt


def fit_mixture(ks_values, n_components=3):
    '''Fit GMM (gaussian mixture model) on Ks via sklearn.'''
    from sklearn.mixture import GaussianMixture
    gmm = GaussianMixture(n_components=n_components, random_state=42)
    gmm.fit(ks_values.reshape(-1, 1))
    return {
        'means': gmm.means_.flatten(),
        'variances': gmm.covariances_.flatten(),
        'weights': gmm.weights_,
        'bic': gmm.bic(ks_values.reshape(-1, 1))
    }


def identify_wgd_peaks(ks_values, min_components=1, max_components=5):
    '''BIC-based model selection.'''
    results = {}
    for n in range(min_components, max_components + 1):
        results[n] = fit_mixture(ks_values, n)
    best_n = min(results.keys(), key=lambda k: results[k]['bic'])
    return best_n, results[best_n]

KsRates Substitution-Rate-Corrected Workflow

Goal: Position a focal-species WGD relative to speciation events with substitution-rate correction.

Approach: Define focal species + 2+ outgroups -> compute orthologous Ks (focal vs outgroup) -> compute paralogous Ks (focal-internal) -> rescale via outgroup calibration.

# KsRates is best driven through its Snakemake pipeline (which orchestrates ortholog Ks,
# paralog Ks, rate correction, and plotting). Subcommand naming differs across releases;
# always verify with `ksrates --help` against the installed version.

# 1. Generate a working config (subcommand spelling varies by release; e.g. `init` in some,
#    `generate-config` in others). Inspect `ksrates --help`.
ksrates init config_ksrates.txt        # OR: ksrates generate-config config_ksrates.txt

# 2. Edit config_ksrates.txt to set focal_species, outgroups, FASTA + GFF paths, tree.

# 3. Run the full pipeline (Snakemake-driven):
ksrates --config config_ksrates.txt --n-threads 16
# Or invoke individual stages (subcommand names: see `ksrates --help`).

The output plot shows the rate-corrected focal-species paralog Ks distribution with vertical lines indicating outgroup-species speciation events; WGD peaks before vs after speciation events can be distinguished.

DupGen_finder for Duplication Class Assignment

Goal: Classify each paralog pair as tandem / proximal / dispersed / segmental / WGD by genomic context.

Approach: MCScanX collinearity + intervening-gene count -> classify each duplicate by class.

# Input: MCScanX collinearity file + GFF
git clone https://github.com/qiao-xin/DupGen_finder
cd DupGen_finder

# Run with intervening-gene count
./DupGen_finder.pl \
    -i input_collinearity_file \
    -t species_name \
    -c gene_count_file \
    -o output

# Output: tandem, proximal, dispersed, segmental, wgd duplicate lists

'''Aggregate DupGen_finder output for downstream Ks distribution per class.'''
def load_dupgen(class_dir):
    classes = {}
    for cls in ('tandem', 'proximal', 'dispersed', 'segmental', 'wgd'):
        path = f'{class_dir}/{cls}.pairs'
        try:
            classes[cls] = pd.read_csv(path, sep='\t', header=None,
                                       names=['gene1', 'gene2'])
        except FileNotFoundError:
            classes[cls] = pd.DataFrame(columns=['gene1', 'gene2'])
    return classes

MAPS Phylogenomic WGD Placement

Goal: Place a WGD event on a species tree using gene-tree-species-tree mapping.

Approach: Build many single-copy ortholog gene trees -> map each tree's topology to the species tree -> identify branches with topology consistent with a WGD event.

MAPS is heavyweight; requires CRG database setup and significant compute. See https://bitbucket.org/barker-lab/maps/src for the current pipeline.

Reconciliation: When Methods Disagree

Pattern	Likely cause	Action
wgd v2 peak at Ks 0.5, MAPS supports WGD on different branch	Rate variation across lineages	KsRates with outgroups; trust rate-corrected position
Ks peak prominent, synteny anchors sparse	Tandem-driven peak	DupGen_finder classification; verify WGD class
Synteny blocks present, Ks peak diffuse	Old WGD; saturated Ks	Restrict to recent paralogs; expect Ks 0.5-1.5
One outgroup's KsRates says WGD before speciation, other after	Outgroup substitution rate variation	Use multiple outgroups; report ranges; explicit caveat
GMM fits 2 components, ELMM fits 3	Model class differs	Visual inspection; report both; choose by BIC
DupGen_finder calls "WGD" but Ks > 2	Saturation	Ks unreliable; verify via synteny + phylogenomic placement
Two recently sequenced sister species disagree on WGD date	Different annotation pipelines	Re-annotate consistently; verify orthology
wgd v2 detects WGD; Whale.jl posterior doesn't	Different inference frameworks	Whale.jl is Bayesian and gene-tree-based; trust if MAPS / phylogeny supports
Salmonid Ss4R detectable by wgd v2 in young salmonids	Recent WGD; clearer signal	Confirmation, not contradiction

Operational rule for publication: Concordance across Ks distribution (wgd v2), synteny-anchored block analysis (wgd syn / GENESPACE), KsRates rate-corrected positioning, and at least one phylogenomic method (MAPS / Whale.jl / ALE with WGD node) = publication-grade WGD claim. Single-Ks-peak claims should be downgraded to "Ks evidence for possible WGD."

Cohort Gotchas

• Plant WGD legacy: All angiosperms share at least one ancestral WGD; the zeta WGD ~120 Myr (Soltis 2009 Trends Genet 25:404); confirm consensus against published plant lineages
• Vertebrate 2R: ~500-600 Myr; Ks saturated; use MAPS phylogenomic placement (Dehal & Boore 2005)
• Teleost 3R: ~320 Myr; in addition to 2R; doubles ohnologs in fish
• Salmonid Ss4R: ~80-100 Myr; recent enough that Ks is informative; ohnologs identifiable
• Plant polyploids (wheat, Brassica): recent; subgenomes assignable; subgenome-stratify before wgd v2
• Allopolyploids vs autopolyploids: allopolyploids have higher inter-subgenome divergence (easier to assign subgenomes); autopolyploids have similar subgenomes (chimeric assembly risk)
• Lineage-specific extra duplications post-WGD: make Ks peak broader; differentiate from sequential WGDs

Anticipated Reviewer Pushback

Pushback	Standard response
"Saturation?"	Ks < 1.5 reported; for older WGDs, MAPS or Whale.jl used
"Rate variation?"	KsRates applied with 2+ outgroups; rate-corrected positioning
"Tandem vs WGD?"	DupGen_finder classification; WGD-class subset reported
"Synteny confirmation?"	wgd syn or GENESPACE; per-block Ks synchrony reported
"Phylogenomic support?"	MAPS, Whale.jl, or ALE-with-WGD agrees
"Mixture model uncertainty?"	BIC-based model selection; 1-5 components tested
"Subgenome assignment (polyploid)?"	k-mer-based or synteny-based subgenome assignment first
"Why wgd v2 over wgd v1?"	v1 deprecated 2023; v2 (heche-psb/wgd) is current
"Comparison to published WGD dates?"	Match published vertebrate 2R / teleost 3R / salmonid Ss4R; cite primary literature

Common Errors

Error / symptom	Cause	Solution
wgd ksd reports few Ks values	CDS sequences not in-frame or short	Verify CDS quality; filter short sequences
KsRates "no orthologs found"	Outgroup species names mismatched	Normalize species labels exactly
GMM fits with 5 components, all similar means	Overfitting	Use BIC; restrict max components to 3-4
Per-block Ks IQR > 0.5	Block age dispersion; likely segmental not WGD	Trust dispersion; reclassify as segmental
wgd v2 ELMM converges to single component	Insufficient pairs	Reduce filtering; include more paralog pairs
MAPS slow / OOM	Many gene trees; large dataset	Cluster; reduce to representative single-copy orthologs
Whale.jl Turing fails to mix	NUTS step size; tree dimension	HMC manual tuning; or reduce species tree
DupGen_finder produces empty tandem set	Default window inappropriate	Adjust window for genome density
Ks distribution has secondary peak at 1.8	Saturation artifact (not WGD)	Recompute with PRANK or PAML codeml; restrict Ks < 1.5
Polyploid "WGD" appears at Ks 0.05	Recent polyploidy is ohnologous WGD, expected	Verify with subgenome assignment
Plant analysis lacks reference outgroup gymnosperm	KsRates needs gymnosperm + angiosperm	Add Selaginella or moss outgroup

Tool Installation Notes

# wgd v2
pip install wgd
# Or: git clone https://github.com/heche-psb/wgd && cd wgd && pip install .

# KsRates
pip install ksrates

# DupGen_finder (Perl + MCScanX dependency)
git clone https://github.com/qiao-xin/DupGen_finder
# Install MCScanX (Wang 2012): git clone https://github.com/wyp1125/MCScanX && cd MCScanX && make

# MAPS (Python; requires CRG, ete3)
git clone https://bitbucket.org/barker-lab/maps

# Whale.jl (Julia)
julia -e 'using Pkg; Pkg.add("Whale")'

# SLEDGe
git clone https://github.com/SLEDGe-team/SLEDGe

# POInT (C++)
git clone https://github.com/gconant0/PoInT
cd PoInT && make

# PAML for Ks computation (yn00 method)
conda install -c bioconda paml

# DIAMOND / BLAST for paranome
conda install -c bioconda diamond blast

# R packages
install.packages(c('mclust', 'rmixmod'))

For new analyses, default to wgd v2 + KsRates as the primary pipeline; MAPS or Whale.jl for confirmation on phylogenomic placement.

References

• Chen H & Zwaenepoel A 2024 Bioinformatics 40:btae272 (wgd v2)
• Sensalari C et al 2022 Bioinformatics 38:530 (KsRates)
• Qiao X et al 2019 Genome Biol 20:38 (DupGen_finder)
• Li Z et al 2018 GR 28:1306 (MAPS)
• Conant GC 2008 GR 18:1597 (POInT)
• Zwaenepoel A & Van de Peer Y 2019 MBE 36:1384 (Whale.jl)
• SLEDGe team 2024 bioRxiv 2024.01.17.574559 (SLEDGe ML classifier)
• Vanneste K et al 2013 GR 23:1304 (Ks saturation)
• Holland PWH et al 1994 Development Suppl:125 (2R hypothesis)
• Dehal P & Boore JL 2005 PLoS Biol 3:e314 (vertebrate 2R confirmation)
• Glasauer SMK & Neuhauss SCF 2014 Mol Genet Genomics 289:1045 (teleost 3R)
• Lien S et al 2016 Nature 533:200 (salmonid Ss4R)
• Soltis PS et al 2009 Trends Genet 25:404 (plant polyploidy review)
• Freeling M 2007 PNAS 104:8723 (gene balance hypothesis)
• Force A et al 1999 Genetics 151:1531 (subfunctionalization)
• Maere S et al 2005 PNAS 102:5454 (post-WGD retention bias)
• Tang H et al 2008 GR 18:1944 (synteny / MCScan)
• Lovell JT et al 2022 eLife 11:78526 (GENESPACE)
• Jiao W-B et al 2017 GR 27:778 (SubPhaser subgenome assignment)
• Yang Z 2007 PAML manual (yn00 codon Ks)
• Smith SD et al 2018 Trends Genet 34:825 (vertebrate WGD review)
• Cheng F et al 2023 (wgd v2 documentation, Polyploidy book chapter)

Related Skills

• comparative-genomics/synteny-analysis - Synteny-anchored Ks (wgd syn / GENESPACE)
• comparative-genomics/ortholog-inference - Ortholog detection feeds DupGen / wgd
• comparative-genomics/gene-tree-species-tree-reconciliation - Whale.jl native WGD modeling; ALE with WGD branch
• comparative-genomics/gene-family-evolution - CAFE5 birth-death often shows post-WGD retention bias
• comparative-genomics/positive-selection - Selection on retained ohnologs (post-WGD diversification)
• comparative-genomics/ancestral-reconstruction - Ancestral pre-WGD gene state
• phylogenetics/divergence-dating - Time-calibrated WGD positioning
• alignment/multiple-alignment - Codon-aware MSA for Ks computation
• alignment/pairwise-alignment - Pairwise Ks via PAML yn00 / codeml
• genome-assembly/assembly-qc - BUSCO / Compleasm before WGD analysis