Version Compatibility

Reference examples tested with: CAFE5 5.1.0+ (Mendes et al 2020 Bioinformatics 36(22-23):5516-5518), Count 11.0319+ (Csurös 2010 Bioinformatics 26:1910), BadiRate 1.35+ (Librado 2012 Bioinformatics 28:279), DupliPHY-Family (Ames et al 2012), CAFExp (legacy CAFE 4.2 -- DEPRECATED; use CAFE5), OrthoFinder 3.0+ for HOG input, R 4.4+, mclust 6.1+, phytools 2.3+, ETE4 4.1.0+ for tree manipulation. ALE/GeneRax/AleRax in companion skill [[gene-tree-species-tree-reconciliation]].

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

• CLI: cafe5 --help; Count.exe (Java); badirate --help
• R: packageVersion('phytools')
• Python: pip show ete4

If code throws CAFE5: lambda did not converge, Count negative branch length, BadiRate gamma not initialized, the most common causes are: (1) annotation heterogeneity inflating family sizes, (2) saturated families (CAFE5 needs reasonable rate variation), (3) negative branch lengths in input tree (Count requires ultrametric). Pre-process: filter OG matrix to families present in >= 50% of species; resolve polytomies; ultrametricize tree.

Gene Family Evolution

"Which gene families expanded or contracted in which lineages?" -> Birth-death models on phylogeny (Hahn 2005; Csurös 2010) treat each orthogroup's per-species count as evolving under a stochastic birth-death process; lineage-specific rate shifts are detected as departures from a global rate. Annotation heterogeneity is the single largest confounder: different annotation pipelines predict different numbers of genes per family, producing apparent lineage-specific expansions that are artifacts of annotation choice (Tonkin-Hill 2020 demonstrated this for bacterial pangenomes). Consistent annotation + BUSCO/Compleasm completeness filtering are mandatory before any birth-death model interpretation. CAFE5 (Mendes et al 2020 Bioinformatics 36:5516) replaces older CAFE versions with gamma-distributed rate categories for more biologically realistic modeling.

• CLI: cafe5 -i orthogroup_counts.tsv -t species_tree.nwk -p -- main CAFE5 workflow
• CLI: cafe5 -e -- error-aware mode for annotation uncertainty
• CLI: Count -- Java GUI / CLI for parsimony + likelihood ASR
• CLI: badirate -t tree.nwk -d counts.tsv -- likelihood birth-death + branch parsimony

Algorithmic Taxonomy

Tool	Approach	Output	Strength	Fails when
CAFE5 (Mendes et al 2020 Bioinformatics 36(22-23):5516-5518)	Birth-death with gamma rate categories	Global / per-family lambda + significant rate shifts	Modern standard; handles rate heterogeneity; explicit Type-I control	Annotation heterogeneity confounds; needs > 100 families
CAFE5-error	Annotation-error-aware extension	Same plus error estimates	Critical for noisy annotations	Manual error-rate specification or estimation
Count (Csurös 2010 Bioinformatics 26:1910)	Both ML and parsimony ASR	Branch event counts (D, L) per family	Comprehensive output; GUI	Slower than CAFE5; less modern UX
BadiRate (Librado 2012 Bioinformatics 28:279)	Likelihood birth-death + branch parsimony	Lineage-specific rate shifts	Combines stochastic + parsimony	Less commonly used; older
DupliPHY-Family (Ames et al 2012)	Per-family birth-death	Ancestral counts per family	Family-level granularity	Older; less integrated with modern OrthoFinder
ALE / GeneRax / AleRax (Szöllősi 2013; Morel 2024)	Per-family DTL reconciliation	Per-family D/T/L event counts	Direct integration with [[gene-tree-species-tree-reconciliation]]	Slower; per-family rather than across-family
CAFExp / CAFE 4.2 (DEPRECATED)	Earlier CAFE	--	Historical	Use CAFE5
Whale.jl with WGD (Zwaenepoel 2019)	Bayesian DL+WGD	WGD-aware family dynamics	Native WGD integration	Julia ecosystem
Functional enrichment downstream	clusterProfiler / topGO on expanded/contracted	GO/KEGG enrichment	Standard	Multiple testing across families

Methodology evolves; CAFE5 is the modern standard; verify the current CAFE5 manual (hahnlab/CAFE5) and Hahn lab papers before locking on a single approach.

Decision Tree by Experimental Scenario

Scenario	Recommended approach	Why
Standard CAFE-style birth-death analysis	CAFE5 with gamma rate categories	Modern standard; handles rate variation
Annotation-pipeline-heterogeneity	CAFE5-error mode	Explicit error modeling
Post-WGD retention bias	CAFE5 + DupGen_finder classification + functional enrichment	Combine birth-death with WGD-specific analysis
Lineage-specific gene-family-rate shifts correlated with phenotype	CAFE5 with binary phenotype	Standard CAFE workflow
Ancestral gene-family counts at internal nodes	Count ASR	Per-node count posteriors
Test for "fast-evolving" family on specific lineage	CAFE5 lambda-tree (per-clade lambda)	Compares lambdas across clades
Functional enrichment in expanded families	clusterProfiler / topGO on expansion lists	Standard
HGT-affected families (prokaryotes)	ALE / GeneRax per-family DTL (see [[gene-tree-species-tree-reconciliation]])	DTL framework explicit
Test if all families share single lambda	CAFE5 global lambda hypothesis	Restricted model
Specific family analysis (e.g. immune gene family)	ALE / AleRax per-family	Per-family detail
Distinguish gain from loss as primary driver	Count separate D and L counts	Standard parsimony
Convergent gene-family-rate shifts	RERconverge on family-count vectors	Trait-correlated rate shifts
Identify ancestral pan-clade family complement	CAFE5 ASR at MRCA	Pre-radiation family complement
Sub-clade-specific expansions in plant genomes	CAFE5 with clade-specific lambda	Compare angiosperm to gymnosperm

Per-Tool Failure Modes

Annotation heterogeneity inflating expansions / contractions

Trigger: Running CAFE5 on counts from genomes annotated by different pipelines (Augustus, MAKER, BRAKER, NCBI).

Mechanism: Different annotation tools predict different numbers of genes per family; the same biological gene family may be annotated with 5 genes in BRAKER and 8 in MAKER. CAFE5 sees the difference as an "expansion" in the MAKER-annotated species (Tonkin-Hill 2020 documented this for bacterial pangenomes; same principle in eukaryotes).

Symptom: "Most expanded families" cluster in species annotated by a single pipeline (often more permissive tool); per-species "expansion rate" correlates with annotation pipeline rather than biology.

Fix: Re-annotate all genomes with a single pipeline (currently BRAKER3 or Funannotate for eukaryotes; Bakta for bacteria) before CAFE5. Alternatively use CAFE5-error mode with explicit error rates per species. Document annotation pipeline + version per species.

Assembly fragmentation creating false contractions

Trigger: Including draft assemblies with low N50 in CAFE5 analysis.

Mechanism: Fragmented assemblies miss genes; the same family appears with fewer genes than expected. CAFE5 reports this as a contraction in the affected species.

Symptom: "Contracted" families in fragmented assemblies; correlation between BUSCO completeness and CAFE5 "contraction" rate; per-species missing-gene count varies 5-10x.

Fix: Require >= 90% BUSCO/Compleasm completeness for inclusion. Exclude species with > 5% lower BUSCO than median. Document N50 + BUSCO per assembly. For unavoidably fragmented assemblies, exclude from CAFE5 or use CAFE5-error with empirical error estimates.

CAFE5 lambda non-convergence

Trigger: CAFE5 reports "lambda did not converge"; lambda jumps between values across runs.

Mechanism: Insufficient data (< 100 families); strong rate heterogeneity not captured; or input tree non-ultrametric.

Symptom: lambda estimate unstable; AIC of model selection variable.

Fix: Require >= 100 orthogroups (preferably > 1000) in input. Ensure tree is ultrametric (ape::chronos() or treePL); CAFE5 expects time-scaled tree. Use gamma rate categories (CAFE5 default -k 4) for rate heterogeneity. If still non-convergent, restrict to single-copy or small families; check tree branch lengths for negative values.

Gamma rate-category misinterpretation

Trigger: Reporting "gamma rate categories" as biological gene-family clusters.

Mechanism: CAFE5 gamma categories are statistical buckets representing rate heterogeneity across families; they're not "fast-evolving family clusters" with biological meaning per se.

Symptom: Confusion in interpretation; "category-1 families are special."

Fix: Treat gamma categories as a statistical device. Report per-family lambda (estimated under per-family rate model) or per-clade lambda. Functional interpretation comes from family-level statistical tests, not category assignment.

Multiple-testing across many families

Trigger: Reporting "significant" expansions / contractions without correction.

Mechanism: With ~10,000 families tested, ~500 will be significant at p=0.05 under H0. Without correction, false-discovery rate is high.

Symptom: Long lists of "expanded" families; functional enrichment dominated by chance hits.

Fix: Apply FDR (Benjamini-Hochberg) across families; or restrict to a priori hypothesized families. CAFE5 reports per-family p-values; FDR-correct downstream.

Tree non-ultrametric / negative branches

Trigger: Using ML tree directly (substitutions per site) as input to CAFE5.

Mechanism: CAFE5 expects ultrametric (time-calibrated) tree; substitution-based branch lengths are not time-calibrated and may even have negative branches after rate variation.

Symptom: CAFE5 errors with "negative branch length"; or produces unreliable lambda estimates.

Fix: Time-calibrate the tree: use ape::chronos(), treePL (Smith & O'Meara 2012 Bioinformatics 28:2689), or LSD2 (To et al 2016 Syst Biol 65:82; gascuel-lab/LSD2) for fast NP-like calibration. Or use existing time-calibrated tree from TimeTree database.

Outlier-family-driven lambda estimate

Trigger: Including extreme-size families (e.g., NLR resistance gene clusters in plants with 500+ members) in CAFE5.

Mechanism: Birth-death model's likelihood is dominated by large families; one or two outlier families can overwhelm the global lambda estimate.

Symptom: Excluding the top-5 largest families changes lambda by > 30%; lambda confidence intervals huge.

Fix: Robust analysis: report lambda with and without outliers; consider per-family lambda for largest families. Functional annotation of outliers reveals if they're biologically expected expansions (rapidly evolving gene families).

Convergent gene-family-rate shifts not captured

Trigger: Phylogenomic question is whether multiple independent lineages show similar gene-family-rate shifts.

Mechanism: CAFE5 doesn't natively test for convergent rate shifts; per-clade lambda is the closest analog.

Symptom: Manual inspection of expansions across independent lineages shows pattern; CAFE5 doesn't formalize it.

Fix: Combine CAFE5 per-family lambdas with RERconverge (Redlich et al 2024 MBE 41:msae210) for trait-correlated rate shifts; use CSUBST (Fukushima 2023 Nat Eco Evo 7:155) for convergent substitution patterns.

CAFE5 vs ALE for HGT-affected family

Trigger: Applying CAFE5 to bacterial families where transfer is common.

Mechanism: CAFE5 models birth-death (D, L) only; ignores transfer (T). For HGT-affected families, the dynamics include T events that CAFE5 cannot capture.

Symptom: CAFE5 lambda for bacterial families is implausibly high; family counts don't fit birth-death model.

Fix: Use ALE / GeneRax / AleRax for HGT-affected families ([[gene-tree-species-tree-reconciliation]]); CAFE5 is appropriate for vertical-inheritance-dominated families. Combine: CAFE5 for the bulk; ALE for HGT-confirmed families.

Family classification (single-copy / multi-copy) affecting interpretation

Trigger: Treating single-copy orthogroups (always = 1 per species) and multi-copy uniformly.

Mechanism: Single-copy OGs have no count variation; including them dilutes the analysis. Multi-copy families show meaningful variation.

Symptom: Including single-copy OGs lowers lambda; per-family results dominated by them.

Fix: Restrict CAFE5 input to multi-copy orthogroups (max count >= 2 in any species). Filter via OrthoFinder HOG-classification single-copy column.

Quantitative Thresholds

Quantity	Threshold	Source / Rationale
Minimum orthogroups for CAFE5	>= 100; preferably > 1000	Mendes et al 2020 Bioinformatics 36:5516
Maximum gene-family count	depends on tree depth; typical < 1000 per family	Practical
BUSCO/Compleasm completeness	>= 90% for inclusion	Standard
FDR for expansions / contractions	q < 0.05 (Benjamini-Hochberg)	Standard
Significant lambda shift	LRT p < 0.05 / corrected for clade tests	CAFE5 LRT
Gamma categories	4 default (-k 4)	CAFE5 docs
Tree ultrametricity	required; calibrate with ape::chronos or treePL	CAFE5 requirement
Per-clade lambda significance	LRT vs global lambda	CAFE5 docs
Annotation pipeline	one pipeline across all species; document version	Best practice
Functional enrichment q-value	q < 0.05 (BH-corrected)	Standard
Multi-copy family filter	max count across species >= 2	Practical
Tree time-calibration source	TimeTree, treePL, LSD2	Standard alternatives
Excluded singletons	yes, in some workflows	Standard
Outlier family exclusion threshold	top 5% by max count, exclude or per-family lambda	Robust
Convergent shift detection	RERconverge or CSUBST	Companion methods

CAFE5 Standard Workflow

Goal: Identify gene families with lineage-specific rate shifts (expansions / contractions).

Approach: Prepare ortholog count matrix -> ultrametricize tree -> run CAFE5 with gamma categories -> FDR-correct -> annotate.

# 1. Generate ortholog matrix from OrthoFinder v3 HOG file (inline Python; no external script needed)
python3 <<'PY'
import pandas as pd
hog = pd.read_csv('Phylogenetic_Hierarchical_Orthogroups/N0.tsv', sep='\t')
sp_cols = [c for c in hog.columns if c not in ('HOG', 'OG', 'Gene Tree Parent Clade')]
counts = pd.DataFrame({sp: hog[sp].fillna('').apply(
    lambda x: len(str(x).split(',')) if x and ',' in str(x) else (1 if str(x).strip() else 0)
) for sp in sp_cols})
counts.insert(0, 'family_id', hog['HOG'])
counts.insert(0, 'Description', '(null)')
counts.to_csv('cafe_input.tsv', sep='\t', index=False)
PY

# Output format: Description  family_id  Species1  Species2  ... (counts per species)

# 2. Ultrametricize tree
Rscript -e "
library(ape)
tree <- read.tree('SpeciesTree_rooted.txt')
tree_ultra <- chronos(tree)
write.tree(tree_ultra, 'tree_ultrametric.nwk')
"

# 3. Run CAFE5 with gamma categories
cafe5 \
    -i cafe_input.tsv \
    -t tree_ultrametric.nwk \
    -p \
    -k 4 \
    -e \
    -o cafe_output
# `-e` (no argument) lets CAFE5 estimate a global error model. To supply a pre-built
# error model file, use `-eerror.txt` (CAFE5 concatenates the flag and argument).
# Verify with `cafe5 --help`.

# Output:
#   cafe_output/Base_results.txt        Per-family results
#   cafe_output/Base_clade_results.txt   Per-clade lambda
#   cafe_output/Base_asr.tre            ASR tree
#   cafe_output/Base_clade_results.txt   LRT against null

# 4. FDR-correct across families (inline Python; see filter_significant_families() below)

'''Filter expanded / contracted families with FDR-correction.'''
import pandas as pd
from statsmodels.stats.multitest import multipletests


def filter_significant_families(cafe_results_path, change_table_path, fdr_threshold=0.05):
    '''Identify families with significant rate shifts and their per-branch expansion/contraction direction.

    CAFE5 outputs (Base_family_results.txt or similar) include a per-family p-value (vs the null
    of a single global lambda). The per-branch expansion/contraction comes from a separate
    `Base_change.tab` file (per-family x per-branch count change). Per-family lambda is NOT
    a column in Base_family_results.txt; that's a global parameter.
    '''
    df = pd.read_csv(cafe_results_path, sep='\t')
    pcol = 'p-value' if 'p-value' in df.columns else 'pvalue'
    df['fdr'] = multipletests(df[pcol].fillna(1.0), method='fdr_bh')[1]
    significant = df[df['fdr'] < fdr_threshold]

    # Pair with per-branch change table; positive cell = expansion on that branch
    changes = pd.read_csv(change_table_path, sep='\t')
    return {'significant_families': significant, 'per_branch_changes': changes}

CAFE5-error for Annotation Heterogeneity

# Prepare per-species error rates (from BUSCO completeness or empirical)
cat > error_rates.tsv << 'EOF'
species_id    error_rate
Species_A     0.05
Species_B     0.08
Species_C     0.03
EOF

# Run with error-aware mode (supply pre-built error model file).
# CAFE5 concatenates the -e flag with its argument (no space): -e<error_model_file>
cafe5 \
    -i cafe_input.tsv \
    -t tree_ultrametric.nwk \
    -p \
    -k 4 \
    -eerror_model.txt \
    -o cafe_error_output

Count for Per-Branch Ancestral State

# Count requires Java
java -jar Count.jar -i count_format.tsv -t species_tree.nwk \
    -o count_output --ancestral_counts

# Per-branch D and L counts in count_output/branch_events.tsv

Functional Enrichment Downstream

library(clusterProfiler)
library(org.Hs.eg.db)  # or appropriate species DB

# Enrichment of expanded families
expanded_genes <- read.csv('expanded_genes.tsv', stringsAsFactors = FALSE)$gene_id

go_enrich <- enrichGO(gene = expanded_genes,
                      OrgDb = org.Hs.eg.db,
                      ont = 'BP',
                      pAdjustMethod = 'BH',
                      pvalueCutoff = 0.05)

kegg_enrich <- enrichKEGG(gene = expanded_genes,
                          organism = 'hsa',
                          pvalueCutoff = 0.05)

Reconciliation: When Methods Disagree

Pattern	Likely cause	Action
CAFE5 significant; ALE shows no DTL signal	CAFE5 detects count change; ALE detects events	Both can be true; CAFE5 is count-level, ALE is event-level
CAFE5 significant in bacterial family	Possibly HGT-driven; CAFE5 ignores T	Re-run with ALE for HGT-affected families
Per-clade lambda differs in CAFE5 vs uniform lambda	Real rate heterogeneity	Trust per-clade
Annotation heterogeneity hypothesis	Re-annotation eliminates "expansion"	Confirm annotation artifact; report as such
CAFE5 + RERconverge agree on expansion + trait shift	Convergent biological mechanism	Strong evidence
Outlier-family-driven global lambda	Top-5 families distort estimate	Report robust lambda; manually flag outliers
CAFE5 expansion in fragmented species	False; assembly fragmentation	Re-assess BUSCO; exclude or correct
Single-copy family flagged as significant	Statistical artifact; no variation	Exclude single-copy OGs; restrict to multi-copy
Whale.jl WGD branch shows D burst, CAFE5 shows expansion	Same event; WGD modeling preferred	Whale.jl is more biological for known WGD
Count parsimony vs CAFE5 likelihood disagree	Parsimony underestimates losses	Trust CAFE5 likelihood

Operational rule for publication: CAFE5 with gamma rate categories + annotation pipeline normalized + BUSCO completeness > 90% + FDR-corrected significance + functional enrichment of expanded/contracted + (for bacteria) ALE complement = publication-grade gene-family evolution analysis.

Cohort Gotchas

• WGD lineages: post-WGD retention bias; gene balance hypothesis (Birchler & Veitia 2007); analyze with [[whole-genome-duplication]] context
• Plant gene families: NLR clusters (resistance) and ribosomal proteins are inherently large; expect lineage variation
• Mammalian gene families: olfactory receptors are highly variable; expect lineage-specific changes
• Bacterial gene families: HGT-driven dynamics; use ALE/AleRax instead of CAFE5
• Polyploid species: subgenome assignment first; analyze each subgenome separately
• Rapidly evolving lineages: higher branch-specific rates; per-clade lambda model
• Conserved species (e.g., extant cyanobacteria): lambda may be very low; few changes to detect
• Recent radiations: insufficient time for divergence; CAFE5 may have low power
• Highly fragmented MAGs: include only high-quality MAGs

Anticipated Reviewer Pushback

Pushback	Standard response
"Annotation pipeline?"	Single pipeline (BRAKER3 / Funannotate / Bakta) across all species; version pinned
"BUSCO completeness?"	>= 90% required; per-species reported
"Assembly fragmentation?"	N50 reported; species with > 5% lower BUSCO than median excluded
"Multiple testing?"	FDR (Benjamini-Hochberg) applied across families
"Lambda non-convergence?"	CAFE5 with -k 4 gamma categories; tree ultrametricized
"Outlier families?"	Robust analysis with and without; outliers individually annotated
"HGT in bacteria?"	ALE / GeneRax cross-checked for HGT-affected families
"Functional enrichment?"	clusterProfiler / topGO with FDR; pathway-level interpretation
"Tree time-calibration?"	TimeTree-based or treePL/LSD2; documented
"WGD effects?"	DupGen_finder + WGD-specific analysis; subgenome-aware
"Convergent shifts?"	RERconverge complementary analysis

Common Errors

Error / symptom	Cause	Solution
CAFE5 "lambda did not converge"	Insufficient data or non-ultrametric tree	Ultrametricize; increase families; check tree
CAFE5 "negative branch length"	ML tree input	Time-calibrate with chronos / treePL / LSD2
CAFE5 errors on input format	Wrong column order	Check OrthoFinder HOG format; species in column 2 onward
Count GUI hangs	Java memory	Increase Java heap: java -Xmx16G -jar Count.jar
BadiRate gamma errors	Initialization issue	Use default -g 1 or specify per family
Per-clade lambda implausibly high	Outlier family or tree issue	Exclude top families; re-run
All families significant	No FDR correction	Apply BH correction
Annotation heterogeneity not addressed	Mixed pipelines	Re-annotate consistently
Family with 0 count for all species	Annotation issue	Filter rows with all zeros
Highly variable family count (e.g. 500-5000)	Real biological variation or annotation	Annotate manually; consider exclusion

Tool Installation Notes

# CAFE5
conda install -c bioconda cafe
# Or: git clone https://github.com/hahnlab/CAFE5

# Count
wget http://www.iro.umontreal.ca/~csuros/gene_content/count.tar.gz
tar xf count.tar.gz

# BadiRate
git clone https://github.com/PauloRoldan/badirate

# DupliPHY-Family
# Web only; no public CLI

# Whale.jl (Julia) -- see [[gene-tree-species-tree-reconciliation]]
julia -e 'using Pkg; Pkg.add("Whale")'

# R packages
install.packages(c('ape', 'phytools', 'clusterProfiler', 'org.Hs.eg.db'))

# Time calibration
conda install -c bioconda treepl
# Or use ape::chronos (R)

# For OrthoFinder input
conda install -c bioconda orthofinder

For Funannotate / BRAKER3 reannotation (essential pre-CAFE5):

conda install -c bioconda funannotate braker3

References

• Hahn MW et al 2005 Genome Res 15:1153 (CAFE original framework)
• Mendes FK et al 2020 Bioinformatics 36:5516 (CAFE5)
• Csurös M 2010 Bioinformatics 26:1910 (Count)
• Librado P et al 2012 Bioinformatics 28:279 (BadiRate)
• Ames RM et al 2012 Bioinformatics 28:48 (DupliPHY-Family)
• Tonkin-Hill G et al 2020 Genome Biol 21:180 (Panaroo; annotation heterogeneity)
• Smith SA & Dunn CW 2008 Bioinformatics 24:715 (Phyutility)
• Smith SA & O'Meara BC 2012 Bioinformatics 28:2689 (treePL)
• To T-H, Jung M, Lycett S & Gascuel O 2016 Syst Biol 65:82 (LSD2)
• Birchler JA & Veitia RA 2007 Plant Cell 19:395 (gene balance)
• Redlich R et al 2024 MBE 41:msae210 (RERconverge categorical)
• Fukushima K & Pollock DD 2023 Nat Eco Evo 7:155 (CSUBST)
• Lynch M & Conery JS 2000 Science 290:1151 (gene duplication mechanism)
• Force A et al 1999 Genetics 151:1531 (subfunctionalization)
• De Bie T et al 2006 Bioinformatics 22:1269 (CAFE original software)
• Han MV et al 2013 MBE 30:1987 (CAFE 3)
• Otto SP & Whitton J 2000 Annu Rev Genet 34:401 (polyploidy mechanisms)
• TimeTree (database, http://www.timetree.org)

Related Skills

• comparative-genomics/ortholog-inference - OrthoFinder HOG matrix is CAFE5 input
• comparative-genomics/gene-tree-species-tree-reconciliation - ALE per-family DTL, complement to CAFE5
• comparative-genomics/whole-genome-duplication - Post-WGD retention bias context
• comparative-genomics/positive-selection - Selection within expanded families
• comparative-genomics/ancestral-reconstruction - Ancestral count reconstruction
• phylogenetics/divergence-dating - Time-calibrated tree for CAFE5
• phylogenetics/modern-tree-inference - Species tree input
• pathway-analysis/go-enrichment - Functional enrichment of expanded families
• pathway-analysis/gsea - GSEA on family expansions
• single-cell/cell-annotation - Cell-type-specific gene-family expansions