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name: bio-comparative-genomics-whole-genome-alignment description: Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools. origin: openai4s category: bioskills/comparative-genomics metadata: tool_type: cli primary_tool: Cactus third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a license: MIT

Version Compatibility

Reference examples tested with: Progressive Cactus 2.9.1+ (ComparativeGenomicsToolkit/cactus; Armstrong 2020 Nature 587:246), Minigraph-Cactus (Hickey 2024 Nat Biotech 42:663; bundled with Cactus 2.5+), HAL toolkit 2.3+ (Hickey 2013 Bioinformatics 29:1341), LASTZ 1.04.22+, UCSC kentUtils for chain/net (Kent 2003 PNAS 100:11484), MUMmer 4.0.0+, minimap2 2.28+, AnchorWave 1.2.5+, progressiveMauve 2.4.0+, sibeliaz 1.2.5+, winnowmap 2.03+ (Jain 2022 Nat Methods 19:705). Toil workflow runner 6.0+ for Cactus on HPC/cloud.

Before using code patterns, verify installed versions match. If versions differ: - CLI: cactus --help, cactus-pangenome --help, halStats --help, lastz --version, nucmer --version, minimap2 --version - Python: pip show toil, toil --version

If code throws Toil workflow restart failure, HAL file corrupted, WDL workflow missing, the Cactus pipeline is Toil-based and requires careful checkpointing; failed runs must be restarted with --restart. HAL file versions differ across hal-toolkit releases; pin the version that produced the file.

Whole Genome Alignment

"Align these multiple genomes at the base-pair level" -> Choose between reference-free progressive alignment (Cactus / Minigraph-Cactus: produces HAL, no privileged reference) and reference-anchored pairwise alignment (LASTZ chains/nets, MUMmer, minimap2: one genome is the reference, queries align to it). The fundamental tradeoff is scale vs structure: pairwise pipelines scale linearly per pair but lose multi-way relationships; progressive Cactus scales linearly with a tree but quadratically without and produces ancestrally-coherent alignments. For comparative genomics at vertebrate / mammal scale, Cactus is now the standard substrate (Zoonomia, Christmas 2023 Science 380:eabn3943; Bird10000 Genomes); for pangenome graph construction, Minigraph-Cactus (Hickey 2024) is the production pipeline.

Algorithmic Taxonomy

Tool Approach Output Strength Fails when
Progressive Cactus (Armstrong 2020 Nature 587:246) LASTZ pairwise -> CAF graph -> BAR -> ancestral assembly per node HAL multi-genome alignment Reference-free; scales to 1000s of genomes with phylogenetic tree; ancestral assembly at every internal node Quadratic without tree; ALSO scaling problems if branch_scale and chaining parameters mis-tuned (Armstrong 2020 Supp Methods)
Minigraph-Cactus (Hickey 2024 Nat Biotech 42:663) minigraph SV graph base + Cactus base-level resolution Pangenome graph (VCF, GFA, BAM) Combines structural variation (minigraph) with base-level alignment (Cactus); HPRC standard Requires reference; designed for intra-species pangenomes (90 human haplotypes)
LASTZ + UCSC chain/net (Kent 2003 PNAS 100:11484) LASTZ local alignments -> chains (collinear) -> nets (one-to-one selection) Reference-anchored alignment (chain, net, MAF) Standard for UCSC genome browser tracks; well-validated; pairwise interpretable Reference-bias; quadratic scaling for N genomes; per-pair manual chain/net pipeline
MUMmer4 nucmer (Marçais 2018 PLoS Comp Biol 14:e1005944) Maximal exact matches -> clustering -> alignment extension Pairwise alignment (delta format) Fast for closely related (>= 70% identity); robust Slow at low identity; pairwise only
minimap2 (Li 2018 Bioinformatics 34:3094) Minimizer-based seeding + base-level alignment SAM / PAF pairwise Very fast; preset -x asm5/10/20 for divergence Lower accuracy at < 70% identity vs LASTZ
AnchorWave (Song 2022 PNAS 119:e2113075119) CDS/exon anchors -> wavefront alignment Sequence-level synteny + SV WGD-aware (ploidy parameter); plant-friendly CDS-anchored; intergenic resolution limited
Progressive Mauve (Darling 2010 PLoS One 5:e11147) Local collinear blocks (LCBs) -> progressive alignment Multi-genome bacterial alignment Bacterial-friendly; handles inversions natively Slow for > 30 genomes; less common today
SibeliaZ (Minkin & Medvedev 2020 Nat Commun 11:6327) de Bruijn graph + colinear blocks Bacterial / closely related multi-genome alignment Scales to 100+ genomes Highly diverged genomes lose recall
HAL toolkit (Hickey 2013 Bioinformatics 29:1341) Hierarchical Alignment Format Index, slice, project, extract from HAL Standard substrate for downstream Cactus tools (TOGA, halSynteny, halLiftover) HAL file version pinning required
Winnowmap2 (Jain 2022 Nat Methods 19:705) minimap2-derived with masked repetitive minimizers SAM / PAF Optimized for highly repetitive regions (centromeres, telomeres) Niche; for repeat-rich pairwise alignment
BLASTZ (legacy) LASTZ predecessor Local alignments Historical; superseded by LASTZ Use LASTZ instead
Multiz (Blanchette 2004 Genome Res 14:708) Reference-anchored chain-merging Multi-species MAF UCSC's older multi-species pipeline Superseded by Cactus
Lagan / mLagan (Brudno 2003) Local-global hybrid alignment Pairwise / multi-species Historical; not actively developed Use Cactus or LASTZ chain/net

Methodology evolves; Cactus / Minigraph-Cactus / HAL are now the dominant production pipelines for vertebrate / mammal / plant WGA. LASTZ chains/nets remain the standard for adding a single new species to a reference-anchored ecosystem (UCSC, Ensembl).

Decision Tree by Experimental Scenario

Scenario Recommended approach Why
Vertebrate clade WGA, 10-500 genomes Progressive Cactus Reference-free; phylogenetic-tree guided; HAL output
Mammalian Zoonomia-scale WGA, 500-2000 genomes Progressive Cactus with --branchScale tuning + HPC Cactus scales to 1000s with proper Toil config
Plant clade WGA, with WGD AnchorWave proali OR Cactus AnchorWave WGD-aware; Cactus more general
Bacterial / archaeal genome alignment progressiveMauve OR SibeliaZ Designed for compact, rearrangement-rich genomes
Single new species added to reference (e.g. UCSC, Ensembl track) LASTZ + chain/net Standard reference-anchored pipeline; interpretable
Closely related strains / haplotypes (~95% identity) minimap2 -x asm5 or MUMmer nucmer Fast pairwise; sufficient accuracy
Cross-species ~70-90% identity LASTZ with --strategy tuned minimap2 loses accuracy below ~70%
Cross-species ~50% identity (e.g. human vs zebrafish) LASTZ with HoxD55 matrix Specialized parameters for distant genomes
Pangenome graph for variant calling Minigraph-Cactus or PGGB See [[pangenome-analysis]] for graph-based variant calling
Repeat-rich genome (large mammal, plant) Cactus with masked input Pre-mask repeats with RepeatMasker/RepeatModeler2
Centromeric / telomeric alignment Winnowmap2 Optimized for repetitive minimizers
Comparative annotation projection Cactus -> TOGA + CESAR (see [[comparative-annotation-projection]]) Cactus HAL is TOGA input
Conservation analysis (phyloP, PhastCons) Cactus -> hal2maf to MAF for phyloP MAF format from HAL is the substrate
Pairwise alignment for synteny minimap2 -x asm5 -> SyRI See [[synteny-analysis]]
Long-read assembly pairwise validation nucmer + dnadiff Standard for assembly QC
Reference-to-pangenome lift-over halLiftover or vg paths HAL coordinate-system tools

Per-Tool Failure Modes

Cactus running quadratically without species tree

Trigger: Running cactus without --guide-tree or with a poorly-resolved guide tree.

Mechanism: Cactus is designed as a progressive aligner: it aligns siblings, then their ancestor, recursively up the species tree. Without a guide tree, it tries all-vs-all alignment, which is O(N^2) and explodes for > 10 genomes.

Symptom: Cactus job time scales as N^2; cluster runs hit wall time before completing.

Fix: Always provide a guide tree in seqFile.txt:

(((human:0.05, chimp:0.05):0.05, macaque:0.1):0.2, mouse:0.5);
human    /path/to/human.fa
chimp    /path/to/chimp.fa
macaque  /path/to/macaque.fa
mouse    /path/to/mouse.fa

Branch lengths in substitutions per site; need not be precise but must reflect relative divergence. For poorly-resolved trees, use STAR (concatenation) tree from a few hundred concatenated single-copy orthologs (cf. [[ortholog-inference]]).

Branch-scale parameter mis-tuned

Trigger: Cactus alignment producing very few or very many alignment columns relative to expected.

Mechanism: Cactus uses --branchScale to scale internal-branch lengths for LASTZ chaining parameters. Default scaling assumes vertebrate-like substitution rates; plants, bacteria, and viruses need adjustment. Wrong scaling produces under-aligned (too low) or over-merged (too high) blocks.

Symptom: HAL file shows alignment column count vastly different from expected; halStats reports unusual coverage; comparative annotation downstream fails.

Fix: Adjust --branchScale per clade: vertebrates default 1.0; plants 0.5-0.7; bacteria 0.3-0.5; viruses 0.1-0.3. Validate against known orthologs (BUSCO single-copies should align across all genomes).

Toil checkpoint restart failures

Trigger: Cactus job interrupted (cluster timeout, OOM, node failure) and restart.

Mechanism: Cactus uses Toil's job store for checkpointing. Restart requires identical jobStore path, identical config, and pointing to the same machine type (some cloud-stored jobs require AWS / Google Cloud credentials).

Symptom: Cactus restart errors with "job store inconsistent" or "missing intermediate file."

Fix: Use cactus --restart jobStore seqFile.txt output.hal. For HPC, ensure the jobStore path is on shared storage accessible to all nodes. For cloud, use S3 or Google Cloud Storage with proper credentials. If restart fails, restart from scratch with a new jobStore.

Reference bias in LASTZ chain/net

Trigger: Reporting "human-chimp synteny" from LASTZ chains/nets with human as reference.

Mechanism: Chain/net is reference-anchored; the alignment is biased toward the reference's genomic context. Repeat-content differences and assembly-quality differences between reference and query produce alignment artifacts.

Symptom: Different chains/nets from different reference choices show inconsistent SV calls; e.g. mouse-as-reference and human-as-reference produce different INV counts.

Fix: For multi-genome questions, use Cactus (reference-free). For pairwise questions, run chain/net both directions (A-on-B and B-on-A) and report consistent calls. Document reference choice in methods. SyRI on minimap2 alignments has similar issues; cross-validate.

minimap2 sensitivity loss at < 70% identity

Trigger: Running minimap2 -x asm10 or -x asm20 between distantly related species.

Mechanism: Minimap2's minimizer-based seeding loses sensitivity at lower identity; many regions never anchor and produce gaps in alignment.

Symptom: Alignment coverage low (< 50% of query mapped); SyRI / SV-calling on this alignment reports few or no SVs; high-identity regions align but mid-identity regions don't.

Fix: Switch to LASTZ for < 70% identity; LASTZ uses 6-bp seeds with scoring matrices designed for distant comparison (HoxD55 for human-fish). For minimap2, try -x asm20 with manual sensitivity tuning (-k 19 -w 19) but expect inferior performance vs LASTZ. minimap2 documentation recommends presets up to 20% divergence.

Cactus producing wrong topology in ancestor assembly

Trigger: Cactus is asked to assemble an ancestral sequence at an internal node, but the input tree has wrong topology.

Mechanism: Cactus uses the guide tree to determine which descendants align to which ancestor. Wrong topology produces incorrect ancestor sequence at internal nodes.

Symptom: Ancestor sequences inconsistent with biological expectation; downstream phylogenetic analyses on Cactus ancestor sequences disagree with concatenation phylogenies.

Fix: Validate species tree before Cactus alignment using phylogenetics/modern-tree-inference on concatenated single-copy orthologs. If topology is uncertain, use a more conservative resolution (e.g. polytomy resolved via STRIDE).

Repeats inflating LASTZ chain count

Trigger: Running LASTZ on unmasked or weakly masked genomes.

Mechanism: Unmasked TEs produce millions of paralogous LASTZ alignments; chains form from these, creating false-positive synteny / alignment.

Symptom: Chain count >> 100,000 per pair; many short chains in TE-rich regions; chain/net pipeline produces excessive chains where biology predicts few.

Fix: Softmask both genomes (RepeatModeler2 + RepeatMasker) before LASTZ. LASTZ honors softmasked sequences in its seeding step (lowercase = ignored at seeding, full alignment after extension). Hardmasking (N's) is too aggressive and breaks LASTZ scoring.

HAL version mismatch breaking downstream tools

Trigger: Using HAL files generated with one version of Cactus / HAL toolkit with downstream tools using a different version.

Mechanism: HAL file format is versioned; minor version mismatch usually OK, major version mismatch causes silent or loud failures.

Symptom: halStats / TOGA / halLiftover errors about "unrecognized HAL version."

Fix: Use the HAL toolkit version that wrote the file (check via halStats --filter halVersion or git log of the run). For shared HAL files, pin Cactus version in documentation.

Insufficient masking before Cactus

Trigger: Cactus on unmasked vertebrate genomes.

Mechanism: Cactus's CAF/BAR algorithms identify candidate alignment columns; without masking, TE-derived blocks dominate. Memory consumption explodes and alignment results are mostly TE pseudo-alignments.

Symptom: Cactus runs out of memory; HAL file is huge (> 100 GB for what should be 10 GB); halStats shows enormous column count.

Fix: Mask all genomes with RepeatMasker (or species-specific RepeatModeler2 library) before Cactus. Soft-masking is acceptable; Cactus handles lowercase appropriately. Document masking strategy.

AnchorWave WGD parameter mis-set

Trigger: AnchorWave proali with wrong --ploidy value for the query genome.

Mechanism: AnchorWave's WGD-aware alignment matches each query CDS to ploidy-many reference CDS regions; wrong ploidy causes either undermatching (low ploidy) or overmatching (high ploidy).

Symptom: Reported alignment count inconsistent with biology (e.g. hexaploid wheat reports diploid-level matches).

Fix: Set --ploidy to query genome's ploidy (1 for haploid reference, 2 for diploid query when reference is diploid, 4 for tetraploid query vs diploid reference). Check with python3 -m jcvi.compara.synteny depth on output anchors.

Quantitative Thresholds

Quantity Threshold Source / Rationale
Cactus runtime scaling Linear with tree-balanced clade; quadratic without tree Armstrong 2020 Supp Methods
Cactus memory per genome (vertebrate, masked) 8-32 GB Armstrong 2020
Cactus minimum genome assembly N50 >= 1 Mb for reliable WGA; chromosome-level preferred Standard convention
Branch scale for vertebrates 1.0 default Cactus docs
Branch scale for plants 0.5-0.7 Tuning convention
Branch scale for bacteria 0.3-0.5 Tuning convention
LASTZ identity range 60-99% practical; 50% with HoxD55 matrix LASTZ docs
minimap2 -x asm5 <= 5% divergence minimap2 docs
minimap2 -x asm10 <= 10% divergence minimap2 docs
minimap2 -x asm20 <= 20% divergence minimap2 docs
MUMmer nucmer recommended identity >= 70% Marçais 2018
Soft-masking required for WGA >= 90% of TE families masked Standard QC
HAL file size, vertebrate 100-genome ~100-500 GB per HAL Approximate; varies with masking
Cactus per-job CPU requirement 4-16 cores per leaf Cactus docs
Minigraph-Cactus haplotype count tested at 90 human haplotypes Hickey 2024
LASTZ chain-net minimum chain length 5000 bp default; tunable UCSC convention
Toil retry limit --retryCount 3 typical Toil docs
Cactus dustmasking sensitivity default sensitive; adjust per clade Cactus configs
AnchorWave anchor minimum identity 80% default; lower for distant species AnchorWave docs
progressiveMauve maximum genomes (practical) 20-30 Darling 2010
SibeliaZ minimum block size 50 bp default; tunable Minkin 2020
HAL toolkit halLiftover precision base-level for collinear; gap-aware for SV Hickey 2013

Progressive Cactus Standard Workflow

Goal: Build a multi-species reference-free WGA from N genomes.

Approach: Provide guide tree + per-species genome FASTA in seqFile.txt -> run cactus with Toil job store -> produce HAL -> extract MAF / synteny / annotation as needed.

# 1. Pre-mask genomes (softmask)
for fa in genomes/*.fa; do
    species=$(basename $fa .fa)
    BuildDatabase -name ${species}_DB $fa
    RepeatModeler -database ${species}_DB -threads 16
    RepeatMasker -lib ${species}_DB-families.fa -xsmall -pa 16 $fa
done

# 2. Prepare seqFile (tree + paths)
cat > seqFile.txt << 'EOF'
(((human:0.05, chimp:0.05):0.05, macaque:0.1):0.2, mouse:0.5);
human    genomes/human.fa.masked
chimp    genomes/chimp.fa.masked
macaque  genomes/macaque.fa.masked
mouse    genomes/mouse.fa.masked
EOF

# 3. Run Cactus
cactus jobStore_path seqFile.txt output.hal \
    --binariesMode local \
    --workDir /tmp/cactus_work \
    --maxCores 64 \
    --logFile cactus.log

# 4. Inspect HAL
halStats output.hal
# Reports: genome count, chromosome / contig count per genome, alignment column count

# 5. Extract MAF for downstream phyloP / conservation
hal2maf output.hal reference_genome ucsc.maf --refGenome human --chunkSize 1000000

For HPC deployment, use Toil's --batchSystem slurm or --batchSystem kubernetes for cluster-scale runs.

LASTZ + UCSC Chain/Net Standard Pipeline

Goal: Pairwise WGA of a query genome to a reference, producing UCSC-format chain and net files.

Approach: LASTZ pairwise -> axtChain -> chainNet -> netSyntenic.

# 1. LASTZ pairwise alignment per chromosome
for chr in $(cut -f1 reference.fa.fai); do
    lastz reference.fa[multiple,unmask] query.fa[multiple,unmask] \
        --hspthresh=3000 --ydrop=9400 --gappedthresh=3000 \
        --ambiguous=iupac --inner=2000 --score=$LASTZ_PARAMS \
        --format=axt \
        > pairwise/${chr}.axt
done

# 2. Build chains. `axtChain` reads PSL when given `-psl`; for AXT input from lastz
# (`--format=axt`) drop the `-psl` flag, or emit PSL from LASTZ with `--format=psl` upstream.
for axt in pairwise/*.axt; do
    chr=$(basename $axt .axt)
    axtChain -linearGap=loose $axt reference.2bit query.2bit chains/${chr}.chain
done

# 3. Merge chains
chainMergeSort chains/*.chain > merged.chain
chainPreNet merged.chain reference.size query.size pre.chain

# 4. Build net (one-to-one selection)
chainNet pre.chain reference.size query.size \
    target.net query.net
netSyntenic target.net target.syntenic.net

# 5. Filter and convert
netToAxt target.syntenic.net pre.chain reference.2bit query.2bit final.axt
axtToMaf final.axt reference.size query.size final.maf

Minigraph-Cactus for Pangenome Construction

Goal: Build a pangenome graph from related genomes (intra-species or sister-species).

Approach: Provide reference + alternative genomes -> minigraph for SV graph -> Cactus for base-level alignment.

# Prepare seqFile (similar to Cactus but designates reference)
cat > pangenome_seqs.txt << 'EOF'
reference   human.fa
hap1        haplotype1.fa
hap2        haplotype2.fa
hap3        haplotype3.fa
EOF

# Run minigraph-cactus
cactus-pangenome jobStore_path pangenome_seqs.txt \
    --outDir output_pangenome \
    --outName pangenome \
    --reference reference \
    --vcf \
    --gfa \
    --gbz \
    --indexCores 32 \
    --mapCores 32

# Outputs:
#   pangenome.gbz       Genome Browser Z compressed graph
#   pangenome.gfa.gz    Graph Fragment Assembly format
#   pangenome.vcf.gz    VCF for short variants from graph
#   pangenome.full.hal  Full Cactus HAL for downstream tools

For HPRC-scale (90 haplotypes), use --mapCores 64 --indexCores 64 on a high-memory node. See [[pangenome-analysis]] for downstream graph analysis.

Reconciliation: When Methods Disagree

Pattern Likely cause Action
Cactus alignment column count >> LASTZ pairwise Cactus includes ancestral alignment; pairwise is direct only Cactus is expected to have more columns when integrating across siblings
minimap2 aligns 60%; LASTZ aligns 80% Sensitivity differs at low identity Use LASTZ for distant comparisons
LASTZ chain/net A-on-B differs from B-on-A Reference bias Use Cactus reference-free for unbiased multi-genome questions
Cactus HAL vs Multiz MAF disagree at deep node Cactus reference-free; Multiz reference-anchored Cactus is the modern standard; Multiz mostly superseded
AnchorWave finds collinearity in WGD region, LASTZ doesn't AnchorWave WGD-aware; LASTZ unaware AnchorWave correct for WGD lineages
Minigraph-Cactus VCF and Cactus pairwise differ Pangenome graph integrates SV; pairwise reports relative to reference Minigraph-Cactus is for variant-aware pangenome work
nucmer (MUMmer) and minimap2 disagree at 80% identity nucmer more sensitive at higher identity; minimap2 faster Either is fine; cross-validate
Cactus runs but HAL file empty for some genomes Genome misnamed in seqFile or masking issue Verify seqFile genome names match FASTA headers; verify masking
progressiveMauve / SibeliaZ vs Cactus on bacteria Bacterial-specific aligners better-tuned for compact genomes progressiveMauve / SibeliaZ preferred for bacterial work; Cactus is overkill

Operational rule for publication: Multi-species WGA for comparative genomics uses Progressive Cactus with documented guide tree, masking, and Toil configuration. Pairwise reference alignments use LASTZ chain/net for UCSC tracks or minimap2 for fast pangenomics. Document choice of alignment tool, reference (if applicable), parameter values, and any non-default settings.

Cohort Gotchas

Anticipated Reviewer Pushback

Pushback Standard response
"Reference bias?" Cactus is reference-free; or for pairwise pipelines, both directions reported
"Guide tree?" Provided in seqFile; built from concatenated single-copy orthologs
"Masking?" RepeatModeler2 species-specific + RepeatMasker softmask before WGA
"Tool choice?" Cactus for multi-species (reference-free, scalable); LASTZ chain/net for single-pair (UCSC standard)
"Branch scale?" Set to 1.0 for vertebrates; 0.5 for plants; documented
"Assembly quality?" N50 reported; BUSCO completeness reported; chromosome-level preferred
"WGD?" AnchorWave proali with explicit ploidy; or Cactus with subgenome assignment
"Reproducibility?" Cactus version + Toil version + branch scale documented; jobStore path retained for restart
"HAL downstream?" TOGA for annotation projection; hal2maf for phyloP / PhastCons; halSynteny for SV

Common Errors

Error / symptom Cause Solution
Cactus fails with "guide tree mismatch" seqFile genome names don't match tree leaves Verify exact name match in seqFile lines vs newick
Cactus memory exhaustion Unmasked repeats Pre-mask with RepeatMasker softmask
Toil restart fails jobStore corruption Restart from scratch with new jobStore
LASTZ produces 0 alignments Sensitivity too strict Lower --hspthresh; check seed-and-extend params
MAF from hal2maf huge Default chunkSize too small Increase --chunkSize 10000000 for large genome
minimap2 SAM file empty Wrong preset for divergence Try -x asm20 or LASTZ for distant comparison
HAL file unreadable in another tool HAL version mismatch Pin HAL toolkit version
Cactus produces many tiny alignment blocks Branch scale too low Increase --branchScale to 1.0
nucmer maxmatch slow Many MUMs (unmasked repeats) Use --mum (unique) or mask repeats
AnchorWave reports few anchors CDS GFF parsing issue Verify GFF3 with gene and CDS features
TOGA fails with "no chain" Cactus HAL not converted to chain Use halSynteny + UCSC chain conversion
Cactus dead-locks on shared filesystem Toil multi-node sync issue Use AWS S3 / GCS jobStore for cloud

Tool Installation Notes

# Progressive Cactus (Docker / Singularity preferred for reproducibility)
docker pull quay.io/comparative-genomics-toolkit/cactus:latest
# Or via virtualenv
python3 -m venv cactus_env && source cactus_env/bin/activate && pip install cactus

# HAL toolkit (usually bundled with Cactus)
git clone https://github.com/ComparativeGenomicsToolkit/hal && cd hal && make

# LASTZ + UCSC kentUtils
conda install -c bioconda lastz ucsc-axt-chain ucsc-chain-merge-sort ucsc-chain-pre-net ucsc-chain-net ucsc-net-syntenic ucsc-net-to-axt

# MUMmer4
conda install -c bioconda mummer

# minimap2 + winnowmap2
conda install -c bioconda minimap2 winnowmap

# AnchorWave
conda install -c bioconda anchorwave

# progressiveMauve
conda install -c bioconda mauve

# SibeliaZ
conda install -c bioconda sibeliaz

# RepeatMasker / RepeatModeler2 (for masking)
conda install -c bioconda repeatmasker repeatmodeler

# Toil for HPC / cloud
pip install toil[all]

For Cactus on HPC, set up Toil with the appropriate batch system (--batchSystem slurm); for cloud, use Toil's S3/GCS support and a job store on shared cloud storage.

References