Bio Comparative Genomics Comparative Annotation Projection

Version Compatibility

Reference examples tested with: TOGA 1.1.7+ (hillerlab/TOGA; Kirilenko 2023 Science 380:eabn3107), CESAR 2.0 (Sharma, Schwede & Hiller 2017 Bioinformatics 33:3985), LiftOff 1.6.3+ (Shumate & Salzberg 2021 Bioinformatics 37(12):1639-1643), Comparative Annotation Toolkit (CAT) 2.4+, GeMoMa 1.9+ (Keilwagen 2019 Methods Mol Biol 1962:161), UCSC liftOver 2024+, Cactus 2.9.1+ (for HAL input), HAL toolkit 2.3+, NextFlow 24+ for TOGA pipeline, BUSCO 5.7+ / Compleasm 0.2.7+ for QC, Luigi + Toil for CAT, R 4.4+. The current TOGA expects HAL from Cactus 2.5+; older HAL formats may fail.

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

• CLI: toga.py --help; cesar --help; liftoff --version
• Python: pip show liftoff
• Java: gemoma --help (Java 11+)

If code throws TOGA chain file missing, CESAR fragment not found, LiftOff annotation not parsed, the toolchain expects specific input formats: TOGA needs HAL or chain/net files from Cactus / LASTZ; CESAR needs exon-level GFF; LiftOff needs reference GFF and aligned FASTA. Pre-process with the appropriate format conversion.

Comparative Annotation Projection

"Annotate this new genome using my well-annotated reference" -> Annotation projection from a reference is the modern alternative to de novo gene prediction; it produces high-quality, comparable annotations across genomes by leveraging evolutionary conservation. The 2023-era standard is TOGA + CESAR 2.0 (Kirilenko 2023 Science 380:eabn3107), which uses whole-genome alignment chains + ML classification + codon-aware exon projection to scale to hundreds of genomes (Zoonomia: 488 mammals; Bird10000 Genomes: 501 birds). For ortholog-based projection (no WGA needed), LiftOff (Shumate & Salzberg 2021 Bioinformatics 37(12):1639) is the standard. The critical decision is WGA-anchored (TOGA) vs ortholog-anchored (LiftOff): TOGA explicitly classifies gene intactness vs loss using the alignment chains, LiftOff relies on reciprocal-best-hit equivalents.

• CLI: toga.py --chain chain.bb --bed ref.bed --tDB target.2bit --qDB query.2bit --pn project_name -- WGA-based projection
• CLI: cesar -i exons.fa -d 4 -o output.aln -- codon-aware exon alignment (used internally by TOGA)
• CLI: liftoff -g ref.gff query.fa ref.fa -o query.gff -- ortholog-based projection
• CLI: gemoma -- evidence-based comparative annotation (Java)
• CLI: cat (Comparative Annotation Toolkit) -- multi-species annotation projection

Algorithmic Taxonomy

Methodology evolves; the Kirilenko 2023 TOGA paradigm (WGA-anchored + intactness classification) is the gold standard for vertebrate-scale comparative annotation. For pairwise transfers, LiftOff is the modern standard. Verify the current TOGA documentation (hillerlab/TOGA) before locking on a single approach.

Decision Tree by Experimental Scenario

Per-Tool Failure Modes

TOGA chain file missing or incompatible

Trigger: Running TOGA on Cactus HAL without proper chain file extraction.

Mechanism: TOGA requires UCSC-style chain files derived from Cactus HAL or LASTZ chains/nets pipeline. Cactus HAL doesn't directly produce chain files; conversion via halSynteny + chainNet + axtChain is required.

Symptom: TOGA fails with "chain file not found" or "no syntenic blocks for query."

Fix: Use halSynteny (HAL toolkit) to extract syntenic blocks; convert to chain format via axtChain and chainNet. The TOGA Nextflow wrapper handles this automatically; for manual runs, see UCSC kentUtils chain documentation.

CESAR exon-fragment misalignment in highly divergent species

Trigger: Projecting from mouse to fish (~400 Myr divergence); many exons fail CESAR projection.

Mechanism: CESAR's HMM model is calibrated for vertebrate divergence (< 100 Myr typical). At deep divergence, exon boundaries shift; CESAR may misalign or fail to project.

Symptom: Many genes in mouse have TOGA "M" (missing) or "PI" (partial-intact) classification in fish; coverage of expected genes is low.

Fix: TOGA documentation recommends < 300 Myr divergence for reliable projection. For deeper divergence, manual review of failed exons; consider GeMoMa with multiple reference species. Some genes won't project because they're truly absent (orphan genes); others fail due to alignment limitations.

LiftOff tandem duplicate ambiguity

Trigger: LiftOff on genomes with extensive tandem duplications (e.g., NLR clusters in plants, olfactory receptors in mammals).

Mechanism: LiftOff uses ortholog detection similar to OrthoFinder; tandem duplicates create many similar sequences, making reciprocal-best-hit identification ambiguous.

Symptom: LiftOff reports many "multimapped" genes; tandem clusters have one-to-many or many-to-one orthology calls.

Fix: Pre-collapse tandem duplicates manually; or use LiftOff with -mismatch 5 and -flank 0.5 for more relaxed mapping; or use TOGA which has tandem-aware classification.

TOGA intactness classification false negatives

Trigger: TOGA classifies a gene as "Lost" when it is actually intact.

Mechanism: TOGA uses ML classifier on chain features + frame preservation; assembly gaps, short alignment fractions, or CESAR projection failures can cause false-loss calls.

Symptom: TOGA "Lost" gene actually present in independent validation (RNA-Seq, manual inspection); known biology contradicts loss.

Fix: Manual review of TOGA "Lost" calls in the loss_summ_data.tsv; cross-validate with RNA-Seq mapping; use ID + biology to verify. TOGA's classifier is calibrated for mammals/birds; non-canonical genome architectures may produce false losses.

Reference choice bias

Trigger: Projecting from one reference (e.g., mouse) produces different annotation than from another (e.g., human).

Mechanism: Each reference's annotation has its own biases (gene structures, splice variants, missing genes). Projection inherits these biases; different references produce somewhat different annotations.

Symptom: Mouse-reference TOGA annotation has gene X missing in query; human-reference TOGA has it present; or different exon structures.

Fix: Project from multiple references; consensus annotation. CAT integrates multi-reference projection; manual review of inconsistencies. Document reference choice impact.

Pseudogenization vs gene loss distinction

Trigger: Reporting a "lost" gene that retains coding sequence (frame may be conserved but expression lost).

Mechanism: TOGA detects loss of coding capacity (intactness), but doesn't directly identify pseudogenization (loss of expression). A pseudogene with intact reading frame may be classified "Intact" by TOGA.

Symptom: TOGA "Intact" annotation but the gene is pseudogene per RNA-Seq + Ribo-Seq evidence.

Fix: Combine TOGA intactness with expression data (RNA-Seq from species of interest); apply PseudoPipe (Zhang 2006 Bioinformatics 22:1437) or RetroFinder (Baertsch 2008 BMC Genomics 9:466) for systematic pseudogene detection.

Splice variant inconsistency across projections

Trigger: Projecting genes with extensive alternative splicing.

Mechanism: Reference may have alternative splice variants; projection of alternative isoforms requires per-isoform alignment which may fail for some variants.

Symptom: Query species has fewer projected isoforms than reference; canonical isoform present but alternatives missing.

Fix: TOGA projects each transcript independently; manual review of dropped isoforms. RNA-Seq from species of interest for novel splice variants.

Annotation pipeline reference quality affecting projection

Trigger: Projecting from an outdated or buggy reference annotation.

Mechanism: Errors in reference annotation propagate to all projections. A wrong exon boundary in mouse is propagated to all mammals.

Symptom: Same exon boundary error appears across many projected annotations.

Fix: Verify reference annotation quality via BUSCO + manual gene model review; use updated Ensembl / NCBI releases (current 2024-Q4).

Polyploid query genome handling

Trigger: Projecting annotations onto a polyploid query without subgenome consideration.

Mechanism: Projection sees multiple homeologous regions; ortholog detection ambiguous between subgenomes.

Symptom: Polyploid query annotation has ~2x the genes expected; many "redundant" projections from homeologs.

Fix: Assign subgenomes before projection (see [[whole-genome-duplication]]); project to each subgenome separately. AnchorWave proali handles ploidy; LiftOff doesn't natively.

Chromosome-level vs scaffold-level reference

Trigger: Projecting from chromosome-level reference to scaffold-level query.

Mechanism: Scaffold-level query has gaps and ambiguous gene assignments; projection mostly succeeds but some genes split across scaffolds.

Symptom: Projected GFF has fragmented gene models; some genes have 2-3 entries across scaffolds.

Fix: Pre-scaffold query (Hi-C scaffolding if possible); or accept partial annotations; document fragmentation rate. TOGA reports "Partial Intact" for these cases.