Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2021 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2021 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
tool_type
cli
primary_tool
TOGA
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.
Read-mapping-style ortholog detection + GFF transfer
Lifted GFF
Fast; no WGA required; standard for query-vs-reference pairs
Tandem duplicates ambiguous; not for gene loss detection
UCSC liftOver
Coordinate-based lift using chain files
Coordinate-lifted regions
Standard for coordinate transfers; not for gene annotations
Doesn't handle gene structure changes
Comparative Annotation Toolkit (CAT)
Luigi + Toil workflow integrating TransMap + AUGUSTUS (TM/TMR/CGP/PB) + homGeneMapping
Per-species comparative annotation
Integrates de novo + projection
Requires Cactus HAL input; Toil/Luigi setup complex
GeMoMa (Keilwagen 2019 Methods Mol Biol 1962:161)
Reference protein homology + evidence integration
Comparative gene annotation
Combines multiple reference species evidence
Slower; less popular than TOGA / LiftOff
AUGUSTUS (Stanke 2008)
De novo prediction; not strictly projection
Per-genome annotation
Augments projection with de novo
Standalone de novo; lower comparative accuracy
BRAKER3 (Gabriel 2024)
Augustus + GeneMark-ETP + RNA-Seq + protein
Comparative-aware de novo
Modern de novo with evidence
Not strictly projection
Funannotate (palmer lab)
Multi-evidence annotation including LiftOff
Funannotate annotations
Integrates evidence
Setup complex
Comparative Annotation Pipeline (CAP)
Earlier WGA-based annotation
Per-species per-gene
Historical; replaced by TOGA
Use TOGA
TransMap
UCSC genome browser annotation lifter
Per-locus lift
Tool for UCSC tracks
Tool-specific
Maker (Cantarel 2008)
Evidence-based de novo + projection
Per-genome annotation
Combines evidence
Maker is for novel genomes; LiftOff for transfer
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
Scenario
Recommended approach
Why
Annotate hundreds of mammal / bird genomes
TOGA with Cactus HAL
Scales to Zoonomia / Bird10000
Annotate single new genome from reference
LiftOff
Fast; no WGA required
Detect gene loss across mammals
TOGA intactness classification
Explicit I/PI/UL/L/M/PM codes
Project alternative isoforms
TOGA (preserves multiple transcripts)
Standard
Project annotations to assembly with high N50 + chromosome-level
TOGA
Requires good assembly
Project to fragmented draft assembly
LiftOff (more tolerant)
LiftOff works on draft assemblies
Multi-species annotation pipeline
CAT (Luigi + Toil)
Integrated workflow
Annotate plant genome from Arabidopsis
LiftOff with plant-specific options
Standard for plant work
Pseudogenization detection at scale
TOGA + intactness analysis
Designed for this
Reference-free gene prediction
BRAKER3 or AUGUSTUS
De novo; not projection
Comparative annotation of multiple references
GeMoMa
Multi-reference evidence integration
UCSC genome browser coordinate transfer
liftOver tool
Coordinate-specific
Annotation transfer to closely related strain (>95% ANI)
LiftOff
High accuracy at close divergence
Annotation transfer to deep divergence (mammal to fish)
TOGA + manual review
Requires WGA; expect lower coverage
Project annotations with WGD-aware handling
AnchorWave + TOGA-like or custom workflow
WGD-aware tools
Annotate non-coding RNAs
Specialized tools (Rfam, ncRNA-specific)
RNA detection different problem
Annotate immune / repetitive genes (MHC, OR)
PGR-TK MAP graph or manual
Repetitive regions; use [[pangenome-analysis]]
Annotate transposable elements
RepeatMasker / RepeatModeler
TE annotation different problem
Validate projected annotations
RNA-Seq alignment to projected
RNA-Seq evidence is gold standard
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.
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.
Quantitative Thresholds
Quantity
Threshold
Source / Rationale
TOGA intactness classes (loss_summ_data.tsv)
I (intact), PI (partial intact), UL (uncertain loss), L (lost), M (missing/assembly gap), PM (partial missing)
Kirilenko 2023 + TOGA repo
TOGA orthology relationships (orthology_classification.tsv)
# Prerequisites: Cactus HAL file from [[whole-genome-alignment]]# OR LASTZ chain/net pipeline output# 1. Extract syntenic blocks from HAL
halSynteny output.hal reference query --queryGenome query > query.synteny.psl
# 2. Convert PSL to UCSC chain format
axtChain -psl -linearGap=loose query.synteny.psl reference.2bit query.2bit chains/query.chain.gz
# Note: `-psl` is correct here only if the input is PSL. When the input comes from# `lastz --format=axt`, drop the `-psl` flag (or emit PSL from LASTZ first).# 3. Run TOGA. The canonical invocation is `python toga.py` from the TOGA checkout;# the Nextflow-style command shown below mirrors the same arguments but may not be# the standard entry point in your release -- verify against the hillerlab/TOGA README.
python toga.py \
chains/query.chain.gz \
reference_annotation.bed \
reference.2bit \
query.2bit \
--pn project_name \
--cpus 32
# Output:# project_name/loss_summ_data.tsv Per-gene intactness call# project_name/orthology_classification.tsv One-to-one / one-to-many / many-to-many# project_name/query_annotation.bed Lifted gene annotation# project_name/query_annotation.gff GFF format# project_name/cesar_alignment/ CESAR exon alignments
'''Parse TOGA loss summary to identify gene loss vs intact.'''import pandas as pd
defload_toga_loss(loss_summary_path):
'''loss_summary_data.tsv columns: TRANSCRIPT, STATUS, IS_INTACT, ...'''
df = pd.read_csv(loss_summary_path, sep='\t')
return df
defclassify_genes(df):
'''Standard TOGA classification: I/PI/UL/L/M/PM'''return df.groupby('STATUS')['TRANSCRIPT'].count()
deffilter_high_confidence_intact(df):
'''Filter to high-confidence intact (I) only. PI is partial-intact; L is Lost.'''return df[df['STATUS'] == 'I']
LiftOff for Pairwise Annotation Transfer
Goal: Transfer reference annotation to query genome via ortholog mapping.
For closely related species, default settings suffice. For divergent (75-90% identity), use -mismatch 5 -gap 10. For tandem-rich regions, -copies allows multiple projections.
Comparative Annotation Toolkit (CAT)
# Setup
git clone https://github.com/ComparativeGenomicsToolkit/Comparative-Annotation-Toolkit
cd Comparative-Annotation-Toolkit && pip install .
# Edit the CAT config file with reference + query genomes; input is a Cactus HAL alignment# CAT is orchestrated by Luigi (task graph) on top of Toil (execution); launch the RunCat module
luigi --module cat RunCat --hal=alignment.hal --ref-genome=mm10 --config=cat.config \
--work-dir work --out-dir out --workers=10 --local-scheduler \
--augustus --augustus-cgp --augustus-pb --assembly-hub > log.txt
CAT projects the reference annotation across the Cactus alignment with TransMap, then integrates AUGUSTUS (TM/TMR/CGP/PB) and homGeneMapping; output is multi-species comparative annotation.
Reconciliation: When Methods Disagree
Pattern
Likely cause
Action
TOGA "Lost" vs LiftOff "Mapped"
LiftOff more permissive; doesn't check frame preservation
Trust TOGA for loss claims; LiftOff for pairwise transfer
TOGA "Partial Intact" vs LiftOff "Mapped"
Frame disruption
Cross-validate; consider biology
Mouse-reference annotation vs human-reference
Reference choice bias
Multi-reference consensus
TOGA "Intact" but no RNA-Seq evidence
Possible pseudogene with intact ORF
Add expression evidence; reclassify
CESAR fails exon
Deep divergence or assembly gap
Manual review; consider GeMoMa alternative
GeMoMa vs LiftOff disagree
Evidence integration vs ortholog-based
GeMoMa for evidence-rich; LiftOff for fast pairwise
TOGA chain doesn't cover gene
Cactus alignment quality issue
Re-run Cactus with adjusted parameters; verify assembly
LiftOff produces duplicate transfers
Tandem cluster
Use -copies or restrict; manual review
CAT pipeline integrates de novo + projection
Combined evidence
Trust integrated; better than single tool
BUSCO of projected annotation low
Missing core genes
Likely tool failure; re-run with relaxed parameters
Operational rule for publication: TOGA + Cactus HAL for clade-level annotation (Zoonomia-style); LiftOff for pairwise transfer to closely related (<100 Myr); BRAKER3 / Funannotate for de novo where projection fails; manual review of TOGA "Lost" / "PI" calls.
