bio-comparative-genomics-comparative-annotation-projection
Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2020 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 hun
npx skills add BioTender-max/awesome-bio-agent-skills --skill comparative-annotation-projection --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Projects gene annotations from a well-annotated reference to query genomes, using TOGA (WGA-anchored projection with intactness classification), CESAR 2.0 for codon-aware exon projection, LiftOff for ortholog-based annotation transfer, Liftover, GeMoMa, and CAT to enable cross-genome annotation transfer, integrity classification, and comparison across many genomes (hundreds of mammals or birds). It supports detecting gene loss vs intactness, projecting alternative isoforms, and choosing between WGA-anchored vs ortholog-based strategies.
How it works
- Provides CLI commands for each projection pathway: TOGA (chain-based projection), CESAR (exon alignment), Liftoff (ortholog-based GFF transfer), GeMoMa (evidence-based projection), CAT (multi-species projection).
- Verifies compatibility and version requirements for TOGA, CESAR 2.0, Liftoff, CAT, GeMoMa, UCSC liftOver, HAL tools, NextFlow, Snakemake, and QC tools; shows how to validate versions via commands like
toga.py --help,cesar --help,liftoff --version. - Describes the input formats each tool expects (HAL/chain files for TOGA, exon-level GFF for CESAR, reference GFF and aligned FASTA for LiftOff) and provides guidance for pre-processing when inputs are misaligned.
- Explains the decision framework between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer, including how intactness vs loss is classified and how multiple references can be used for consensus.
- Lists a decision tree of scenarios and the recommended approach per use-case (e.g., scaling to hundreds of genomes with TOGA HAL, fast projection with LiftOff for a single genome, detecting pseudogenization, multi-reference projection with CAT).
- Enumerates common failure modes (TOGA chain files, CESAR exon-fragment misalignment, LiftOff tandem duplicates, false negatives in intactness) and suggested fixes.
- Provides quantitative thresholds for classification and projection quality (e.g., intactness codes, orthology classes, divergence limits, coverage/identity thresholds, BUSCO, N50, and transfer success rates).
When to use it
Use when transferring annotations from a well-annotated reference to query genomes, especially when you need to classify gene-loss vs gene-intact across many genomes, or when building cross-species comparative annotations (e.g., mammal/bird datasets). Use TOGA for WGA-anchored projections and explicit intactness classification; use LiftOff for fast ortholog-based transfers when WGA is not required or for closely related references. Switch to GeMoMa or CAT for multi-reference or evidence-integrated projections as appropriate.
What it can touch
- TOGA: chain files, HAL/ LASTZ inputs, and related chain-derived inputs; requires specific input formats (HAL-derived chains, nets, axtChain outputs).
- CESAR 2.0: exon-level GFF inputs for codon-aware exon projection.
- LiftOff: reference GFF, query FASTA, and reference FASTA.
- CAT: multi-species workflow integration.
- GeMoMa: reference protein homology data; evidence integration via Java tool.
- UCSC liftOver: coordinate-lift chain files.
Caveats
- Requires compatibility among multiple tools; TOGA, CESAR 2.0, LiftOff versions must align with tested references (TOGA 1.1.7+; CESAR 2.0; LiftOff 1.6.3+; CAT 2.4+).
- TOGA chain file availability and proper HAL/LASTZ-derived inputs are critical; misformatted or missing chain files cause failures.
- Divergence thresholds affect projection success (TOGA ~300 Myr; CESAR ~150 Myr; LiftOff ~80% identity).
- Pseudogenization detection is indirect through intactness; RNA-Seq evidence is recommended for corroboration.
## Version Compatibility Reference examples tested with: TOGA 1.1.7+ (hillerlab/TOGA; Kirilenko 2023 Science 380:eabn3107), CESAR 2.0 (Sharma & Hiller 2017 NAR 45:8369), 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, Snakemake 8.0+ 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
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- TOGA chain file missing or incompatible
- CESAR exon-fragment misalignment in highly divergent species
- LiftOff tandem duplicate ambiguity
- TOGA intactness classification false negatives
- Reference choice bias
- Pseudogenization vs gene loss distinction
- Splice variant inconsistency across projections
- Annotation pipeline reference quality affecting projection
- Polyploid query genome handling
- Chromosome-level vs scaffold-level reference
OR LASTZ chain/net pipeline output halSynteny output.hal reference query --queryGenome query > query.synteny.psl axtChain -psl -linearGap=loose query.synteny.psl reference.2bit query.2bit chains/query.chain.gz 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 \
What does the bio-comparative-genomics-comparative-annotation-projection skill do?
Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2020 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 hun
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill comparative-annotation-projection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.