bio-comparative-genomics-hgt-detection
Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT
npx skills add BioTender-max/awesome-bio-agent-skills --skill hgt-detection --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
Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT from contamination, ruling out gBGC as a false signal, or quantifying DTL rates with ALE/GeneRax on bacterial trees.
How it works
The skill presents a suite of tools and references across three signal classes:
- Composition: SIGI-HMM, AlienHunter, IslandPath-DIMOB, IslandViewer 4 web; these assess codon usage, dinucleotide biases, and mobility genes to flag recent transfers.
- BLAST-distribution: HGTector v2, DarkHorse, Alien Index; compare hits across taxonomy to flag atypical distributions without requiring gene trees.
- Phylogenetic incongruence: AvP, HGTphyloDetect for eukaryotes; ALE, GeneRax, AleRax for prokaryote reconciliation; RANGER-DTL 2.0 for parsimony-based reconciliation. The workflow emphasizes concordance across at least two signal classes and explicit exclusion of contamination and differential gene loss. It includes guidance to verify versions, run orders, and compatibility notes for various tools, and it lists multiple scenario-based recommendations (e.g., using IslandViewer 4 web for single-genome screening, or ALE/AleRax for larger datasets). It also provides a detailed per-tool failure modes section focusing on contamination and other artifacts, and notes to perform contamination filtering before eukaryotic HGT analyses.
When to use it
Use when screening prokaryote genomes for HGT events and genomic islands, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages, separating eukaryotic HGT from contamination, and quantifying DTL rates on bacterial trees. It also recommends when to prefer particular tools based on dataset size and research question (e.g., single genome vs large-scale phylogenomic analysis).
What it can touch
The skill references tools and workflows:
- HGTector v2, AvP, HGTphyloDetect, ALE, GeneRax, AleRax, RANGER-DTL, IslandViewer 4, metachip, ASTRAL-Pro2, DarkHorse, Alien Index, SIGI-HMM, AlienHunter, IslandPath-DIMOB, BLAST databases, and various tree-based workflows. These touch genome sequences, gene trees, species trees, and BLAST results as part of the analysis pipeline.
Caveats
Mentions compatibility considerations (version checks and taxonomy database updates), potential false positives due to contamination (especially in eukaryotic HGT analyses), amelioration eroding composition signals over time, and scenarios where BLAST-based methods may fail for close-relative donors. Also notes that several tools are computationally intensive (e.g., certain reconciliation methods) and require appropriate data quality (e.g., high-quality MAGs for MetaCHIP). The license is NOASSERTION. The skill relies on a mix of web, CLI, and Python-based tools and stresses cross-validation across methods to support HGT inferences.
## Version Compatibility Reference examples tested with: HGTector 2.0b3+, AvP 1.0.4+, HGTphyloDetect 1.0+, ALE 1.0+ (ssolo/ALE github), GeneRax 2.1.3+, AleRax 1.2.0+ (Morel 2024), RANGER-DTL 2.0+, IslandViewer 4 (web), mobileOG-db 1.0+, MetaCHIP 1.10+, IQ-TREE 2.3.6+, BioPython 1.84+, DIAMOND 2.1.10+. Open Tree of Life and NCBI Taxonomy reference databases updated 2024-Q3 minimum for HGTector/AvP. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show hgtector` then `hgtector search --help` - CLI: `ALEml_undated --help`, `generax --help`, `alerax --help` - DB: `hgtector database --check` for taxonomy version If code throws `Taxonomy ID not found`, `database version mismatch`, or `KeyError` on NCBI taxids, refresh the local taxonomy dump (NCBI updates monthly). ALE/GeneRax expect newick gene trees with bootstraps; AleRax expects gene-tree distributions (uniform bootstrap samples or UFBoot trees). # Horizontal Gene Transfer Detection **"Are these genes horizontally acquired, and from where?"** -> HGT signal lives in three orthogonal signal classes: composition (recent transfers carry donor codon usage; erodes by Lawrence-Ochman 1998 ameli
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Contamination masquerading as eukaryotic HGT (THE critical failure)
- Amelioration eroding compositional signal
- Differential gene loss mimicking HGT
- Hybridization / reticulate evolution
- gBGC-driven AT->GC substitution bias
- Close-relative donor making BLAST methods fail
- Poor sampling of donor lineage
- Tetranucleotide z-score thresholding in heterogeneous genomes
- Quantitative Thresholds
- HGTector v2 Workflow
for og in orthogroups/*.fa; do
iqtree2 -s $og -m TEST -B 1000 -nt 2 --prefix ${og%.fa}_ufb
done
for tree in orthogroups/*_ufb.ufboot; do
ALEobserve $tree
ALEml_undated species_tree.nwk ${tree}.ale separators="|" sample=100
python aggregate_ale.py orthogroups/*_uTs > dtl_branchwise.tsv
avp prepare -t taxonomy.tsv -d nr.fasta -o database
avp detect -i query_proteome.fasta -d database -o avp_results -threads 8
avp classify -i avp_results -d database --au-test --bootstrap 1000What does the bio-comparative-genomics-hgt-detection skill do?
Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill hgt-detection --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.