bio-comparative-genomics-positive-selection
Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing
npx skills add BioTender-max/awesome-bio-agent-skills --skill positive-selection --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.
## Version Compatibility Reference examples tested with: PAML 4.10.7+, HyPhy 2.5.62+ (BUSTED-MH from Lucaci 2023 bioRxiv; FUBAR-MH from same), datamonkey.org 2024+ for web jobs, IQ-TREE 2.3.6+, MACSE V2.07+, PRANK 170427+, MAFFT 7.526+, PREQUAL 1.02+, HmmCleaner 0.243+, GARD (HyPhy bundled), RDP5 5.59+, ete4 4.1.0+, BioPython 1.84+, scipy 1.13+, polyDFE 2.0+, DFE-alpha 2.16+, GRAPES 1.1.1+, RERconverge 0.3.0+, CSUBST 1.6.0+, PhyloAcc 2.4.0+. Quest-for-Selection benchmark refreshed annually. Before using code patterns, verify installed versions match. If versions differ: - CLI: `codeml` (PAML; check by `codeml /dev/null` -- prints version banner), `hyphy --version`, `gard --help` - Python: `pip show pyhyphy`; introspect ete4 API for tree-labeling - R: `packageVersion('RERconverge')`; `?correlateWithBinaryPhenotype` If code throws `branch-site test LRT non-positive`, `omega2 hit upper bound 999`, `MEME ML mixed gradient`, the most common cause is alignment error or saturated dS -- inspect alignment with TCS / Guidance2 and dS-vs-divergence-time. PAML 4.10 changed several control-file keywords from 4.9 (`getSE = 1` syntax tightened). # Positive Selection Analysis **"Is this gene / bra
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Method Failure Modes
- Recombination producing false positive selection
- Alignment errors producing false positives
- Saturated synonymous sites
- gBGC inflating apparent positive selection
- Branch-site test foreground specification
- LRT critical value confusion
- omega2 hitting upper bound (999)
- Multiple-testing burden in genome scans
- Convergent substitution misinterpreted as positive selection
- Quantitative Thresholds
Per-gene pipeline (parallelizable) for og in orthogroups/*.fa; do prank -d=$og -o=msa/$base.prank -codon -F PREQUAL -i msa/$base.prank.best.fas -o msa_filt/$base hyphy gard --alignment msa_filt/$base.filtered --output gard/$base.json hyphy busted --alignment msa_filt/$base.filtered \ hyphy meme --alignment msa_filt/$base.filtered \ hyphy absrel --alignment msa_filt/$base.filtered \ done python aggregate_selection_scan.py busted_mh/ meme/ absrel/ > selection_results.tsv
What does the bio-comparative-genomics-positive-selection skill do?
Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill positive-selection --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.