Bio protein clustering pangenome

Bio Protein Clustering Pangenome

Cluster proteins into orthogroups and derive pangenome matrices.

Instructions

1. Cluster proteins. Choose the tool by dataset size and goal:
   • Default for orthology inference up to a few hundred genomes: OrthoFinder v3 (improved accuracy and lower RAM at scale, supports MSA-based gene trees; supersedes OrthoFinder v2 and OrthoMCL workflows).
   • Very large pangenomes where OrthoFinder is too RAM-heavy: ProteinOrtho v6.1.7+ as the fast, scalable alternative.
   • Sequence clustering (not strict orthology) and similarity search backbones: MMseqs2 v15-6f452+. Enable GPU mode (mmseqs ... --gpu) on CUDA Turing+ nodes for a ~20× speedup at near-identical sensitivity.
2. Build presence/absence matrix AND an integer copy-number matrix (orthogroup × genome) covering the query AND the close relatives produced by /bio-phylogenomics.
3. Compute core/accessory/cloud/singleton partitions.
4. Identify single-copy orthologs for phylogenetic analysis.
5. Discriminate paralogs from orthologs in multi-copy gene families.
6. Calculate pangenome statistics (completeness, orthogroup occupancy).
7. When a query genome or genome set is under study, use the literature-derived analysis playbook to choose an appropriate comparison baseline: closest relatives, a broader clade, environmental references, or a negative/control set.
8. Genome-property frontier table — produce relative_genome_metrics.tsv with one row per (query + relative) and columns for genome size, contig count, N50, gene count, coding density, GC, tRNA count, rRNA count, and any group-relevant property. Add a column that places the query in the relative distribution (percentile, min/median/max, "record-class" tag) and a column citing the literature reference defining the group's known range.
9. Synteny / conserved neighborhoods — for each pair (query, relative) compute conserved gene neighborhoods (e.g., ≥2 collinear orthologs). Tool selection:
   • Pairwise / classical: MCScanX (Nature Protocols 2024 updated protocol).
   • Multi-genome at scale (>2 assemblies, up to >3 Gbp, >15% divergence): ntSynt (BMC Biology 2025, DOI: 10.1186/s12915-025-02455-w) — alignment-free minimizer-graph approach; does not detect duplications.
   • Strain-level work where duplication detection matters: SibeliaZ. Save results as conserved_neighborhoods.tsv with columns: query_block_id, relative, relative_block_id, members (ortholog IDs), intergenic_spacing_query, intergenic_spacing_relative, spacing_ratio, notes. Flag conserved gene pairs and unusual spacing/expansions.
10. Identify discovery-relevant differences defined by the playbook, including query-specific families, missing expected families, expansions/contractions, unusual sharing patterns, and high-value unknowns. Persist as family_copy_number_comparison.tsv (query vs relative-median fold change per family) — coordinated with bio-annotation's family matrix.
11. Annotate candidate orthogroups with /bio-annotation; for high-value unknowns, route representatives to /bio-structure-annotation when structure-based inference is appropriate.
12. Produce a comparison summary that separates conserved lineage features from unusual or query-specific features and states the baseline used. The summary must report ALL of: genome-property frontier, marker-category presence/copy, family expansions/contractions, synteny conservation/breakage, and ncRNA counts side-by-side with relatives.

Quick Reference

Task	Action
Run workflow	Follow the steps in this skill and capture outputs.
Validate inputs	Confirm required inputs and reference data exist.
Review outputs	Inspect reports and QC gates before proceeding.
Tool docs	See docs/README.md.
References	See references.md.

Input Requirements

Prerequisites:

• Tools available in the active environment (Pixi/conda/system). See docs/README.md for expected tools.
• Protein FASTA inputs are available. Inputs:
• proteins.faa (FASTA protein sequences)

Output

• results/bio-protein-clustering-pangenome/orthogroups.tsv
• results/bio-protein-clustering-pangenome/presence_absence.parquet
• results/bio-protein-clustering-pangenome/copy_number_matrix.parquet
• results/bio-protein-clustering-pangenome/relative_genome_metrics.tsv
• results/bio-protein-clustering-pangenome/family_copy_number_comparison.tsv
• results/bio-protein-clustering-pangenome/conserved_neighborhoods.tsv
• results/bio-protein-clustering-pangenome/closest_relative_comparison.tsv
• results/bio-protein-clustering-pangenome/query_specific_candidates.tsv
• results/bio-protein-clustering-pangenome/pangenome_report.md
• results/bio-protein-clustering-pangenome/logs/

Quality Gates

• Cluster size distributions meet project thresholds.
• Matrix completeness meets project thresholds.
• On failure: retry with alternative parameters; if still failing, record in report and exit non-zero.
• Verify proteins.faa is non-empty and amino acid encoded.
• Comparison baseline is justified from literature, phylogeny, taxonomy, or data availability.
• Query-specific, missing, expanded, and conserved orthogroups are reported separately.
• Candidate discovery orthogroups have annotation evidence or a recommended follow-up analysis.
• relative_genome_metrics.tsv places each query in the distribution of relatives and notes the literature-defined extreme of the inferred group.
• family_copy_number_comparison.tsv reports per-family fold change vs the relative median for the full annotated family set, not only top candidates.
• conserved_neighborhoods.tsv is produced and includes intergenic spacing for both query and relative sides; broken synteny, unusual spacing, and expansions are flagged.

Examples

Example 1: Expected input layout

proteins.faa (FASTA protein sequences)

Troubleshooting

Issue: Missing inputs or reference databases Solution: Verify paths and permissions before running the workflow.

Issue: Low-quality results or failed QC gates Solution: Review reports, adjust parameters, and re-run the affected step.