bio-comparative-genomics-genome-distance-and-species-delineation

What this skill does

## Version Compatibility

Reference examples tested with: skani 0.2.5+ (Shaw & Yu 2023 Nat Methods 20:1661; bluenote-1577/skani), FastANI 1.34+ (Jain 2018 Nat Commun 9:5114), pyani 0.3.0+ (Pritchard 2016 Anal Methods 8:12), pyskani 0.1+ (Larralde 2025), OrthoANI 1.40+ (Lee 2016 Int J Syst Evol Microbiol 66:1100), OrthoANIu 1.2+, GTDB-Tk 2.7.1+ (Chaumeil 2022 Bioinformatics 38:5315), GTDB release 220 (2024-Q3+), TYGS web (Meier-Kolthoff & Goker 2019 NAR 47:W42), GGDC v3.0 (web), Mash 2.3+ (Ondov 2016 Genome Biol 17:132), Dashing 2 (Baker & Langmead 2023 Genome Biol 24:36), CompareM 0.1.2+ for AAI (Parks/Cherubini), pyANI 0.3.1+, BLAT 36+, DIAMOND 2.1+. JSpeciesWS web (Richter & Rossello-Mora 2009 PNAS 106:19126).

Before using code patterns, verify installed versions match. If versions differ:
- CLI: `skani --version`; `fastANI --version`; `gtdbtk --version`; `mash --version`; `pyani --version`
- Python: `pip show gtdbtk pyani`

If code throws `GTDB-Tk database not found`, `skani sketch incompatible`, `Mash sketch version`, these tools have database-version coupling: GTDB-Tk requires the GTDB release matched to the binary version; Mash/skani sketches are forward-compatible but not always backward. Check `gtdbtk check_install` for database completeness.

# Genome Distance and Species Delineation

**"Are these genomes the same species, and what species are they?"** -> Prokaryote species delineation has shifted from 16S rRNA identity (now considered insufficient at < 98.7%) to **whole-genome ANI** at a 95% threshold (Jain 2018 Nat Commun 9:5114; corroborating Goris 2007 and Konstantinidis 2005). The modern operational standard for taxonomy is **GTDB-Tk** (Chaumeil 2020/2022 Bioinformatics 38:5315), which assigns genomes to the Genome Taxonomy Database (GTDB) using ANI radius + marker-gene placement. **skani** (Shaw & Yu 2023 Nat Methods 20:1661) has replaced FastANI as the default ANI tool in GTDB-Tk 2.4+ for being 20-30x faster while maintaining accuracy. The 95% ANI threshold is robust but not absolute -- the species circumscription radius varies by genus (Parks 2018 Nat Biotech 36:996).

- CLI: `skani dist genomes1.fa genomes2.fa -t 16` -- fast ANI computation
- CLI: `fastANI -q query.fa -r reference.fa -o output.txt` -- standard ANI
- CLI: `gtdbtk classify_wf --genome_dir genomes/ --out_dir gtdbtk_out --cpus 32` -- GTDB classification
- CLI: `mash dist *.fa` -- k-mer MinHash distance
- Web: TYGS (https://tygs.dsmz.de/) and GGDC (https://ggdc.dsmz.de/) for dDDH

## Algorithmic Taxonomy

| Tool | Approach | Output | Strength | Fails when |
|------|----------|--------|----------|------------|
| skani (Shaw & Yu 2023 Nat Methods 20:1661) | Sparse chaining on minimizers; ANI estimation | ANI percent + alignment fraction | 20-30x faster than FastANI; default in GTDB-Tk 2.4+; supports MAGs | Currently no AAI; not for cross-domain (archaea vs bacteria) |
| FastANI (Jain 2018 Nat Comm 9:5114) | Mashmap-based fragment alignment | ANI percent + orthologous fraction | Standard ANI tool 2018-2023; well-validated | Slower than skani; designed for >=80% identity |
| pyani / pyANI (Pritchard 2016 Anal Methods 8:12) | Multi-method ANI: ANIb (BLASTN), ANIm (MUMmer), TETRA | ANI matrix + visualization | Multiple algorithm consensus; reproducible | Slower than skani / FastANI; legacy for many studies |
| OrthoANI / OrthoANIu (Lee 2016 IJSEM 66:1100) | Reciprocal-best-orthologs ANI | ANI percent (more robust than blast-based) | Considered more precise than ANIb | Slower; less integrated |
| GTDB-Tk (Chaumeil 2020/2022 Bioinformatics 38:5315) | Marker-gene phylogeny + ANI radius (skani; was FastANI v2.3.x) | Taxonomic classification at all ranks (GTDB nomenclature) | Modern prokaryote taxonomy standard | Specific to GTDB; some classifications differ from NCBI |
| TYGS (Meier-Kolthoff & Goker 2019 NAR 47:W42) | dDDH (digital DNA-DNA hybridization) | Pairwise dDDH + species delineation | Most rigorous species delineation (vs traditional DDH) | Web-only; rate-limited; specific platform |
| GGDC (Auch 2010 SAB 2:117; v3 web) | Digital DDH calculation | dDDH percent + thresholds | Validated against laboratory DDH | Web-only; computational cost |
| Mash (Ondov 2016 Genome Biol 17:132) | MinHash k-mer sketches | Approximate distance (1 - similarity) | Extremely fast for large-scale clustering | k-mer-based; loses biological interpretation |
| Dashing 2 (Baker & Langmead 2023 GB 24:36) | Sketching with Bloom filter optimization | Same as Mash but faster | 5-10x faster than Mash | Newer; less broadly used |
| pyskani (Larralde 2025 bioRxiv) | Python wrapper around skani | ANI in Python | Programmatic access; CI/CD friendly | Newer; ecosystem still developing |
| CompareM | All-vs-all AAI (amino acid identity) | AAI percent | Cross-genus comparison via protein | Slow; needs all proteomes |
| JSpeciesWS (web; Richter & Rossello-Mora 2009) | ANIb / ANIm | Web ANI + species delineation | Standard for clinical microbiology | Web rate limits; slow |
| ANI Calculator (CGB Korea) | Web ANI | Web ANI | Quick check | Web-only |

Methodology evolves; verify GTDB release (currently r220 / 2024-Q3) and GTDB-Tk version compatibility. The 95% ANI species threshold has been confirmed across 90,000+ prokaryote genomes (Jain 2018; Parks 2018 demonstrating clear bimodality).

## Decision Tree by Experimental Scenario

| Scenario | Recommended approach | Why |
|----------|------------------------|-----|
| Classify a bacterial genome to species | GTDB-Tk classify_wf | Standard prokaryote taxonomy; ANI + marker-gene placement |
| Compute ANI between two genomes | skani | Fast (vs FastANI); accurate |
| Compute ANI for 1000+ genome pairs | skani all-vs-all | Scales; preferred for screening |
| Verify species delineation publication | TYGS + GGDC dDDH | Gold standard for novel species |
| MAG species assignment | GTDB-Tk + skani; CheckM2 first for completeness | MAGs need quality assessment + taxonomy |
| Bacterial strain typing | ANIb or dDDH; 99-99.99% for same strain | Strain resolution requires ANI > 99% |
| Sub-species / serotype level | ANI > 99.5% + epidemiological context | Sub-species requires biology + ANI |
| Across deep prokaryote divergence | AAI (CompareM); ANI saturates below 75% | AAI better for cross-genus |
| Cross-archaeal vs bacterial taxonomy | Skani-archaea-aware OR separate analysis | Default skani doesn't differentiate domains explicitly |
| Fast metagenomic taxonomy screen | Mash or Dashing 2 | k-mer-based; sketches reusable |
| Distance for genome clustering | skani matrix -> hierarchical clustering or NJ | Standard workflow |
| Reconcile 16S vs genome taxonomy | Run both; check for inter-genus conflicts | 16S < 98.7% typically inadequate |
| Build a reference database for ANI lookup | skani sketches indexed; query against | Pre-sketched reference for repeated queries |
| Phylogenetic placement | GTDB-Tk + IQ-TREE on extracted markers | Place new genome in known tree |
| AAI for genera-level comparison | CompareM or aai.rb (KBase) | AAI < ANI signal at deep divergence |
| Subspecies-level pathogenicity | ANI > 99% + virulence-gene annotation | ANI alone insufficient |
| Type-strain comparison | TYGS automatic type-strain matching | Built-in type-strain database |

## Per-Tool Failure Modes

### skani / FastANI ANI saturating below 75%

**Trigger:** Computing ANI between two genomes at < 75% nucleotide identity.

**Mechanism:** ANI is computed only on alignable regions; at < 75% identity, alignment fraction drops dramatically (< 50%); the few alignable regions are biased toward conserved regions, inflating apparent ANI.

**Symptom:** Reported ANI 75-80% with alignment fraction < 50%; meaningless biologically.

**Fix:** Below 75% ANI, switch to AAI (amino-acid identity from translated proteins); AAI is more meaningful at deep divergence (~50% AAI between distant genera). For ANI matrix at long range, use Mash distance (k-mer-based, no alignment).

### GTDB-Tk database version m