Genome distance and species delineation

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 mismatch

Trigger: Using GTDB-Tk binary with mismatched GTDB reference data version.

Mechanism: GTDB releases (r207, r214, r220, ...) include reference trees, marker-gene HMMs, and ANI sketch files; GTDB-Tk versions are aligned to specific releases. Mismatch causes silent or loud failures.

Symptom: GTDB-Tk fails with "marker gene HMM not found" or runs but produces inconsistent classifications.

Fix: Check release compatibility: gtdbtk check_install shows the expected vs found versions. Pin via conda env. Download the matching release from data.gtdb.ecogenomic.org.

Below-95% ANI but same species (genus-specific radius)

Trigger: Strict 95% ANI threshold; rejecting closely related genomes as different species.

Mechanism: Parks 2018 demonstrated species-circumscription radius varies (typically 95-99% but tighter for some clonal lineages). For some genera (e.g., Pseudomonas), the species threshold is 94% per genus-specific analysis.

Symptom: Two clearly biologically-related strains (epidemiologically connected outbreak) have ANI 94%; using strict 95% calls them different species.

Fix: Use GTDB-Tk's species radius approach which uses genus-specific cutoffs. For novel-species naming, consider 95% as primary + ecology + biology. Report ANI alongside additional context (gene content, phenotype, ecology).

High alignment fraction required (AF >= 0.5)

Trigger: ANI reported without alignment fraction; or AF < 0.5.

Mechanism: A 95% ANI with 20% AF (only 20% of genome alignable) is biologically meaningless; the comparison covers a small fraction of the genomes. Standard species delineation requires AF >= 0.5 + ANI >= 95%.

Symptom: ANI calls "same species" but only 20-30% of genome aligned.

Fix: Require AF >= 0.5 + ANI >= 95% for species call. Below this, classification is ambiguous; consider AAI or HGT (e.g. a phage-rich genome will have low AF to its actual species).

MAG contamination / incompleteness

Trigger: Running GTDB-Tk on MAGs without CheckM2 pre-screening.

Mechanism: Low-completeness MAGs (< 50%) may have marker-gene gaps that break GTDB-Tk's placement; high-contamination MAGs (> 5%) place anomalously.

Symptom: GTDB-Tk reports "no genome retained" or makes implausible classifications.

Fix: Pre-filter MAGs with CheckM2 (Chklovski 2023 Nat Methods 20:1203): require >= 70% completeness, < 5% contamination, < 10% strain heterogeneity for species-level. For lower-quality MAGs, report genus-level only or exclude.

Mash distance vs ANI inconsistency

Trigger: Using Mash distance for species delineation directly.

Mechanism: Mash distance = 1 - (Mash similarity) is correlated with 1 - ANI but is not the same metric. The 0.05 Mash distance threshold (sometimes cited as "5% Mash = 95% ANI") is approximate; exact ANI varies +-0.5%.

Symptom: Genomes with Mash distance 0.04-0.06 inconsistently called "same species" or not.

Fix: Use Mash for fast screening + clustering, but verify species delineation with ANI on candidate pairs. Mash 0.05 ~ ANI 95% is a rough heuristic; not a publishable threshold.

Ortholog-based ANI conservative vs alignment-based

Trigger: Comparing OrthoANI / OrthoANIu to FastANI / skani.

Mechanism: OrthoANI uses reciprocal-best-orthologs; FastANI uses fragment alignment. They differ by 0.5-2% systematically; OrthoANI is more conservative.

Symptom: OrthoANI = 94.5%, FastANI = 96% on same pair; different "same species" calls.

Fix: Document tool used; for taxonomy, use the tool aligned to the reference database (GTDB-Tk uses skani; NCBI uses FastANI). Cross-validation for unclear cases.

Tetranucleotide bias inflating Mash for low-GC genomes

Trigger: Comparing genomes with extreme GC content (Streptomyces ~70% vs Mycoplasma ~25%) via Mash.

Mechanism: k-mer frequency distributions are GC-dependent; Mash distance between extreme-GC genomes is inflated by background composition rather than biology.

Symptom: Two unrelated extreme-GC genomes (e.g. Mycoplasma + Mycoplasma) appear closer than they biologically are.

Fix: Use ANI / AAI for cross-GC comparisons; Mash is reliable only within a GC-comparable range. Document GC range when reporting Mash distances.

Type strain conflicts in TYGS

Trigger: Submitting genome to TYGS where type strain is missing or misclassified.

Mechanism: TYGS depends on type strain database; if the type strain is genome-sequenced incompletely or misclassified, the placement may be incorrect.

Symptom: TYGS reports unexpected nearest type strain.

Fix: Cross-validate with GTDB-Tk; check type-strain genome quality. For novel-species naming, TYGS report should be supplemented with manual taxonomic check.

Quantitative Thresholds

Quantity	Threshold	Source / Rationale
Species delineation ANI	>= 95% (Jain 2018)	Standard; based on 90,000 prokaryote genomes
Genus-specific species ANI radius	94-99% (varies by clade)	Parks 2018 Nat Biotech 36:996
Alignment fraction (AF) for ANI	>= 0.5 (Jain 2018)	Below this, comparison too small
Strain delineation ANI	>= 99% (typical); >= 99.5% strict	Operational
Sub-species delineation	>= 99% ANI + epidemiology	Manual
AAI species delineation	>= 70%	Konstantinidis 2005; varies clade
AAI genus delineation	>= 60%	Standard
dDDH species delineation	>= 70%	Goris 2007; matches ANI 95%
dDDH genus delineation	>= 50%	Auch 2010
16S rRNA species threshold (deprecated)	>= 98.7%	Stackebrandt 2006; superseded by ANI
Mash distance ~ ANI heuristic	0.05 ~ 95% ANI	Ondov 2016; rough
GTDB-Tk completeness for placement	>= 50% (CheckM2 OBLIGATE >= 70%)	Chaumeil 2022
GTDB-Tk contamination	< 10% (< 5% for species-level)	Chaumeil 2022
MAG quality for taxonomy	CheckM2 >= 70% comp, < 5% cont, < 10% strain het	Chklovski 2023
skani CLI threads	up to 64; scales linearly	skani docs
skani sketch size	default 1000 minimizers; tunable	skani docs
FastANI default fragment	3000 bp; varies	Jain 2018 default
GTDB-Tk classify_wf time per genome	2-30 min on 16 CPUs	Empirical
Type strain ANI uncertainty	+- 1%	Operational

skani Standard Workflow

Goal: Compute ANI between query and reference set; classify species.

Approach: Build skani sketch -> compute distances -> apply species delineation logic.

# 1. Pre-sketch reference set
skani sketch reference_genomes/*.fa -o reference_sketches

# 2. Compute ANI for a single query
skani dist query.fa reference_sketches/* -t 16 \
    --robust --slow > query_distances.tsv

# 3. Compute all-vs-all matrix (large set)
skani triangle genomes/*.fa -t 32 --robust --sparse -o ani_matrix.tsv
# --sparse emits tabular Ref_file Query_file ANI Align_fraction_ref Align_fraction_query;
# without --sparse, `skani triangle` emits a Phylip-style square matrix.

# 4. Filter and visualize
awk '$3 >= 95 && $5 >= 50' query_distances.tsv > species_matches.tsv

'''Apply 95% ANI species delineation with AF >= 0.5 constraint.'''
import pandas as pd
import numpy as np


def parse_skani(path):
    '''skani output: Ref_file Query_file ANI Align_fraction_ref Align_fraction_query'''
    df = pd.read_csv(path, sep='\t')
    df.columns = ['ref_file', 'query_file', 'ani', 'af_ref', 'af_query']
    return df


def species_delineate(df, ani_threshold=95.0, af_threshold=0.5):
    '''Return genome pairs called same species.'''
    df['min_af'] = df[['af_ref', 'af_query']].min(axis=1)
    same_species = df[(df['ani'] >= ani_threshold) & (df['min_af'] >= af_threshold)]
    return same_species

GTDB-Tk Classification Workflow

Goal: Assign GTDB taxonomy (kingdom -> species) to a set of bacterial/archaeal genomes.

Approach: GTDB-Tk classify_wf identifies markers, builds tree placement, calculates ANI radius, returns full taxonomy.

# Set environment
export GTDBTK_DATA_PATH=/path/to/release220_data

# Verify install
gtdbtk check_install

# Run classify_wf
gtdbtk classify_wf \
    --genome_dir genomes/ \
    --out_dir gtdbtk_out \
    --cpus 32 \
    --extension fa \
    --skip_ani_screen   # Skip if want phylogeny-based only

# Output:
#   gtdbtk_out/classify/gtdbtk.bac120.summary.tsv   bacterial classifications
#   gtdbtk_out/classify/gtdbtk.ar53.summary.tsv     archaeal classifications
#   gtdbtk_out/identify/                              marker gene tables
#   gtdbtk_out/align/                                 multiple sequence alignments

'''Parse GTDB-Tk summary for ranked classification.'''
import pandas as pd


def parse_gtdbtk_summary(path):
    '''GTDB-Tk summary columns include: user_genome, classification, classification_method, ani, msa_percent'''
    df = pd.read_csv(path, sep='\t')
    df['classification_split'] = df['classification'].str.split(';')
    df['kingdom'] = df['classification_split'].str[0].str.replace('d__', '')
    df['phylum'] = df['classification_split'].str[1].str.replace('p__', '')
    df['class'] = df['classification_split'].str[2].str.replace('c__', '')
    df['order'] = df['classification_split'].str[3].str.replace('o__', '')
    df['family'] = df['classification_split'].str[4].str.replace('f__', '')
    df['genus'] = df['classification_split'].str[5].str.replace('g__', '')
    df['species'] = df['classification_split'].str[6].str.replace('s__', '')
    return df


df = parse_gtdbtk_summary('gtdbtk_out/classify/gtdbtk.bac120.summary.tsv')
df_species_level = df[df['species'] != '']  # Species-level classification
df_high_quality = df[df['msa_percent'] > 80]

TYGS / GGDC for dDDH

For publication-grade novel-species naming, dDDH is required. Use the TYGS web service (https://tygs.dsmz.de/) or GGDC (https://ggdc.dsmz.de/). Submit FASTA -> receive dDDH against type strains + phylogeny.

Mash for Fast Clustering

# Sketch all genomes (do once)
mash sketch -p 16 -o all_sketches genomes/*.fa

# All-vs-all distance
mash dist -p 16 all_sketches.msh all_sketches.msh > mash_distances.tsv

# Cluster via NJ or hierarchical clustering
python -c "
import pandas as pd
from scipy.cluster import hierarchy
df = pd.read_csv('mash_distances.tsv', sep='\t', header=None,
                 names=['ref', 'query', 'distance', 'pvalue', 'shared_hashes'])
matrix = df.pivot('ref', 'query', 'distance').fillna(0)
linkage = hierarchy.linkage(matrix.values, method='average')
hierarchy.dendrogram(linkage, labels=matrix.columns)
"

Reconciliation: When Methods Disagree

Pattern	Likely cause	Action
skani says 96%, FastANI says 95.5%	Tool difference; both > 95% threshold	Consistent species call
skani says 95.2%, FastANI says 94.8%	Borderline case, tool difference	Cross-validate with OrthoANI; check GTDB-Tk
ANI = 94%, dDDH = 70%	Different metrics; both borderline same-species	Standard concordant ambiguous; report both
16S rRNA same; ANI < 95%	16S insufficient for species (Stackebrandt 2006)	Trust ANI; reclassify
GTDB says species X, NCBI says species Y	GTDB taxonomy differs from NCBI for ~10% of species	Use GTDB for genome-based; cite both if external comparison
ANI 96%, AF 0.3	Insufficient alignment fraction	Same-species call invalid; report cautious
ANI 99%, AF 0.9	Same species, high confidence	Robust species call
Mash 0.04 distance, FastANI 94.5% ANI	Different units; both around species threshold	Use ANI for definitive call
OrthoANI 94%, FastANI 96%	Method variation	Cross-check with skani; report range
GTDB-Tk classifies but MSA % < 50	Low-quality placement	Report at higher rank (genus) only
TYGS gives unexpected nearest type strain	Type-strain database issue	Verify type-strain quality; cross-validate with GTDB
MAG GTDB-Tk classify fails	Incomplete MAG; missing markers	Improve assembly; report at family or order only

Operational rule for publication: GTDB-Tk classify_wf as primary classification + ANI to nearest type strain (skani or FastANI) + AF >= 0.5 + report Tettelin partition where relevant + cross-validate with dDDH (TYGS) for novel-species claims.

Cohort Gotchas

• Archaea: GTDB has separate ar53 marker set; specify --archaea or let GTDB-Tk auto-detect
• Cyanobacteria: large genomes, sometimes split GTDB-Tk markers; cross-validate
• MAGs from metagenomes: require CheckM2 quality filter first
• Strain-level resolution: ANI > 99% needed; epidemiological context useful
• Endosymbionts: small / reduced genomes; ANI may be unreliable
• High-GC genomes (Streptomyces, Mycobacterium): GTDB-Tk specific markers handle these
• Type strains: authoritative anchor for taxonomy; TYGS automatic
• Pre-2017 ANI publications: likely used 30-fold lower-precision tools; verify current ANI
• Sub-species naming: ANI 99% + biology + epidemiology; not standardized
• Cross-domain comparisons (Bacteria vs Archaea): rarely meaningful; AAI better

Anticipated Reviewer Pushback

Pushback	Standard response
"Why 95% ANI?"	Jain 2018 Nat Comm 9:5114; 90,000 prokaryote genomes demonstrate clear bimodality at 95%
"Genus-specific radius?"	GTDB-Tk uses genus-specific ANI radius; ranges 94-99% per genus
"AF >= 0.5 reported?"	Yes; standard convention; sub-0.5 invalidates species call
"Tool choice?"	skani 2.4+ default in GTDB-Tk; FastANI for verification; OrthoANI as third check for borderline cases
"GTDB vs NCBI?"	GTDB taxonomy is genome-based; NCBI is heritage; cite GTDB primary, NCBI secondary
"Type strain comparison?"	TYGS automatic type-strain matching; reported alongside ANI
"MAG quality?"	CheckM2 >= 70% completeness, < 5% contamination required; reported per MAG
"dDDH for novel species?"	TYGS performed; dDDH >= 70% threshold for same species
"Multiple methods agree?"	skani + FastANI + OrthoANI converged within 1% on candidate pairs
"GTDB release version?"	r220 (2024-Q3); database version pinned

Common Errors

Error / symptom	Cause	Solution
skani "sketch incompatible"	Old sketch + new skani	Rebuild sketches; use same skani version
GTDB-Tk "marker not found"	Wrong GTDB version	Update GTDB-Tk + database to matching release
FastANI "no alignment"	< 75% ANI	Switch to AAI or Mash
pyani memory exhaustion	> 50 genomes	Use skani or pre-cluster with Mash
Mash distance pvalue uninformative	Default	Use NA filter; trust distance directly
TYGS web rate limit	Submitting too many genomes	Batch submissions; use offline GGDC if available
GTDB-Tk classify_wf classification empty	Genomes < 50% complete	Filter MAGs with CheckM2 first
OrthoANI very slow	All-vs-all on > 50 genomes	Use skani for screening
skani sketch GTDB databases	GTDB-Tk auto-sketches	Don't re-sketch separately
Custom species ID needed	TYGS supports type-strain comparison + GGDC for dDDH	Use both
ANI vs 16S contradict	16S insufficient	Trust ANI
Subspecies confusion	ANI > 99% + biology required	Standardize naming

Tool Installation Notes

# skani
conda install -c bioconda skani

# FastANI
conda install -c bioconda fastani

# GTDB-Tk
conda install -c bioconda gtdbtk
# Download database
wget https://data.gtdb.ecogenomic.org/releases/release220/220.0/auxillary_files/gtdbtk_r220_data.tar.gz
tar xf gtdbtk_r220_data.tar.gz
export GTDBTK_DATA_PATH=$PWD/release220

# pyani / pyANI
pip install pyani-plus

# Mash + Dashing 2
conda install -c bioconda mash
conda install -c bioconda dashing2

# CheckM2 for MAG QC
conda install -c bioconda checkm2

# OrthoANI / OrthoANIu
git clone https://github.com/EzbioCloud-Bioinformatics-Team/OrthoANIu

For 1000+ genome scans, use cluster with 64+ cores; skani all-vs-all on 1000 genomes runs in ~10-30 minutes; GTDB-Tk classify_wf runs in ~20-60 min per 100 genomes.

References

• Jain C et al 2018 Nat Commun 9:5114 (FastANI; 95% ANI threshold)
• Shaw J & Yu YW 2023 Nat Methods 20:1661 (skani)
• Chaumeil P-A et al 2020 Bioinformatics 36:1925 (GTDB-Tk v1)
• Chaumeil P-A et al 2022 Bioinformatics 38:5315 (GTDB-Tk v2)
• Parks DH et al 2018 Nat Biotech 36:996 (GTDB establishment)
• Parks DH et al 2022 Nat Biotech 40:1660 (GTDB r207)
• Meier-Kolthoff JP & Goker M 2019 NAR 47:W42 (TYGS)
• Auch AF et al 2010 SAB 2:117 (GGDC)
• Ondov BD et al 2016 Genome Biol 17:132 (Mash MinHash)
• Baker DN & Langmead B 2023 Genome Biol 24:36 (Dashing 2)
• Pritchard L et al 2016 Anal Methods 8:12 (pyani)
• Lee I et al 2016 IJSEM 66:1100 (OrthoANIu)
• Richter M & Rossello-Mora R 2009 PNAS 106:19126 (JSpeciesWS)
• Goris J et al 2007 IJSEM 57:81 (ANI threshold validation)
• Konstantinidis KT & Tiedje JM 2005 PNAS 102:2567 (AAI concept)
• Stackebrandt E & Ebers J 2006 Microbiol Today 33:152 (16S thresholds; superseded)
• Chklovski A et al 2023 Nat Methods 20:1203 (CheckM2)
• Olm MR et al 2017 ISME J 11:2864 (dRep; ANI clustering)
• Rodriguez-R LM & Konstantinidis KT 2016 Microbe 11:8 (ANI reference)
• Yoon S-H et al 2017 IJSEM 67:1613 (ANI species delimitation)
• Larralde M et al 2025 bioRxiv (pyskani, pyfastani, pyorthoani)

Related Skills

• comparative-genomics/pangenome-analysis - ANI-based clustering precedes pangenome construction
• comparative-genomics/ortholog-inference - Cross-species ANI as orthology benchmark
• comparative-genomics/hgt-detection - High-ANI same-species genomes for HGT context
• comparative-genomics/gene-tree-species-tree-reconciliation - Species-tree construction precedes ANI species delineation
• phylogenetics/species-trees - Marker-gene tree alongside ANI
• metagenomics/kraken-classification - Metagenomic classification different problem
• metagenomics/metaphlan-profiling - Profile vs taxonomic placement
• genome-assembly/assembly-qc - Quality before classification
• read-qc/quality-reports - CheckM2 on MAGs
• variant-calling/clinical-interpretation - Pathogen typing context