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

Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

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name	bio-comparative-genomics-genome-distance-and-species-delineation
description	Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.
tool_type	cli
primary_tool	skani

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 Nat Commun 10:2182), GGDC v3.0 (web), Mash 2.3+ (Ondov 2016 Genome Biol 17:132), Dashing 2 (Baker & Langmead 2023 Genome Res 33:1218), CompareM 0.1.2+ for AAI (Parks/Cherubini), pyANI 0.3.1+, BLAT 36+, DIAMOND 2.1+. JSpeciesWS web (Richter et al 2016 Bioinformatics 32:929).

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 Nat Commun 10:2182)	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 Stand Genomic Sci 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 GR 33:1218)	Sketching with Bloom filter optimization	Same as Mash but faster	5-10x faster than Mash	Newer; less broadly used
pyskani (Larralde 2025 NAR Genom Bioinform)	Python wrapper around skani	ANI in Python

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	Operational convention (GTDB-Tk / skani); 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%	Operational convention; 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]..replace(, )
    df[] = df[].[]..replace(, )
     df


df = parse_gtdbtk_summary()
df_species_level = df[df[] != ]  
df_high_quality = df[df[] > ]

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 Nucleic Acids Res 50:D785 (GTDB r207)
Meier-Kolthoff JP & Goker M 2019 Nat Commun 10:2182 (TYGS)
Auch AF et al 2010 Stand Genomic Sci 2:117 (GGDC)
Ondov BD et al 2016 Genome Biol 17:132 (Mash MinHash)
Baker DN & Langmead B 2023 Genome Res 33:1218 (Dashing 2)
Pritchard L et al 2016 Anal Methods 8:12 (pyani)
Lee I et al 2016 IJSEM 66:1100 (OrthoANI)
Richter M et al 2016 Bioinformatics 32:929 (JSpeciesWS web server)
Goris J et al 2007 IJSEM 57:81 (ANI threshold validation)
Konstantinidis KT & Tiedje JM 2005 PNAS 102:2567 (species definition via ANI + gene content)
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 2014 Microbe 9:111 (ANI reference)
Yoon S-H et al 2017 IJSEM 67:1613 (EzBioCloud database)
Larralde M et al 2025 NAR Genom Bioinform 7:lqaf095 (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

name	bio-comparative-genomics-genome-distance-and-species-delineation
description	Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.
tool_type	cli
primary_tool	skani

Version Compatibility

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

Genome Distance and Species Delineation

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 Nat Commun 10:2182)	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 Stand Genomic Sci 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 GR 33:1218)	Sketching with Bloom filter optimization	Same as Mash but faster	5-10x faster than Mash	Newer; less broadly used
pyskani (Larralde 2025 NAR Genom Bioinform)	Python wrapper around skani	ANI in Python

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.

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

GTDB-Tk database version mismatch

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

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.

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

High alignment fraction required (AF >= 0.5)

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

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.

Mash distance vs ANI inconsistency

Trigger: Using Mash distance for species delineation directly.

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	Operational convention (GTDB-Tk / skani); 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%	Operational convention; 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]..replace(, )
    df[] = df[].[]..replace(, )
     df


df = parse_gtdbtk_summary()
df_species_level = df[df[] != ]  
df_high_quality = df[df[] > ]

TYGS / GGDC for dDDH

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

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 Nucleic Acids Res 50:D785 (GTDB r207)
Meier-Kolthoff JP & Goker M 2019 Nat Commun 10:2182 (TYGS)
Auch AF et al 2010 Stand Genomic Sci 2:117 (GGDC)
Ondov BD et al 2016 Genome Biol 17:132 (Mash MinHash)
Baker DN & Langmead B 2023 Genome Res 33:1218 (Dashing 2)
Pritchard L et al 2016 Anal Methods 8:12 (pyani)
Lee I et al 2016 IJSEM 66:1100 (OrthoANI)
Richter M et al 2016 Bioinformatics 32:929 (JSpeciesWS web server)
Goris J et al 2007 IJSEM 57:81 (ANI threshold validation)
Konstantinidis KT & Tiedje JM 2005 PNAS 102:2567 (species definition via ANI + gene content)
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 2014 Microbe 9:111 (ANI reference)
Yoon S-H et al 2017 IJSEM 67:1613 (EzBioCloud database)
Larralde M et al 2025 NAR Genom Bioinform 7:lqaf095 (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

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

来源信息

安装方式

检查来源文件

同仓库更多 Skills

Version Compatibility

Genome Distance and Species Delineation

Algorithmic Taxonomy

Decision Tree by Experimental Scenario

Per-Tool Failure Modes

skani / FastANI ANI saturating below 75%

GTDB-Tk database version mismatch

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

High alignment fraction required (AF >= 0.5)

MAG contamination / incompleteness

Mash distance vs ANI inconsistency

Ortholog-based ANI conservative vs alignment-based

Tetranucleotide bias inflating Mash for low-GC genomes

Type strain conflicts in TYGS

Quantitative Thresholds

skani Standard Workflow

GTDB-Tk Classification Workflow

TYGS / GGDC for dDDH

Mash for Fast Clustering

Reconciliation: When Methods Disagree

Cohort Gotchas

Anticipated Reviewer Pushback

Common Errors

Tool Installation Notes

References

Related Skills

同仓库更多 Skills

Version Compatibility

Genome Distance and Species Delineation

Algorithmic Taxonomy

Decision Tree by Experimental Scenario

Per-Tool Failure Modes

skani / FastANI ANI saturating below 75%

GTDB-Tk database version mismatch

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

High alignment fraction required (AF >= 0.5)

MAG contamination / incompleteness

Mash distance vs ANI inconsistency

Ortholog-based ANI conservative vs alignment-based

Tetranucleotide bias inflating Mash for low-GC genomes

Type strain conflicts in TYGS

Quantitative Thresholds

skani Standard Workflow

GTDB-Tk Classification Workflow

TYGS / GGDC for dDDH

Mash for Fast Clustering

Reconciliation: When Methods Disagree

Cohort Gotchas

Anticipated Reviewer Pushback

Common Errors

Tool Installation Notes

References

Related Skills