Cited by 5,748Genomics and Phylogenetic StudiesBacterial Identification and Susceptibility TestingMicrobial Community Ecology and Physiology

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High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries

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Jain, Chirag, Rodriguez-R, Luis M., Phillippy, Adam M., Konstantinidis, Konstantinos T., Aluru, Srinivas·2018·Nature Communications

DOI 10.1038/s41467-018-07641-9PMC PMC6269478

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Abstract

A fundamental question in microbiology is whether there is continuum of genetic diversity among genomes, or clear species boundaries prevail instead. Whole-genome similarity metrics such as Average Nucleotide Identity (ANI) help address this question by facilitating high resolution taxonomic analysis of thousands of genomes from diverse phylogenetic lineages. To scale to available genomes and beyond, we present FastANI, a new method to estimate ANI using alignment-free approximate sequence mapping. FastANI is accurate for both finished and draft genomes, and is up to three orders of magnitude faster compared to alignment-based approaches. We leverage FastANI to compute pairwise ANI values among all prokaryotic genomes available in the NCBI database. Our results reveal clear genetic discontinuity, with 99.8% of the total 8 billion genome pairs analyzed conforming to >95% intra-species and <83% inter-species ANI values. This discontinuity is manifested with or without the most frequently sequenced species, and is robust to historic additions in the genome databases.

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Fig. 1 Correlation of FastANI and Mash-based ANI output with ANIb values for datasets D1–D5. Because FastANI assumes a probabilistic identity cutoff that is set to 80% by default, it reports 76, 570, 4271, 464, and 130 genome matches for the individual queries in datasets D1–D5 respectively. To enable a direct quality comparison against FastANI, Mash is executed for only those pairs that are reported by FastANI. Notice that each dataset encompasses a different nucleotide identity range (x-axes). Gray line represents a straight line y = x plot for reference. Pearson correlation coefficients corresponding to these plots are listed separately in Table 2. Last plot shows error of these methods w.r.t. ANIb using all five datasetsCC BY · PubMed Central

Fig. 2 Scaling up FastANI’s performance using multi-core parallel execution. We executed parallel FastANI processes on 40 physical cores, where each process was assigned an equally sized part of the reference D1–D5 databases. Left and right plots evaluate FastANI’s compute and indexing phase, respectively. FastANI achieves reasonable speedups on all datasets except the compute phase in D1 and D5, as their runtime on a single core is too small to begin with (Table 3)CC BY · PubMed Central

Fig. 3 Genetic discontinuity observed using 90 K genomes. a Histogram plot showing the distribution of ANI values among the 90 K genomes. Only ANI values in the 76–100% range are shown. Out of total 8.01 billion pairwise genome comparisons, FastANI reported only 17M ANI values (0.21%) with ANI between 83 and 95% indicating a genetic discontinuum. Multiple colors are used to show how genomes from different genera are contributing to this distribution. Few peaks in the histogram arise from genera that have been extensively sequenced and dominate the database. b Density curves of ANI values in the ANI range 76–100%. Each curve shows the density curve corresponding to the database at a particular time period. Discontinuity in all four curves is observed consistently. c. Distribution of ANI values with each comparison labeled by the nomenclature of genomes being compared. All the comparisons between Escherichia coli and Shigella spp. have been labeled separately. The 95% ANI threshold on x-axis serves as a valid classifier for comparisons belonging to same and different speciesCC BY · PubMed Central

Fig. 4 FastANI algorithm explained using synthetic and real examples. a Illustration of FastANI’s work-flow for computing ANI between a query genome and a reference genome. Five mappings are obtained from three query fragments using Mashmap24. Mforward saves the maximum identity mapping for each query fragment. In this example, Mforward = {m2, m4, m5}. From this set, Mreciprocal picks m4 and m5 as the maximum identity mapping for each reference bin. Mapping identities of orthologous mappings, thus found in Mreciprocal, are finally averaged to compute ANI. b FastANI supports visualization of the orthologous mappings Mreciprocal that are used to estimate the ANI value using genoPlotR39. In this figure, ANI is computed between Bartonella quintana strain (NC_018533.1) as query and Bartonella henselae strain (NC_005956.1) as reference. Red line segments denote the orthologous mappings computed by FastANI for ANI estimationCC BY · PubMed Central

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Multi-omic profiling of a novel activated sludge strain Sphingobacterium sp. WM1 reveals the mechanism of tetracycline biodegradation and its merits of potential application
Chen, X., Zhu, Y., Chen, J., Yan, S., & Xie, S. (2023) · Water Research · doi:10.1016/j.watres.2023.120397 · Cited by 56
The application of molecular tools to study the drinking water microbiome – Current understanding and future needs
Zhang, Y., & Liu, W. (2019) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2019.1571351 · Cited by 53
“and targeted variable regions) Genus–family 12%–15% in underestimating diversity (Brooks et al., 2015; Jain et al., 2017) Source: Zhang and Liu, forthcoming.”
Background
Population genomics of Staphylococcus pseudintermedius in companion animals in the United States
Smith, J. T., Amador, S., McGonagle, C. J., Needle, D. B., Gibson, R. J., & Andam, C. P. (2020) · Communications Biology · doi:10.1038/s42003-020-1009-y · Cited by 55
Microdiversity characterizes prevalent phylogenetic clades in the glacier-fed stream microbiome
Fodelianakis, S., Washburne, A., Bourquin, M., Pramateftaki, P. V., Kohler, T. J., Styllas, M. N., Tolosano, M., Staercke, V. D., Schön, M., Busi, S. B., et al. (2021) · The ISME Journal · doi:10.1038/s41396-021-01106-6 · Cited by 55
Genome of a Novel Bacterium “Candidatus Jettenia ecosi” Reconstructed From the Metagenome of an Anammox Bioreactor
Mardanov, A. V., Beletsky, A. V., Ravin, N. V., Botchkova, E. A., Litti, Y., & Nozhevnikova, A. N. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02442 · Cited by 54
“The ANI between the genomes of J2 and KSU-1 was 94.91%, a value just below the species boundary cutoff of 95% (Konstantinidis and Tiedje, 2005; Jain et al., 2018), suggesting a recent divergence and speciation of these species (Olm et al., 2019).”
BackgroundInfluential
Unraveling the emergence and population diversity of Listeria monocytogenes in a newly built meat facility through whole genome sequencing
Álvarez-Molina, A., Cobo-Díaz, J. F., López, M., Maradona, M. P., de Toro, M., & Álvarez‐Ordóñez, A. (2021) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2021.109043 · Cited by 53
Chloroplast genomes of five Oedogonium species: genome structure, phylogenetic analysis and adaptive evolution
Xiong, Q., Hu, Y.-X., Lv, W., Wang, Q.-H., Liu, G.-X., & Hu, Z.-Y. (2021) · BMC Genomics · doi:10.1186/s12864-021-08006-1 · Cited by 23
Ecological Interactions of Cyanobacteria and Heterotrophs Enhances the Robustness of Cyanobacterial Consortium for Carbon Sequestration
Ataeian, M., Liu, Y., Kouris, A., Hawley, A. K., & Strous, M. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.780346 · Cited by 51
“MetaErg was used for annotation of the MAGs (Dong and Strous, 2019). fastANI (Jain et al., 2018) was used for comparison of MAGs across samples to MAGs previously obtained from Kulunda soda lakes (Vavourakis et al., 2018) and Cariboo Plateau soda lakes (Zorz et al., 2019).”
Method
Genomics analysis and degradation characteristics of lignin by Streptomyces thermocarboxydus strain DF3-3
Tan, F., Cheng, J., Zhang, Y., Jiang, X., & Liu, Y. (2022) · Biotechnology for Biofuels and Bioproducts · doi:10.1186/s13068-022-02175-1 · Cited by 54
Niche differentiation among annually recurrent coastal Marine Group II Euryarchaeota
Orellana, L. H., Francis, B., Krüger, K., Teeling, H., Müller, M.-C., Fuchs, B. M., Konstantinidis, K. T., & Amann, R. (2019) · The ISME Journal · doi:10.1038/s41396-019-0491-z · Cited by 54
Self-citation
Desulfovibrio diazotrophicus sp. nov., a sulfate‐reducing bacterium from the human gut capable of nitrogen fixation
Sayavedra, L., Li, T., Batista, M. B., Seah, B. K. B., Booth, C., Zhai, Q., Chen, W., & Narbad, A. (2021) · Environmental Microbiology · doi:10.1111/1462-2920.15538 · Cited by 55
Evaluation of the Microba Community Profiler for Taxonomic Profiling of Metagenomic Datasets From the Human Gut Microbiome
Parks, D. H., Rigato, F., Vera-Wolf, P., Krause, L., Hugenholtz, P., Tyson, G. W., & Wood, D. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.643682 · Cited by 54
“A baseline of 95% ANI was selected to match the commonly used operational definition of a prokaryotic species (Jain et al., 2018; Parks et al., 2020).”
Method
Tandem gene duplications contributed to high-level azole resistance in a rapidly expanding Candida tropicalis population
Fan, X., Dai, R.-C., Zhang, S., Geng, Y.-Y., Kang, M., Guo, D.-W., Mei, Y.-N., Pan, Y.-H., Sun, Z.-Y., Xu, Y.-C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43380-2 · Cited by 55
“S1) 56 .”
Plasmid classifications
Garcillán-Barcia, M. P., Redondo, S., & de la Cruz, F. (2023) · Plasmid · doi:10.1016/j.plasmid.2023.102684 · Cited by 54
The multi-kingdom microbiome of the goat gastrointestinal tract
Cao, Y., Feng, T., Wu, Y., Xu, Y., Du, L., Wang, T., Luo, Y., Wang, Y., Li, Z., Xuan, Z., et al. (2023) · Microbiome · doi:10.1186/s40168-023-01651-6 · Cited by 65
Metagenome-wide analysis uncovers gut microbial signatures and implicates taxon-specific functions in end-stage renal disease
Zhang, P., Wang, X., Li, S., Cao, X., Zou, J., Fang, Y., Shi, Y., Xiang, F., Shen, B., Li, Y., et al. (2023) · Genome biology · doi:10.1186/s13059-023-03056-y · Cited by 55
“(“species” hereafter) at the nucleotide similarity threshold of 95% [22].”
Background
Host‐specific competitiveness to form nodules in Rhizobium leguminosarum symbiovar viciae
Boivin, S., Lahmidi, N. A., Sherlock, D., Bonhomme, M., Dijon, D., Gotty, K., Quéré, A. L., Pervent, M., Tauzin, M., Carlsson, G., et al. (2019) · New Phytologist · doi:10.1111/nph.16392 · Cited by 53
“leguminosarum, gsE (27 strains), gsC (10 strains) and gsB (3 strains), that have been previously identified in European isolates (Kumar et al., 2015; Jain et al., 2018; Cavassim et al., 2019).”
Background
Genomic and phenotypic analysis of rock phosphate-solubilizing rhizobacteria
Santos-Torres, M., Perdomo, F. A., Mendoza-Labrador, J., Gutiérrez, A. Y., Vargas, C., Castro-Rincón, E., Caro‐Quintero, A., Uribe-Vélez, D., & Estrada-Bonilla, G. A. (2020) · Rhizosphere · doi:10.1016/j.rhisph.2020.100290 · Cited by 53
Revealing the microbial heritage of traditional Brazilian cheeses through metagenomics
Kothe, C. I., Mohellibi, N., & Renault, P. (2022) · Food Research International · doi:10.1016/j.foodres.2022.111265 · Cited by 51
The microbial community of a biofilm lining the wall of a pristine cave in Western New Guinea
Turrini, P., Tescari, M., Visaggio, D., Pirolo, M., Lugli, G. A., Ventura, M., Frangipani, E., & Visca, P. (2020) · Microbiological Research · doi:10.1016/j.micres.2020.126584 · Cited by 54
Genes Influencing Phage Host Range in Staphylococcus aureus on a Species-Wide Scale
Moller, A. G., Winston, K., Ji, S., Wang, J., Hargita, M. N., Solís‐Lemus, C., & Read, T. D. (2021) · mSphere · doi:10.1128/msphere.01263-20 · Cited by 57
“31 (45), which is shown as a lower165 triangle identity matrix in Supplemental Table S1.”
Background
Destination shapes antibiotic resistance gene acquisitions, abundance increases, and diversity changes in Dutch travelers
D’Souza, A. W., Boolchandani, M., Patel, S., Galazzo, G., van Hattem, J. M., Arcilla, M. S., Melles∗, D. C., de Jong, M. D., Schultsz, C., Bootsma, M., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00893-z · Cited by 54
Genome Analysis of a Verrucomicrobial Endosymbiont With a Tiny Genome Discovered in an Antarctic Lake
Williams, T. J., Allen, M. A., Ivanova, N., Huntemann, M., Haque, S., Hancock, A. M., Brazendale, S., & Cavicchioli, R. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.674758 · Cited by 52
“FastANI (Jain et al., 2018) was used to calculate ANI between Ca. Organicella MAGs.”
Method
Shared antibiotic resistance and virulence genes in Staphylococcus aureus from diverse animal hosts
Bruce, S. A., Smith, J. T., Mydosh, J. L., Ball, J., Needle, D. B., Gibson, R. J., & Andam, C. P. (2022) · Scientific Reports · doi:10.1038/s41598-022-08230-z · Cited by 55
To kill or to be killed: pangenome analysis of Escherichia coli strains reveals a tailocin specific for pandemic ST131
Tantoso, E., Eisenhaber, B., Kirsch, M., Shitov, V. A., Zhao, Z., & Eisenhaber, F. (2022) · BMC Biology · doi:10.1186/s12915-022-01347-7 · Cited by 57
Core Genome Multilocus Sequence Typing Scheme for Improved Characterization and Epidemiological Surveillance of Pathogenic Brucella
Abdel‐Glil, M. Y., Thomas, P., Brandt, C., Melzer, F., Subbaiyan, A., Chaudhuri, P., Harmsen, D., Jolley, K. A., Janowicz, A., Garofolo, G., et al. (2022) · Journal of Clinical Microbiology · doi:10.1128/jcm.00311-22 · Cited by 54
“3 (38), and by performing a protein-based phylogeny using up to 400 universal bacterial proteins with PhyloPhlan v0.”
Method
Bacillus megaterium HgT21: a Promising Metal Multiresistant Plant Growth-Promoting Bacteria for Soil Biorestoration
Guzmán-Moreno, J., García-Ortega, L. F., Torres-Saucedo, L., Rivas-Noriega, P., Ramírez-Santoyo, R. M., Sánchez-Calderón, L., Quiroz-Serrano, I. N., & Vidales-Rodríguez, L. E. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.00656-22 · Cited by 55
“The selection of representative species was carried out according to their pairwise average nucleotide identity (ANI) values, which were determined by FastANI (102).”
Method
How can microbial population genomics inform community ecology?
VanInsberghe, D., Arevalo, P., Chien, D. M., & Polz, M. F. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0253 · Cited by 52
Metabolic Potential for Reductive Acetogenesis and a Novel Energy-Converting [NiFe] Hydrogenase in Bathyarchaeia From Termite Guts – A Genome-Centric Analysis
Loh, H. Q., Hervé, V., & Brune, A. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.635786 · Cited by 49
“Average nucleotide acid identities (ANI) were calculated with fastANI (Jain et al., 2018).”
Method
Shedding Light on Microbial “Dark Matter”: Insights Into Novel Cloacimonadota and Omnitrophota From an Antarctic Lake
Williams, T. J., Allen, M. A., Berengut, J. F., & Cavicchioli, R. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.741077 · Cited by 52
Validation of the application of gel beads-based single-cell genome sequencing platform to soil and seawater
Nishikawa, Y., Kogawa, M., Hosokawa, M., Wagatsuma, R., Mineta, K., Takahashi, K., Ide, K., Yura, K., Behzad, H., Gojobori, T., et al. (2022) · ISME Communications · doi:10.1038/s43705-022-00179-4 · Cited by 51
Comparative Genomics of the Genus Pseudomonas Reveals Host- and Environment-Specific Evolution
Saati‐Santamaría, Z., Baroncelli, R., Rivas, R., & García‐Fraile, P. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.02370-22 · Cited by 51
“33) (87) and adding the “–matrix” flag.”
gcPathogen: a comprehensive genomic resource of human pathogens for public health
Guo, C., Chen, Q., Fan, G., Sun, Y., Nie, J., Shen, Z., Meng, Z., Zhou, Y., Li, S., Wang, S., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad875 · Cited by 53
Taxogenomics and Systematics of the Genus Pantoea
Tambong, J. T. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02463 · Cited by 51
The cyanobacterium Prochlorococcus has divergent light-harvesting antennae and may have evolved in a low-oxygen ocean
Ulloa, O., Henríquez‐Castillo, C., Ramírez‐Flandes, S., Plominsky, Á. M., Murillo, A. A., Morgan‐Lang, C., Hallam, S., & Stepanauskas, R. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025638118 · Cited by 51
The Pectobacterium pangenome, with a focus on Pectobacterium brasiliense, shows a robust core and extensive exchange of genes from a shared gene pool
Jonkheer, E. M., Brankovics, B., Houwers, I., van der Wolf, J. M., Bonants, P. J. M., Vreeburg, R. A. M., Bollema, R., de Haan, J. R., Berke, L., Smit, S., et al. (2021) · BMC Genomics · doi:10.1186/s12864-021-07583-5 · Cited by 52
Accurate and robust inference of microbial growth dynamics from metagenomic sequencing reveals personalized growth ratesPreprint
Joseph, T., Chlenski, P., Litman, A., Korem, T., & Pe’er, I. (2022) · Genome Research · doi:10.1101/gr.275533.121 · Cited by 51
“We used FastANI (Jain et al. 2018) to ascertain that all MAG pairs had <95% ANI: two pairs—MAGs 6 and 19, and MAGs 8 and 22 —had ANI> 99%; we therefore discarded MAGs 19 and 22, which had lower completion.”
Method
Taxonomic and Metabolic Incongruence in the Ancient Genus Streptomyces
Chevrette, M. G., Carlos‐Shanley, C., Louie, K., Bowen, B. P., Northen, T. R., & Currie, C. R. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02170 · Cited by 49
“ANI between genomes was calculated via fastANI (Jain et al., 2018).”
Method
Wastewater treatment plant upgrade induces the receiving river retaining bioavailable nitrogen sources
Wang, Q., Liang, J., Zhao, C. N., Bai, Y., Liu, R., Liu, H., & Qu, J. (2020) · Environmental Pollution · doi:10.1016/j.envpol.2020.114478 · Cited by 50
Extensive Horizontal Gene Transfer within and between Species of Coagulase-Negative Staphylococcus
Smith, J. T., & Andam, C. P. (2021) · Genome Biology and Evolution · doi:10.1093/gbe/evab206 · Cited by 50
Analysis of global Aeromonas veronii genomes provides novel information on source of infection and virulence in human gastrointestinal diseases
Liu, F., Yuwono, C., Tay, C. Y., Wehrhahn, M. C., Riordan, S. M., & Zhang, L. (2022) · BMC Genomics · doi:10.1186/s12864-022-08402-1 · Cited by 54
Horizontal transmission enables flexible associations with locally adapted symbiont strains in deep-sea hydrothermal vent symbioses
Breusing, C., Genetti, M., Russell, S. L., Corbett-Detig, R. B., & Beinart, R. A. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115608119 · Cited by 50
Toward quantifying the adaptive role of bacterial pangenomes during environmental perturbations
Conrad, R. E., Viver, T., Gago, J. F., Hatt, J. K., Venter, S. N., Rossello-Mora, R. A., & Konstantinidis, K. T. (2021) · The ISME Journal · doi:10.1038/s41396-021-01149-9 · Cited by 46
Self-citation
A comprehensive resource for Bordetella genomic epidemiology and biodiversity studies
Bridel, S., Bouchez, V., Brancotte, B., Hauck, S., Armatys, N., Landier, A., Mühle, E., Guillot, S., Toubiana, J., Maiden, M. C. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31517-8 · Cited by 47
Expanding the phylogenetic distribution of cytochrome b -containing methanogenic archaea sheds light on the evolution of methanogenesis
Ou, Y.-F., Dong, H., McIlroy, S. J., Crowe, S. A., Hallam, S., Han, P., Kallmeyer, J., Simister, R. L., Vuillemin, A., Leu, A. O., et al. (2022) · The ISME Journal · doi:10.1038/s41396-022-01281-0 · Cited by 50
FastANI, Mash and Dashing equally differentiate between Klebsiella species
Hernández-Salmerón, J. E., & Moreno‐Hagelsieb, G. (2022) · PeerJ · doi:10.7717/peerj.13784 · Cited by 49
“32 ( Jain et al., 2018 ), which calculates a close approximation to the original ANI implementation ( Goris et al., 2007 ), which was based on pairwise alignments produced with blastn ( Altschul et al., 1997 ).”
Metagenomics-resolved genomics provides novel insights into chitin turnover, metabolic specialization, and niche partitioning in the octocoral microbiome
Keller‐Costa, T., Kozma, L., Silva, S. G., Toscan, R. B., Gonçalves, J. M. S., Lago‐Lestón, A., Kyrpides, N. C., da Rocha, U. N., & Costa, R. (2022) · Microbiome · doi:10.1186/s40168-022-01343-7 · Cited by 50

Authors: Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T., & Aluru, S.
Affiliations: National Institutes of Health (US)
Georgia Institute of Technology (US)
Corresponding author: Konstantinidis, Konstantinos T.
Aluru, Srinivas
ORCID: Jain, Chirag
Rodriguez-R, Luis M.
Phillippy, Adam M.
Konstantinidis, Konstantinos T.
Aluru, Srinivas
Venue: Nature Communications
Publisher: Springer Science and Business Media LLC
Published: 2018-11-30
Volume: 9
Issue: 1
Pages: 5114-5114
ISSN: 2041-1723 (ISSN-L)
DOI: 10.1038/s41467-018-07641-9
PMC: PMC6269478
OpenAlex: W2951254987
Cited by: 5,748
References: 40
Citations by year: 2026: 1107 · 2025: 1110 · 2024: 977 · 2023: 789 · 2022: 695 · 2021: 617 · 2020: 305 · 2019: 128 · 2018: 16
FWCI: 96.11
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Nature Communications (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, other-oa)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders: National Science Foundation (1416259, 1816027, 1356288)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Health Sciences › Medicine › Microbiology (medical) › Bacterial Identification and Susceptibility Testing
Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
Keywords: average nucleotide identity, prokaryotic genomes, species boundaries, taxonomic classification, draft genome sequence
MeSH terms: Phylogeny, Prokaryotic Cells, Genetic Variation, Databases, Factual, Genome, Bacterial, Sequence Analysis, DNA

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High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries

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