High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries
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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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FastANI was used to calculate the average nucleotide identity between the isolated strains and the original Lp082, ensuring isolates were evolved from Lp082.
“FastANI (v1.3.1) (https://github.com/ParBLiSS/FastANI.) was used to calculate the average nucleotide identity between the isolated strains and the original Lp082,47 ensuring isolates were evolved from Lp082”
From Gut microbiota drives structural variation of exogenous probiotics to enhance colonization (2025) · cites this paper as [47]
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“At least one high-quality draft genome for most of the IsmAencoding MSPs was generated (completeness > 90%, contamination < 5%; Parks et al., 2015), and genome-wide similarity comparison confirmed that these MSPs are different species with a maximum average nucleotide identity of 88% (Figure S5E; Table S4) (Jain et al…”
BackgroundHigh-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome
Zheng, W., Zhao, S., Yin, Y., Zhang, H., Needham, D. M., Evans, E. D., Dai, C. L., Lu, P. J., Alm, E. J., & Weitz, D. A. (2022) · Science · doi:10.1126/science.abm1483 · Cited by 283 · see citing works
High-throughput microbial culturomics using automation and machine learning
Huang, Y., Sheth, R. U., Zhao, S., Cohen, L. A., Dabaghi, K., Moody, T., Sun, Y., Ricaurte, D., Richardson, M., Velez-Cortes, F., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01674-2 · Cited by 283 · see citing works
Colbert, L. E., Alam, M. B. E., Wang, R., Karpinets, T. V., Lo, D., Lynn, E. J., Harris, T. A., Elnaggar, J. H., Yoshida-Court, K., Tomasic, K., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.09.012 · Cited by 279 · see citing works
Genomic diversity landscape of the honey bee gut microbiota
Ellegaard, K., & Engel, P. (2019) · Nature Communications · doi:10.1038/s41467-019-08303-0 · Cited by 274 · see citing works
Machine learning for microbiologists
Asnicar, F., Thomas, A. M., Passerini, A., Waldron, L., & Segata, N. (2023) · Nature Reviews Microbiology · doi:10.1038/s41579-023-00984-1 · Cited by 269 · see citing works
“Cluster similarity thresholds for these tasks are typically set to 97% identity among 16S rRNA genes 56,57 to define operational taxonomic units and 95% whole-genome identity among strains to define species 55,58 .”
Li, C., Stražar, M., Mohamed, A. M., Ávila-Pacheco, J., Walker, R. L., Lebar, T., Zhao, S., Lockart, J., Dame, A., Thurimella, K., et al. (2024) · Cell · doi:10.1016/j.cell.2024.03.014 · Cited by 260 · see citing works
Bactopia: a Flexible Pipeline for Complete Analysis of Bacterial Genomes
Petit, R. A., & Read, T. D. (2020) · mSystems · doi:10.1128/msystems.00190-20 · Cited by 245 · see citing works
“For this project we used BaTs to a) summarize the results of running multiple 88 bacterial genomes through BaAP, b) extract 16S sequences and create a phylogeny, c) assign 89 taxonomic classifications with the Genome Taxonomy Database (4), d) subset Lactobacillus 90 crispatus samples by average nucleotide identity (AN…”
BackgroundMethodInfluentialReconstruction of ancient microbial genomes from the human gut
Wibowo, M. C., Yang, Z., Borry, M., Hübner, A., Huang, K. D., Tierney, B. T., Zimmerman, S. E., Barajas-Olmos, F. M., Contreras-Cubas, C., Garcia‐Ortíz, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03532-0 · Cited by 237 · see citing works
Fast and robust metagenomic sequence comparison through sparse chaining with skani
Shaw, J., & Yu, Y. W. (2023) · Nature Methods · doi:10.1038/s41592-023-02018-3 · Cited by 243 · see citing works
“This means that a minimizer ANI estimator cannot use very sparse seeds, since the fraction of selected seeds is”
BackgroundMethodGenome mining of biosynthetic and chemotherapeutic gene clusters in Streptomyces bacteria
Belknap, K. C., Park, C. J., Barth, B. M., & Andam, C. P. (2020) · Scientific Reports · doi:10.1038/s41598-020-58904-9 · Cited by 242 · see citing works
MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio High Fidelity readsPreprint
Uliano‐Silva, M., Ferreira, J. G. R. N., Krasheninnikova, K., Formenti, G., Abueg, L. A. L., Torrance, J., Myers, E. W., Durbin, R. M., Blaxter, M., & McCarthy, S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.23.521667 · Cited by 242 · see citing works
Durrant, M. G., Li, M. M., Siranosian, B. A., Montgomery, S. B., & Bhatt, A. S. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2019.10.022 · Cited by 239 · see citing works
Genomics and pathotypes of the many faces of Escherichia coli
Geurtsen, J., de Been, M., Weerdenburg, E., Zomer, A., McNally, A., & Poolman, J. (2022) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuac031 · Cited by 212 · see citing works
“Based purely on ANI, E. coli and Shigella combined represent a singular typical species with a whole-genome average nucleotide identity of up to 98% (Jain et al. 2018).”
BackgroundDiversity and functional landscapes in the microbiota of animals in the wild
Levin, D., Raab, N., Pinto, Y., Rothschild, D., Zanir, G., Godneva, A., Mellul, N., Futorian, D., Gal, D., Leviatan, S., et al. (2021) · Science · doi:10.1126/science.abb5352 · Cited by 234 · see citing works
“The maximum ANI values were used for novelty categorization as follows: rSGBs with maximum ANI values above 95% were defined as known species, values below 83% as previously undescribed (novel) species, and values in between as intermediate.”
MethodInfluentialDrivers and determinants of strain dynamics following fecal microbiota transplantation
Schmidt, T. S. B., Li, S. S., Maistrenko, O. M., Akanni, W., Coelho, L. P., Dolai, S., Fullam, A., Glazek, A. M., Hercog, R., Herrema, H., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01913-0 · Cited by 233 · see citing works
A collection of bacterial isolates from the pig intestine reveals functional and taxonomic diversity
Wylensek, D., Hitch, T. C. A., Riedel, T., Afrizal, A., Kumar, N., Wortmann, E., Liu, T., Devendran, S., Lesker, T. R., Hernández, S. B., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19929-w · Cited by 232 · see citing works
BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters
Kautsar, S. A., van der Hooft, J. J. J., de Ridder, D., & Medema, M. H. (2021) · GigaScience · doi:10.1093/gigascience/giaa154 · Cited by 224 · see citing works
Charting the Complexity of the Marine Microbiome through Single-Cell Genomics
Pachiadaki, M., Brown, J. M., Brown, J., Bezuidt, O. K. I., Berube, P. M., Biller, S. J., Poulton, N. J., Burkart, M. D., Clair, J. J. L., Chisholm, S. W., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.017 · Cited by 218 · see citing works
“Prokka was run a second time with a custom protein annotation database built from compiling Swiss-Prot (Bateman et al., 2017) entries for Archaea and Bacteria.”
BackgroundMethodInfluentialHost preference and invasiveness of commensal bacteria in the Lotus and Arabidopsis root microbiota
Wippel, K., Tao, K., Niu, Y., Zgadzaj, R., Kiel, N., Guan, R., Dahms, E., Zhang, P., Jensen, D. B., Logemann, E., et al. (2021) · Nature Microbiology · doi:10.1038/s41564-021-00941-9 · Cited by 212 · see citing works
Extensive microbial diversity within the chicken gut microbiome revealed by metagenomics and culture
Gilroy, R., Ravi, A., Getino, M., Pursley, I., Horton, D. L., Alikhan, N.-F., Baker, D., Gharbi, K., Hall, N., Watson, M., et al. (2021) · PeerJ · doi:10.7717/peerj.10941 · Cited by 231 · see citing works
Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity
Li, Z., Pan, D., Wei, G., Pi, W.-L., Zhang, C., Wang, J., Peng, Y., Zhang, L., Wang, Y., Hubert, C. R. J., et al. (2021) · The ISME Journal · doi:10.1038/s41396-021-00932-y · Cited by 223 · see citing works
Consistent Metagenome-Derived Metrics Verify and Delineate Bacterial Species Boundaries
Olm, M., Crits-Christoph, A., Diamond, S., Lavy, A., Carnevali, P. B. M., & Banfield, J. (2020) · mSystems · doi:10.1128/msystems.00731-19 · Cited by 195 · see citing works
“Discrete sequence populations have been identified in large public genome databases (8, 9), most recently in a study using 90,000 bacterial genomes available in the public NCBI Genome database as of March 2017 (10).”
InfluentialLiang, J., Mao, G., Yin, X., Ma, L., Liu, L., Bai, Y., Zhang, T., & Qu, J. (2019) · Water Research · doi:10.1016/j.watres.2019.115160 · Cited by 218 · see citing works
Towards the biogeography of prokaryotic genes
Coelho, L. P., Alves, R., del Río, Á. R., Myers, P. N., Cantalapiedra, C. P., Giner‐Lamia, J., Schmidt, T., Mende, D. R., Orakov, A. N., Letunić, I., et al. (2021) · Nature · doi:10.1038/s41586-021-04233-4 · Cited by 199 · see citing works
Robust variation in infant gut microbiome assembly across a spectrum of lifestyles
Olm, M. R., Dahan, D., Carter, M. M., Merrill, B. D., Yu, F. B., Jain, S., Meng, X., Tripathi, S. M., Wastyk, H. C., Neff, N. F., et al. (2022) · Science · doi:10.1126/science.abj2972 · Cited by 210 · see citing works
SeqCode: a nomenclatural code for prokaryotes described from sequence data
Hedlund, B. P., Chuvochina, M. S., Hugenholtz, P., Konstantinidis, K. T., Murray, A. E., Palmer, M., Parks, D. H., Probst, A. J., Reysenbach, A., Rodriguez‐R, L. M., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01214-9 · Cited by 213 · see citing works
Self-citationPinnell, L. J., & Turner, J. W. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01252 · Cited by 212 · see citing works
“…all publicly available Desulfovibrionaceae, Desulfobacteraceae, and Desulfobulbaceae genomes in GenBank (n = 122, 107, and 65, respectively) by average nucleotide identity (ANI) using fastANI (Jain et al., 2018), using >95% ANI as the intraspecies threshold and 83% as an interspecies threshold.”
MethodDurrant, M. G., Fanton, A., Tycko, J., Hinks, M. M., Chandrasekaran, S. S., Perry, N. T., Schaepe, J. M., Du, P. P., Lotfy, P., Bassik, M. C., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01494-w · Cited by 212 · see citing works
An ANI gap within bacterial species that advances the definitions of intra-species units
Rodriguez‐R, L. M., Conrad, R. E., Viver, T., Feistel, D. J., Lindner, B. G., Venter, S. N., Orellana, L. H., Amann, R., Rossello-Mora, R. A., & Konstantinidis, K. T. (2023) · mBio · doi:10.1128/mbio.02696-23 · Cited by 212 · see citing works
“In summary, it appears that a natural gap in ANI values can be used to define prokaryotic species and has been largely consistent with how species are recognized (21).”
BackgroundMethodResultInfluentialSelf-citationLeech, J., Cabrera-Rubio, R., Walsh, A. M., Macori, G., Walsh, C. J., Barton, W., Finnegan, L., Crispie, F., O’Sullivan, O., Claesson, M. J., et al. (2020) · mSystems · doi:10.1128/msystems.00522-20 · Cited by 200 · see citing works
Minimal standards for the description of new genera and species of rhizobia and agrobacteria
de Lajudie, P., Andrews, M., Ardley, J. K., Eardly, B. D., Jumas‐Bilak, E., Kuzmanović, N., Lassalle, F., Lindström, K., Mhamdi, R., Martínez‐Romero, E., et al. (2019) · INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY · doi:10.1099/ijsem.0.003426 · Cited by 209 · see citing works
Prokaryotic taxonomy and nomenclature in the age of big sequence data
Hugenholtz, P., Chuvochina, M. S., Oren, A., Parks, D. H., & Soo, R. M. (2021) · The ISME Journal · doi:10.1038/s41396-021-00941-x · Cited by 208 · see citing works
Tang, C., Kong, X., Ahmed, S. A., Thakur, R., Chowdhary, A., Nenoff, P., Uhrlaß, S., Verma, S. B., Meis, J. F. G. M., Kandemir, H., et al. (2021) · Mycopathologia · doi:10.1007/s11046-021-00544-2 · Cited by 207 · see citing works
Phage–host coevolution in natural populations
Piel, D., Bruto, M., Labreuche, Y., Blanquart, F., Goudenège, D., Barcia‐Cruz, R., Chenivesse, S., Panse, S. L., James, A., Dubert, J., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01157-1 · Cited by 204 · see citing works
Kashaf, S. S., Proctor, D. M., Deming, C., Saary, P., Hölzer, M., Program, N. C. S., Mullikin, J. C., Thomas, J., Young, A., Bouffard, G., et al. (2021) · Nature Microbiology · doi:10.1038/s41564-021-01011-w · Cited by 199 · see citing works
A Reverse Ecology Approach Based on a Biological Definition of Microbial Populations
Arevalo, P., VanInsberghe, D., Elsherbini, J., Gore, J., & Polz, M. F. (2019) · Cell · doi:10.1016/j.cell.2019.06.033 · Cited by 201 · see citing works
“We observe that both the ecologically differentiated species and populations identified by PopCOGenT are generally more closely related than measured by conventional marker gene-based analyses or are predicted by current species definitions such as based on average nucleotide identity (ANI) (Jain et al., 2018; Mende e…”
Klebsiella oxytoca Complex: Update on Taxonomy, Antimicrobial Resistance, and Virulence
Yang, J., Long, H., Hu, Y., Feng, Y., McNally, A., & Zong, Z. (2021) · Clinical Microbiology Reviews · doi:10.1128/cmr.00006-21 · Cited by 201 · see citing works
“The corresponding genome sequences of strains harboring blaOXY-10, blaOXY-11, and blaOXY-12 were examined for precise species identification as described previously (23, 24).”
MethodBovio-Winkler, P., Guerrero, L. D., Erijman, L., Oyarzúa, P., Suárez‐Ojeda, M. E., Cabezas, A., & Etchebehere, C. (2023) · BMC Microbiology · doi:10.1186/s12866-023-02765-5 · Cited by 204 · see citing works
“The Average Nucleotide Identities (ANI) and the Average Amino Acid Identity (AAI) between MAGs and isolated species were determined using FastANI [55] and EzAAI [56].”
MethodCarroll, L. M., Wiedmann, M., & Kovač, J. (2020) · mBio · doi:10.1128/mbio.00034-20 · Cited by 200 · see citing works
“which two genomes belong to the same genomospecies if they share an ANI value above a set threshold (usually 95 ANI) (5).”
Metabolome-microbiome signatures in the fermented beverage, Kombucha
Villarreal-Soto, S. A., Bouajila, J., Pace, M., Leech, J., Cotter, P. D., Souchard, J.-P., Taillandier, P., & Beaufort, S. (2020) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2020.108778 · Cited by 196 · see citing works
Thousands of previously unknown phages discovered in whole-community human gut metagenomes
Benler, S., Yutin, N., Antipov, D., Rayko, M., Shmakov, S., Gussow, A. B., Pevzner, P. A., & Koonin, E. V. (2021) · Microbiome · doi:10.1186/s40168-021-01017-w · Cited by 195 · see citing works
Pan, S., Zhu, C., Zhao, X., & Coelho, L. P. (2022) · Nature Communications · doi:10.1038/s41467-022-29843-y · Cited by 195 · see citing works
Beresford-Jones, B. S., Forster, S. C., Stares, M. D., Notley, G., Viciani, E., Browne, H. P., Boehmler, D. J., Soderholm, A. T., Kumar, N., Vervier, K., et al. (2022) · Cell Host & Microbe · doi:10.1016/j.chom.2021.12.003 · Cited by 194 · see citing works
“6 alignment fraction were considered the same species (Jain et al., 2018; Nielsen et al., 2014; Varghese et al., 2015).”
BackgroundInfluentialHitch, T. C. A., Riedel, T., Oren, A., Overmann, J., Lawley, T. D., & Clavel, T. (2021) · ISME Communications · doi:10.1038/s43705-021-00017-z · Cited by 193 · see citing works
Nogal, A., Louca, P., Zhang, X., Wells, P. M., Steves, C. J., Spector, T. D., Falchi, M., Valdes, A. M., & Menni, C. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.711359 · Cited by 187 · see citing works
“FastANI version 1.32 (Jain et al., 2018) was separately run on Lachnoclostridium and Coprococcus genomes to calculate the average nucleotide identity (ANI) between all pairs of sequences (Supplementary Tables 2, 3, 4).”
MethodInfluentialDeepMicrobes: taxonomic classification for metagenomics with deep learning
Liang, Q., Bible, P. W., Liu, Y., Zou, B., & Wei, L. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa009 · Cited by 189 · see citing works
Verrucomicrobiota are specialist consumers of sulfated methyl pentoses during diatom blooms
Orellana, L. H., Francis, B., Ferraro, M., Hehemann, J., Fuchs, B. M., & Amann, R. (2021) · The ISME Journal · doi:10.1038/s41396-021-01105-7 · Cited by 189 · see citing works
A Comprehensive Analysis of the Global Human Gut Archaeome from a Thousand Genome Catalogue
Chibani, C. M., Mahnert, A., Borrel, G., Almeida, A., Werner, A., Brugère, J., Gribaldo, S., Finn, R. D., Schmitz, R. A., & Moissl‐Eichinger, C. (2020) · Nature Microbiology · doi:10.1038/s41564-021-01020-9 · Cited by 183 · see citing works
Metagenomics: An Effective Approach for Exploring Microbial Diversity and Functions
Nam, N. N., Do, H. D. K., Trinh, K. T. L., & Lee, N. Y. (2023) · Foods · doi:10.3390/foods12112140 · Cited by 203 · see citing works
Revision of the ‘Candidatus Phytoplasma’ species description guidelines
Bertaccini, A., Rosete, Y. A., Contaldo, N., Duduk, B., Fiore, N., Montano, H. G., Kube, M., Kuo, C., Martini, M., Oshima, K., et al. (2022) · INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY · doi:10.1099/ijsem.0.005353 · Cited by 180 · see citing works
“Phytoplasma’ species will be based on a threshold of 95 % genome-wide ANI as suggested for bacteria [12, 13].”
Cotto, I., Dai, Z., Huo, L., Anderson, C. L., Vilardi, K. J., Ijaz, U. Z., Khunjar, W., Wilson, C., Clippeleir, H. D., Gilmore, K. R., et al. (2019) · Water Research · doi:10.1016/j.watres.2019.115268 · Cited by 169 · see citing works
Robertson, J. A., Bessonov, K., Schonfeld, J., & Nash, J. H. E. (2020) · Microbial Genomics · doi:10.1099/mgen.0.000435 · Cited by 169 · see citing works
Karcher, N., Nigro, E., Punčochář, M., Blanco-Míguez, A., Ciciani, M., Manghi, P., Zolfo, M., Cumbo, F., Manara, S., Golzato, D., et al. (2021) · Genome Biology · doi:10.1186/s13059-021-02427-7 · Cited by 159 · see citing works
Spasov, E., Tsuji, J. M., Hug, L. A., Doxey, A. C., Sauder, L. A., Parker, W. J., & Neufeld, J. D. (2020) · The ISME Journal · doi:10.1038/s41396-020-0650-2 · Cited by 168 · see citing works
Hidden diversity of soil giant viruses
Schulz, F., Alteio, L. V., Goudeau, D., Ryan, E. M., Yu, F. B., Malmstrom, R. R., Blanchard, J. L., & Woyke, T. (2018) · Nature Communications · doi:10.1038/s41467-018-07335-2 · Cited by 166 · see citing works
Zhu, C., Wu, L., Ning, D., Tian, R., Gao, S.-H., Zhang, B., Zhao, J., Zhang, Y., Xiao, N., Wang, Y., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59019-3 · Cited by 166 · see citing works
“Since our recovered MAGs were dereplicated at an average nucleotide identity of 95%, they represented species-level genome bins 89,90 .”
A fast adaptive algorithm for computing whole-genome homology maps
Jain, C., Koren, S., Dilthey, A., Phillippy, A. M., & Aluru, S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty597 · Cited by 166 · see citing works
Self-citationProtective role of the Arabidopsis leaf microbiota against a bacterial pathogen
Vogel, C. M., Potthoff, D. B., Schäfer, M., Barandun, N., & Vorholt, J. A. (2021) · Nature Microbiology · doi:10.1038/s41564-021-00997-7 · Cited by 164 · see citing works
“Average nucleotide identities were calculated using FastANI 73 for members of the same family/order (Supplementary Table 13).”
InfluentialWu, X., Peng, J., Liu, P., Bei, Q., Rensing, C., Li, Y., Yuan, H., Liesack, W., Zhang, F. S., & Cui, Z. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.147329 · Cited by 163 · see citing works
A gut microbial signature for combination immune checkpoint blockade across cancer types
Gunjur, A., Shao, Y., Rozday, T., Klein, O., Mu, A., Haak, B. W., Markman, B., Kee, D., Carlino, M. S., Underhill, C., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02823-z · Cited by 165 · see citing works
“For the tree of Faecalibacterium genomes, pairwise whole-genome ANI distances were computed using FastANI 82 (many-to-many mode), which was converted into a distance matrix and then to a Newick-format tree using rapidNJ (v2.”
Dynamics of rice microbiomes reveal core vertically transmitted seed endophytes
Zhang, X., Ma, Y., Wang, X., Liao, K., He, S., Zhao, X., Guo, H., Zhao, D., & Wei, H. (2022) · Microbiome · doi:10.1186/s40168-022-01422-9 · Cited by 157 · see citing works
Insight into the function and evolution of the Wood–Ljungdahl pathway in Actinobacteria
Jiao, J., Fu, L., Hua, Z., Liu, L., Salam, N., Liu, P., Lv, A.-P., Wu, G., Xian, W.-D., Zhu, Q., et al. (2021) · The ISME Journal · doi:10.1038/s41396-021-00935-9 · Cited by 155 · see citing works
Newly Explored Faecalibacterium Diversity Is Connected to Age, Lifestyle, Geography, and Disease
De Filippis, F., Pasolli, E., & Ercolini, D. (2020) · Current Biology · doi:10.1016/j.cub.2020.09.063 · Cited by 161 · see citing works
“The twelve clades exhibited a median intra-clade distance ranging from 1.6% (clade L) to 3.6% (clade B), always below 5%, which is usually considered the cutoff for defining different species (Figure 1C; Data S2) [27].”
BackgroundInfluentialDelmont, T. O., Kiefl, E., Kilinc, O., Esen, Ö. C., Uysal, I., Rappé, M. S., Giovannoni, S. J., & Eren, A. M. (2019) · eLife · doi:10.7554/elife.46497 · Cited by 137 · see citing works
“…short reads (ANIr) recruited from environmental metagenomes using reference genomes, show an interesting discontinuity among sequence-discrete populations at sequence identity levels between 80% and 90–95% (Konstantinidis and DeLong, 2008; Caro-Quintero and Konstantinidis, 2012; Jain et al., 2018).”
Background
- 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
- Venue
- Nature Communications
- Published
- 2018-11-30
- Volume
- 9
- Issue
- 1
- Pages
- 5114-5114
- ISSN
- 2041-1723 (ISSN-L)
- 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 · 2014: 1
- FWCI
- 96.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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