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FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments
Open accessAbstract
BACKGROUND: We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. METHODOLOGY/PRINCIPAL FINDINGS: Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the "CAT" approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100-1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. CONCLUSIONS/SIGNIFICANCE: FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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![Figure 1 Local support values for splits found by PhyML with SPR moves and/or FastTree. We examined local support values for the splits inferred by PhyML 3.0 with + SPRs on simulated alignments with 250 protein sequences. We classified PhyML's splits as correct and found by both PhyML and FastTree, correct but missed by FastTree, or incorrect. We show the distribution of support values for each class. The right-most bin includes the strongly supported splits (0.95 to 1.0), and the gray dashed line shows the uniform distribution. The support values are PhyML's minimum of the approximate likelihood ratio test [21] and SH-like [16], [17] local supports.](/paper-figures/8d86db71595b4c3189b248ede78bcc3f/fasttree-2-approximately-maximum-likelihood-trees-for-large-alignments-figure-1.jpg)
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“Thus, the validation process for our methods is centered around the comparison of phylogenetic trees estimated from the alignment with FastTree 2.1 (ref. 34).”
From Improving microbial phylogeny with citizen science within a mass-market video game (2025) · cites this paper as [34]
In the citing paper
“On the other hand, FastTree was found to be much faster than RAxML and PhyML but reported lower likelihood scores for data sets with both small and large numbers of sequences (Guindon et al. 2010; Price et al. 2010; Liu et al. 2011).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Price et al. 2010
In the citing paper
“So far, these four programs have only been evaluated using simulated data (Guindon et al. 2010; Price et al. 2010; Liu et al. 2011), and empirical data sets with wide ranges of taxon numbers (e.g., up to 237,882 taxa in Price et al. [2010]) but relatively small numbers of genes (from ∼10 [Price et al. 2010; Liu et al. 2011; Chernomor et al. 2016] to ∼200 [Guindon et al. 2010; Money and Whelan 2012”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Price et al. 2010
In the citing paper
“As a tradeoff, FastTree generates less accurate tree estimates than SPR-based ML methods (Price et al. 2010).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Price et al. 2010
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“For example, the recently developed FastTree program can be orders of magnitude faster than either PhyML or RAxML/ExaML (Price et al. 2010).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Price et al. 2010
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Cites
The neighbor-joining method: a new method for reconstructing phylogenetic trees.
Saitou, N., & Nei, M. (1987) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a040454 · Cited by 59,959
CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP
Felsenstein, J. (1985) · Evolution · doi:10.1111/j.1558-5646.1985.tb00420.x · Cited by 44,735
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649
Guindon, S., Dufayard, J.-F., Lefort, V., Anisimova, M., Hordijk, W., & Gascuel, O. (2010) · Systematic Biology · doi:10.1093/sysbio/syq010 · Cited by 18,376
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
Guindon, S., & Gascuel, O. (2003) · Systematic Biology · doi:10.1080/10635150390235520 · Cited by 16,923
RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models
Stamatakis, A. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl446 · Cited by 15,582
Evolutionary trees from DNA sequences: A maximum likelihood approach.
Felsenstein, J. (1981) · Journal of Molecular Evolution · doi:10.1007/bf01734359 · Cited by 14,571
Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., Huber, T., Dalevi, D., Hu, P., & Andersen, G. L. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.03006-05 · Cited by 10,891
A Rapid Bootstrap Algorithm for the RAxML Web Servers
Stamatakis, A., Hoover, P., & Rougemont, J. (2008) · Systematic Biology · doi:10.1080/10635150802429642 · Cited by 7,028
FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix
Price, M. N., Dehal, P. S., & Arkin, A. P. (2009) · Molecular Biology and Evolution · doi:10.1093/molbev/msp077 · Cited by 5,602
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Brady, D. J., Grapputo, A., Romoli, O., & Sandrelli, F. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20235862 · Cited by 213
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Barrio, Á. M., Lamichhaney, S., Fan, G., Rafati, N., Pettersson, M. E., Zhang, H., Dainat, J., Ekman, D. S., Höppner, M. P., Jern, P., et al. (2016) · eLife · doi:10.7554/elife.12081 · Cited by 212
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Benoit, G., Raguideau, S., James, R. S., Phillippy, A. M., Chikhi, R., & Quince, C. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01983-6 · Cited by 212
Roux, S., Krupovìč, M., Poulet, A., Debroas, D., & Enault, F. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040418 · Cited by 211
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Whiteley, A. S., Jenkins, S., Waite, I. S., Kresoje, N., Payne, H. G., Mullan, B. P., Allcock, R. J. N., & O’Donnell, A. G. (2012) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2012.07.008 · Cited by 211
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Martis, M. M., Zhou, R., Haseneyer, G., Schmutzer, T., Vrána, J., Kubaláková, M., König, S., Kugler, K., Scholz, U., Hackauf, B., et al. (2013) · The Plant Cell · doi:10.1105/tpc.113.114553 · Cited by 211
Wang, Y., Ames, N. P., Tun, H. M., Tosh, S. M., Jones, P. J. H., & Khafipour, E. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00129 · Cited by 211
Long, S. W., Linson, S. E., Saavedra, M. O., Cantu, C. C., Davis, J. J., Brettin, T. S., & Olsen, R. J. (2017) · mSphere · doi:10.1128/mspheredirect.00290-17 · Cited by 211
Khaledi, A., Weimann, A., Schniederjans, M., Asgari, E., Kuo, T., Oliver, A., Cabot, G., Kola, A., Gastmeier, P., Hogardt, M., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.201910264 · Cited by 211
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Miller, C. S., Handley, K. M., Wrighton, K. C., Frischkorn, K. R., Thomas, B. C., & Banfield, J. F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056018 · Cited by 210
Kappelmann, L., Krüger, K., Hehemann, J., Harder, J., Markert, S., Unfried, F., Becher, D., Shapiro, N., Schweder, T., Amann, R., et al. (2018) · The ISME Journal · doi:10.1038/s41396-018-0242-6 · Cited by 210
Kokou, F., Sasson, G., Friedman, J., Eyal, S., Ovadia, O., Harpaz, S., Cnaani, A., & Mizrahi, I. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0560-0 · Cited by 210
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Holt, K. E., McAdam, P. R., Thai, P. V. K., Thương, N. T. T., Hà, Đ. T. M., Lan, N. N., Lân, N. H., Nhu, N. T. Q., Hai, H. T., Ha, V. T. N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0117-9 · Cited by 209
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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 209
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Cg, K., Nicolitch, O., Griffiths, R. I., Goodall, T., Jones, B., Weser, C., Langridge, H., Davison, J., Dellavalle, A., Eisenhauer, N., et al. (2024) · Nature · doi:10.1038/s41586-024-08185-3 · Cited by 209
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Zhang, H., Zou, C., Peng, O., Ashraf, U., Xu, Q., Gong, L., Fan, B., Zhang, Y., Xu, Z., Xue, C., et al. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad052 · Cited by 208
RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation
Liu, K., Linder, C. R., & Warnow, T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027731 · Cited by 207
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Wilkins, D. E., Leung, M. H. Y., & Lee, P. K. H. (2017) · Microbiome · doi:10.1186/s40168-016-0209-7 · Cited by 207
Debroas, D., Domaizon, I., Humbert, J. F., Jardillier, L. E., Lepère, C., Oudart, A., & Taïb, N. (2017) · FEMS Microbiology Ecology · doi:10.1093/femsec/fix023 · Cited by 207
Pope, P. B., Mackenzie, A. K., Gregor, I., Smith, W. J. M., Sundset, M. A., McHardy, A. C., Morrison, M., & Eijsink, V. G. H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038571 · Cited by 206
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Guo, Z., Ma, P., Yang, G., Hu, J., Liu, Y.-L., Xia, E., Zhong, M., Zhao, L., Sun, G., Xu, Y., et al. (2019) · Molecular Plant · doi:10.1016/j.molp.2019.05.009 · Cited by 206
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Bhattacharya, D., Agrawal, S., Aranda, M., Baumgarten, S., Belcaid, M., Drake, J. L., Erwin, D. H., Foret, S., Gates, R. D., Gruber, D. F., et al. (2016) · eLife · doi:10.7554/elife.13288 · Cited by 204
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Belleghem, S. M. V., Rastas, P. M. A., Papanicolaou, A., Martin, S. H., Arias, C. F., Supple, M. A., Hanly, J. J., Mallet, J., Lewis, J. J., Hines, H. M., et al. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-016-0052 · Cited by 204
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Hu, W., Hou, Q., Delgado‐Baquerizo, M., Stegen, J., Du, Q., Dong, L., Ji, M., Sun, Y., Yao, S., Gong, H., et al. (2022) · Environmental Microbiology · doi:10.1111/1462-2920.16099 · Cited by 204
Li, R., Tun, H. M., Jahan, M., Zhang, Z., Kumar, A., Dilantha Fernando, W. G., Farenhorst, A., & Khafipour, E. (2017) · Scientific Reports · doi:10.1038/s41598-017-02516-3 · Cited by 203
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Mende, D. R., Bryant, J. A., Aylward, F. O., Eppley, J. M., Nielsen, T., Karl, D. M., & DeLong, E. F. (2017) · Nature Microbiology · doi:10.1038/s41564-017-0008-3 · Cited by 203
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Ma, B., Lu, C., Wang, Y., Yu, J., Zhao, K., Xue, R., Ren, H., Lv, X., Pan, R., Zhang, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43000-z · Cited by 203
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Kreisinger, J., Bastien, G., Hauffe, H. C., Marchesi, J. R., & Perkins, S. E. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0295 · Cited by 202
Warren, R. L., Keeling, C. I., Yuen, M. M. S., Raymond, A., Taylor, G. A., Vandervalk, B. P., Mohamadi, H., Paulino, D., Chiu, R., Jackman, S. D., et al. (2015) · The Plant Journal · doi:10.1111/tpj.12886 · Cited by 202
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Young, J. C., Chehoud, C., Bittinger, K., Bailey, A., Diamond, J. M., Cantu, E., Haas, A. R., Abbas, A., Frye, L., Christie, J. D., et al. (2015) · American Journal of Transplantation · doi:10.1111/ajt.13031 · Cited by 201
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González, M. E., Schaffer, J. V., Orlow, S. J., Gao, Z., Li, H., Alekseyenko, A. V., & Blaser, M. J. (2016) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2016.04.066 · Cited by 201
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Spang, A., Stairs, C. W., Dombrowski, N., Eme, L., Lombard, J., Caceres, E. F., Greening, C., Baker, B. J., & Ettema, T. J. G. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0406-9 · Cited by 200
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Yang, Y. J., Moore, M. J., Brockington, S. F., Soltis, P. S., Wong, G. K., Carpenter, E. J., Zhang, Y., Chen, L., Yan, Z., Xie, Y., et al. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv081 · Cited by 200
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Kowarsky, M., Camuñas-Soler, J., Kertesz, M., Vlaminck, I. D., Koh, W. L. C., Pan, W., Martin, L., Neff, N. F., Okamoto, J., Wong, R. J., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707009114 · Cited by 197
Iyer, L. M., Zhang, D., Rogozin, I. B., & Aravind, L. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr691 · Cited by 196
Bowman, J. S., & Ducklow, H. W. (2015) · PLoS ONE · doi:10.1371/journal.pone.0135868 · Cited by 196
Earl, J. P., Adappa, N. D., Król, J. E., Bhat, A. S., Balashov, S. V., Ehrlich, R. L., Palmer, J. N., Workman, A. D., Blasetti, M. T., Sen, B., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0569-2 · Cited by 196
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Landscape of mobile genetic elements and their antibiotic resistance cargo in prokaryotic genomes
Khedkar, S., Smyshlyaev, G., Letunić, I., Maistrenko, O. M., Coelho, L. P., Orakov, A. N., Forslund, S. K., Hildebrand, F., Lütge, M., Schmidt, T., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac163 · Cited by 195
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
Environment or kin: whence do bees obtain acidophilic bacteria?
McFrederick, Q. S., Wcislo, W. T., TAYLOR, D. R., Ishak, H. D., Dowd, S. E., & Mueller, U. G. (2012) · Molecular Ecology · doi:10.1111/j.1365-294x.2012.05496.x · Cited by 194
Rebollar, E. A., Hughey, M. C., Medina, D., Harris, R. N., Ibáñez, R., & Belden, L. K. (2016) · The ISME Journal · doi:10.1038/ismej.2015.234 · Cited by 194
Genome expansion and lineage-specific genetic innovations in the forest pathogenic fungi Armillaria
Sipos, G., Prasanna, A. N., Walter, M. C., O’Connor, E., Bálint, B., Krizsán, K., Kiss, B., Hess, J., Varga, T., Slot, J. C., et al. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-017-0347-8 · Cited by 194
Zhang, L., Su, W., Tao, R., Zhang, W., Chen, J., Wu, P., Yan, C., Jia, Y., Larkin, R. M., Lavelle, D. O., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-02445-9 · Cited by 194
Porritt, R. A., Paschold, L., Rivas, M. N., Cheng, M. H., Yonker, L. M., Chandnani, H. K., Lopez, M., Simnica, D., Schultheiß, C., Santiskulvong, C., et al. (2021) · Journal of Clinical Investigation · doi:10.1172/jci146614 · Cited by 194
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 194
Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence
Kraemer, M. U. G., Hill, V., Ruis, C., Dellicour, S., Bajaj, S., McCrone, J. T., Baele, G., Parag, K. V., Battle, A. L., Gutiérrez, B., et al. (2021) · Science · doi:10.1126/science.abj0113 · Cited by 194
Key roles for freshwater A ctinobacteria revealed by deep metagenomic sequencing
Ghai, R., Mizuno, C. M., Picazo, A., Camacho, A., & Rodríguez‐Valera, F. (2014) · Molecular Ecology · doi:10.1111/mec.12985 · Cited by 193
Chen, Q., Li, H., Zhou, X., Zhao, Y., Su, J., Zhang, X., & Huang, F.-Y. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.07.164 · Cited by 193
Large-scale gene losses underlie the genome evolution of parasitic plant Cuscuta australis
Sun, G., Xu, Y., Liu, H., Sun, T., Zhang, J., Hettenhausen, C., Shen, G., Qi, J., Qin, Y., Li, J., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04721-8 · Cited by 193
Molecular epidemiology of carbapenem-resistant hypervirulent Klebsiella pneumoniae in China
Yang, X., Sun, Q., Li, J., Yu, J., Li, Y., Lin, J., Chen, K., Chan, E. W., Zhang, R., & Chen, S. (2022) · Emerging Microbes & Infections · doi:10.1080/22221751.2022.2049458 · Cited by 193
Dynamic root microbiome sustains soybean productivity under unbalanced fertilization
Wang, M., Ge, A., Ma, X., Wang, X., Xie, Q., Wang, L., Song, X., Jiang, M., Yang, W., Murray, J. D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45925-5 · Cited by 193
Gut microbiome composition is associated with temperament during early childhood
Christian, L. M., Galley, J. D., Hade, E. M., Schoppe-Sullivan, S., Kamp Dush, C., & Bailey, M. T. (2015) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2014.10.018 · Cited by 191
Wu, D., Jospin, G., & Eisen, J. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077033 · Cited by 191
DESMAN: a new tool for de novo extraction of strains from metagenomes
Quince, C., Connelly, S., Raguideau, S., Alneberg, J., Shin, S. G., Collins, G., & Eren, A. M. (2017) · Genome biology · doi:10.1186/s13059-017-1309-9 · Cited by 191
Vrieze, J. D., Pinto, A. J., Sloan, W. T., & Ijaz, U. Z. (2018) · Microbiome · doi:10.1186/s40168-018-0449-9 · Cited by 191
Gálvez, E. J. C., Iljazović, A., Amend, L., Lesker, T. R., Renault, T. T., Thiemann, S., Hao, L., Roy, U., Gronow, A., Charpentier, E., et al. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.09.012 · Cited by 191
Determining the culturability of the rumen bacterial microbiome
Creevey, C. J., Kelly, W. J., Henderson, G., & Leahy, S. C. (2014) · Microbial Biotechnology · doi:10.1111/1751-7915.12141 · Cited by 190
Boyer, M., Madoui, M., Gimenez, G., Scola, B. L., & Raoult, D. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015530 · Cited by 190
The orange carotenoid protein in photoprotection of photosystem II in cyanobacteria
Kirilovsky, D. L., & Kerfeld, C. A. (2011) · Biochimica et Biophysica Acta (BBA) - Bioenergetics · doi:10.1016/j.bbabio.2011.04.013 · Cited by 190
Brockington, S. F., Yang, Y. J., Gandía‐Herrero, F., Covshoff, S., Hibberd, J. M., Sage, R. F., Wong, G. K., Moore, M. J., & Smith, S. A. (2015) · New Phytologist · doi:10.1111/nph.13441 · Cited by 190
Rausch, P., Basic, M., Batra, A., Bischoff, S. C., Blaut, M., Clavel, T., Gläsner, J., Gopalakrishnan, S., Graßl, G. A., Günther, C., et al. (2016) · International Journal of Medical Microbiology · doi:10.1016/j.ijmm.2016.03.004 · Cited by 190
Disease-associated genotypes of the commensal skin bacterium Staphylococcus epidermidis
Méric, G., Mageiros, L., Pensar, J., Laabei, M., Yahara, K., Pascoe, B., Kittiwan, N., Tadee, P., Post, V., Lamble, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07368-7 · Cited by 190
The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance
Tegally, H., San, J. E., Cotten, M. L., Moir, M., Tegomoh, B., Mboowa, G., Martin, D. P., Baxter, C., Lambisia, A. W., Diallo, A. B., et al. (2022) · Science · doi:10.1126/science.abq5358 · Cited by 190
Munyaka, P. M., Eissa, N., Bernstein, C. N., Khafipour, E., & Ghia, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142536 · Cited by 189
Global and regional dissemination and evolution of Burkholderia pseudomallei
Chewapreecha, C., Holden, M. T. G., Vehkala, M., Välimäki, N., Yang, Z., Harris, S. R., Mather, A. E., Tuanyok, A., Smet, B. D., Hello, S. L., et al. (2017) · Nature Microbiology · doi:10.1038/nmicrobiol.2016.263 · Cited by 189
Flórez, L. V., Scherlach, K., Gaube, P., Ross, C., Sitte, E., Hermes, C., Rodrigues, A. L., Hertweck, C., & Kaltenpoth, M. (2017) · Nature Communications · doi:10.1038/ncomms15172 · Cited by 189
Krüger, K., Chafee, M. E., Francis, B., del Rio, T. G., Becher, D., Schweder, T., Amann, R., & Teeling, H. (2019) · The ISME Journal · doi:10.1038/s41396-019-0476-y · Cited by 189
Zhang, L., Delgado‐Baquerizo, M., Shi, Y., Liu, X., Yang, Y., & Chu, H. (2021) · Water Research · doi:10.1016/j.watres.2021.117139 · Cited by 189
Metadata
- Authors
- Price, M. N., Dehal, P. S., & Arkin, A. P.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Price, Morgan N.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-03-10
- Volume
- 5
- Issue
- 3
- Pages
- e9490
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC2835736
- OpenAlex
- W2031611770
- Cited by
- 14,894
- References
- 37
- Citations by year
- 2026: 1298 · 2025: 1543 · 2024: 1657 · 2023: 1486 · 2022: 1676 · 2021: 1651 · 2020: 1363 · 2019: 1013 · 2018: 776 · 2017: 686 · 2016: 521 · 2015: 415 · 2014: 309 · 2013: 256 · 2012: 133
- FWCI
- 63.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Funders
- U.S. Department of Energy (-AC02-05CH11231, 05CH11231, DE AC02 05CH11231, DE-AC02-)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- phylogenetic inference, maximum likelihood phylogeny, nearest neighbor interchange, cat model, 16S rRNA, phylogenetic accuracy, scalability
- MeSH terms
- Algorithms, Animals, Computers, Data Interpretation, Statistical, Genetic Techniques, Humans, Models, Genetic, Phylogeny, RNA, Ribosomal, 16S, Software, Likelihood Functions, Sequence Alignment, Databases, Protein
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