The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Strengths and Limitations of 16S rRNA Gene Amplicon Sequencing in Revealing Temporal Microbial Community Dynamics
Open accessAbstract
This study explored the short-term planktonic microbial community structure and resilience in Lake Lanier (GA, USA) while simultaneously evaluating the technical aspects of identifying taxa via 16S rRNA gene amplicon and metagenomic sequence data. 16S rRNA gene amplicons generated from four temporally discrete samples were sequenced with 454 GS-FLX-Ti yielding ∼40,000 rRNA gene sequences from each sample and representing ∼300 observed OTUs. Replicates obtained from the same biological sample clustered together but several biases were observed, linked to either the PCR or sequencing-preparation steps. In comparisons with companion whole-community shotgun metagenome datasets, the estimated number of OTUs at each timepoint was concordant, but 1.5 times and ∼10 times as many phyla and genera, respectively, were identified in the metagenomes. Our analyses showed that the 16S rRNA gene captures broad shifts in community diversity over time, but with limited resolution and lower sensitivity compared to metagenomic data. We also identified OTUs that showed marked shifts in abundance over four close timepoints separated by perturbations and tracked these taxa in the metagenome vs. 16S rRNA amplicon data. A strong summer storm had less of an effect on community composition than did seasonal mixing, which revealed a distinct succession of organisms. This study provides insights into freshwater microbial communities and advances the approaches for assessing community diversity and dynamics in situ.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
![Figure 2 Community composition shifts over time as revealed by 16S data. Taxonomic binning of 16S amplicon sequences for each of the 14 individual datasets at the phylum (top) and genus (middle) levels were based on the July 2011 version of the Greengenes database [31]. Freshwater lineages (bottom) were based on a freshwater database according to the taxonomy framework described in Newton et al., 2011. Datasets are ordered left to right by date, technical sequencing replicate (lane 1 and lane 2), and DNA replicate (A, B and C). Taxa identified as major drivers of the differences between timepoints (SIMPER analysis) are labeled (see figure key).](/paper-figures/95d482e4b0544c11b79cf60811f904c2/strengths-and-limitations-of-16s-rrna-gene-amplicon-sequencing-in-revealing-figure-2.jpg)
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Such hypervariable genes or regions permit identification of microbial community members but can be limited in their capacity to resolve phylogenetically related species or strains and do not provide information on functional gene content of the microbial members present.72”
From The oral microbiome: Role of key organisms and complex networks in oral health and disease (2021) · cites this paper as [72]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007) · Applied and Environmental Microbiology · doi:10.1128/aem.00062-07 · Cited by 20,145
Non‐parametric multivariate analyses of changes in community structure
Clarke, K. R. (1993) · Australian Journal of Ecology · doi:10.1111/j.1442-9993.1993.tb00438.x · Cited by 14,449
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
Velvet: Algorithms for de novo short read assembly using de Bruijn graphsPreprint
Zerbino, D. R., & Birney, E. (2008) · Genome Research · doi:10.1101/gr.074492.107 · Cited by 9,587
Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., & Glöckner, F. O. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks808 · Cited by 8,850
Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms
Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Huntley, J., Fierer, N., Owens, S. M., Betley, J., Fraser, L., Bauer, M., et al. (2012) · The ISME Journal · doi:10.1038/ismej.2012.8 · Cited by 8,839
Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing
Bokulich, N. A., Subramanian, S., Faith, J. J., Gevers, D., Gordon, J. I., Knight, R. D., Mills, D. A., & Caporaso, J. G. (2012) · Nature Methods · doi:10.1038/nmeth.2276 · Cited by 4,525
Microbial diversity in the deep sea and the underexplored “rare biosphere”
Sogin, M. L., Morrison, H. G., Huber, J. A., Welch, D. B. M., Huse, S. M., Neal, P. R., Arrieta, J. M., & Herndl, G. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605127103 · Cited by 3,803
Bacterial Community Variation in Human Body Habitats Across Space and Time
Costello, E. K., Lauber, C. L., Hamady, M., Fierer, N., Gordon, J. I., & Knight, R. (2009) · Science · doi:10.1126/science.1177486 · Cited by 3,231
Cited by
Sorted by citations
Cryan, J. F., O'Riordan, K. J., Cowan, C. S. M., Sandhu, K. V., Bastiaanssen, T. F. S., Boehme, M., Codagnone, M. G., Cussotto, S., Fulling, C., Golubeva, A. V., et al. (2019) · Physiological Reviews · doi:10.1152/physrev.00018.2018 · Cited by 5,286
Introduction to the human gut microbiota
Thursby, E., & Juge, N. (2017) · Biochemical Journal · doi:10.1042/bcj20160510 · Cited by 3,600
Rinninella, E., Raoul, P., Cintoni, M., Franceschi, F., Miggiano, G. A. D., Gasbarrini, A., & Mele, M. C. (2019) · Microorganisms · doi:10.3390/microorganisms7010014 · Cited by 3,462
Gut microbiota and human NAFLD: disentangling microbial signatures from metabolic disorders
Aron‐Wisnewsky, J., Vigliotti, C., Witjes, J. J., Le, P. T., Holleboom, A. G., Verheij, J., Nieuwdorp, M., & Clément, K. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0269-9 · Cited by 1,132
Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing
Ranjan, R., Rani, A., Metwally, A., McGee, H. S., & Perkins, D. L. (2016) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2015.12.083 · Cited by 981
The oral microbiome: Role of key organisms and complex networks in oral health and disease
Sedghi, L., DiMassa, V., Harrington, A., Lynch, S. V., & Kapila, Y. L. (2021) · Periodontology 2000 · doi:10.1111/prd.12393 · Cited by 755
The role of the gut microbiome in systemic inflammatory disease
Clemente, J. C., Manasson, J., & Scher, J. U. (2018) · BMJ · doi:10.1136/bmj.j5145 · Cited by 587
McGuinness, A. J., Davis, J. A., Dawson, S. L., Loughman, A., Collier, F., O’Hely, M., Simpson, C. A., Green, J., Marx, W., Hair, C., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01456-3 · Cited by 502
Next-generation sequencing: insights to advance clinical investigations of the microbiome
Wensel, C. R., Pluznick, J. L., Salzberg, S. L., & Sears, C. L. (2022) · Journal of Clinical Investigation · doi:10.1172/jci154944 · Cited by 437
The Gastrointestinal Microbiome: Alcohol Effects on the Composition of Intestinal Microbiota
Engen, P. A. (2015) · Alcohol research · doi:10.35946/arcr.v37.2.07 · Cited by 451
The progress of gut microbiome research related to brain disorders
Zhu, S., Jiang, Y., Xu, K., Cui, M., Ye, W., Zhao, G., Jin, L., & Chen, X. (2020) · Journal of Neuroinflammation · doi:10.1186/s12974-020-1705-z · Cited by 419
Mohr, A. E., Jäger, R., Carpenter, K. C., Kerksick, C. M., Purpura, M., Townsend, J. R., West, N. P., Black, K., Gleeson, M., Pyne, D. B., et al. (2020) · Journal of the International Society of Sports Nutrition · doi:10.1186/s12970-020-00353-w · Cited by 378
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics
Escobar‐Zepeda, A., de León, A. V., & Sánchez‐Flores, A. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00348 · Cited by 397
A predictive index for health status using species-level gut microbiome profiling
Gupta, V. K., Kim, M., Bakshi, U., Cunningham, K. Y., Davis, J. M., Lazaridis, K. N., Nelson, H., Chia, N., & Sung, J. (2020) · Nature Communications · doi:10.1038/s41467-020-18476-8 · Cited by 338
Compositional data analysis of the microbiome: fundamentals, tools, and challenges
Tsilimigras, M. C. B., & Fodor, A. A. (2016) · Annals of Epidemiology · doi:10.1016/j.annepidem.2016.03.002 · Cited by 364
Org, E., Blum, Y., Kasela, S., Mehrabian, M., Kuusisto, J., Kangas, A. J., Soininen, P., Wang, Z., Ala‐Korpela, M., Hazen, S. L., et al. (2017) · Genome biology · doi:10.1186/s13059-017-1194-2 · Cited by 337
Salipante, S. J., Kawashima, T. I., Rosenthal, C. A., Hoogestraat, D. R., Cummings, L. A., SenGupta, D. J., Harkins, T. T., Cookson, B. T., & Hoffman, N. G. (2014) · Applied and Environmental Microbiology · doi:10.1128/aem.02206-14 · Cited by 318
Hawkins, J., de Vere, N., Griffith, A., Ford, C. R., Allainguillaume, J., Hegarty, M., Baillie, L. W. J., & Adams‐Groom, B. (2015) · PLoS ONE · doi:10.1371/journal.pone.0134735 · Cited by 307
Relationship between gut microbiota and circulating metabolites in population-based cohorts
Vojinović, D., Radjabzadeh, D., Kurilshikov, A., Amin, N., Wijmenga, C., Franke, L., Ikram, M. A., Uitterlinden, A. G., Zhernakova, A., Fu, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13721-1 · Cited by 272
Tabatabaei, M., Aghbashlo, M., Valijanian, E., Panahi, H. K. S., Nizami, A., Ghanavati, H., Sulaiman, A., Mirmohamadsadeghi, S., & Karimi, K. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.07.037 · Cited by 278
Tessler, M., Neumann, J. S., Afshinnekoo, E., Pineda, M., Hersch, R., Velho, L. F. M., Segóvia, B. T., Lansac‐Tôha, F. A., Lemke, M., DeSalle, R., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-06665-3 · Cited by 260
Paharik, A. E., Parlet, C. P., Chung, N., Todd, D. A., Rodriguez, E. I., Dyke, M. J. V., Cech, N. B., & Horswill, A. R. (2017) · Cell Host & Microbe · doi:10.1016/j.chom.2017.11.001 · Cited by 242
Analysis of soil microbial communities based on amplicon sequencing of marker genes
Schöler, A., Jacquiod, S., Vestergaard, G., Schulz, S., & Schloter, M. (2017) · Biology and Fertility of Soils · doi:10.1007/s00374-017-1205-1 · Cited by 237
Integrative Personal Omics Profiles during Periods of Weight Gain and Loss
Piening, B., Zhou, W., Contrepois, K., Röst, H. L., Gu, G. J., Mishra, T., Hanson, B., Bautista, E. J., Leopold, S. R., Yeh, C. Y., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2017.12.013 · Cited by 212
Reproducible protocols for metagenomic analysis of human faecal phageomes
Shkoporov, A. N., Ryan, F. J., Draper, L. A., Forde, A., Stockdale, S. R., Daly, K. M., McDonnell, S. A., Nolan, J. A., Sutton, T., Dalmasso, M., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0446-z · Cited by 203
Vrieze, J. D., Pinto, A. J., Sloan, W. T., & Ijaz, U. Z. (2018) · Microbiome · doi:10.1186/s40168-018-0449-9 · Cited by 191
Zakharkina, T., Martín‐Loeches, I., Matamoros, S., Póvoa, P. M. S. R., Torres, A., Kastelijn, J. B., Hofstra, J. J., de Wever, B., de Jong, M. D., Schultz, M. J., et al. (2017) · Thorax · doi:10.1136/thoraxjnl-2016-209158 · Cited by 189
Microbial community successional patterns in beach sands impacted by the Deepwater Horizon oil spill
Rodriguez‐R, L. M., Overholt, W. A., Hagan, C., Huettel, M., Kostka, J. E., & Konstantinidis, K. T. (2015) · The ISME Journal · doi:10.1038/ismej.2015.5 · Cited by 177
Self-citationKlammsteiner, T., Walter, A., Bogataj, T., Heussler, C. D., Stres, B., Steiner, F. M., Schlick-Steiner, B. C., Arthofer, W., & Insam, H. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.00993 · Cited by 186
Computational methods for 16S metabarcoding studies using Nanopore sequencing data
Santos, A., van Aerle, R., Barrientos, L., & Martínez-Urtaza, J. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.01.005 · Cited by 198
Endometriosis and the microbiome: a systematic review
Leonardi, M., Hicks, C., El‐Assaad, F., El-Omar, E. M., & Condous, G. S. (2019) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.15916 · Cited by 182
Griffin, J. S., & Wells, G. (2016) · The ISME Journal · doi:10.1038/ismej.2016.121 · Cited by 157
Tender, C. A. D., Debode, J., Vandecasteele, B., D’Hose, T., Cremelie, P., Haegeman, A., Ruttink, T., Dawyndt, P., & Maes, M. (2016) · Applied Soil Ecology · doi:10.1016/j.apsoil.2016.05.001 · Cited by 157
Hong, X., Chen, J., Liu, L., Wu, H. G., Tan, H.-Q., Xie, G., Xu, Q., Zou, H., Yu, W., Wang, L., et al. (2016) · Scientific Reports · doi:10.1038/srep26621 · Cited by 154
Vibrio –bivalve interactions in health and disease
Destoumieux‐Garzón, D., Canesi, L., Oyanedel, D., Travers, M., Charrière, G. M., Pruzzo, C., & Vezzulli, L. (2020) · Environmental Microbiology · doi:10.1111/1462-2920.15055 · Cited by 149
Gut microbiota from persons with attention-deficit/hyperactivity disorder affects the brain in mice
Tengeler, A. C., Dam, S. A., Wiesmann, M., Naaijen, J., van Bodegom, M., Belzer, C., Dederen, P. J. W. C., Verweij, V., Franke, B., Kozicz, T., et al. (2020) · Microbiome · doi:10.1186/s40168-020-00816-x · Cited by 153
Cait, A., Cardenas, E., Dimitriu, P. A., Amenyogbe, N., Dai, D. L., Cait, J., Sbihi, H., Stiemsma, L. T., Subbarao, P., Mandhane, P. J., et al. (2019) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2019.06.029 · Cited by 149
Machine learning approaches in microbiome research: challenges and best practices
Παπουτσόγλου, Γ., Tarazona, S., Lopes, M. B., Klammsteiner, T., Ibrahimi, E., Eckenberger, J., Novielli, P., Tonda, A. P., Mihajlović, A., Shigdel, R., et al. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1261889 · Cited by 153
Inhibition of anaerobic digestion processes: Applications of molecular tools
Amha, Y. M., Anwar, M. Z., Brower, A., Jacobsen, C. S., Stadler, L. B., Webster, T. M., & Smith, A. L. (2017) · Bioresource Technology · doi:10.1016/j.biortech.2017.08.210 · Cited by 148
Gupta, S., Mortensen, M. S., Schjørring, S., Trivedi, U., Vestergaard, G., Stokholm, J., Bisgaard, H. F., Krogfelt, K. A., & Sørensen, S. J. (2019) · Communications Biology · doi:10.1038/s42003-019-0540-1 · Cited by 149
Fecal Microbiota Analysis: An Overview of Sample Collection Methods and Sequencing Strategies
Thomas, V., Clark, J. R., & Dore, J. M. (2015) · Future Microbiology · doi:10.2217/fmb.15.87 · Cited by 138
Defining the relationship between vaginal and urinary microbiomes
Komesu, Y. M., Dinwiddie, D. L., Richter, H. E., Lukacz, E. S., Sung, V. W., Siddiqui, N. Y., Zyczynski, H. M., Ridgeway, B., Rogers, R. G., Arya, L. A., et al. (2019) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2019.08.011 · Cited by 128
Hou, Q., Kwok, L., Zheng, Y., Wang, L., Guo, Z., Zhang, J., Huang, W., Wang, Y., Leng, L., Li, H., et al. (2016) · Scientific Reports · doi:10.1038/srep37376 · Cited by 115
Yang, L., Haidar, G., Zia, H., Nettles, R., Qin, S., Wang, X., Shah, F. A., Rapport, S. F., Charalampous, T., Methé, B. A., et al. (2019) · Respiratory Research · doi:10.1186/s12931-019-1218-4 · Cited by 114
The house cricket (Acheta domesticus) as a novel food: a risk profile
Fernández-Cassi, X., Supeanu, A., Vaga, M., Jansson, A., Boqvist, S., & Vågsholm, I. (2019) · Journal of Insects as Food and Feed · doi:10.3920/jiff2018.0021 · Cited by 113
Stefanini, I., & Cavalieri, D. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00991 · Cited by 112
Barengolts, E., Green, S. J., Eisenberg, Y., Akbar, A., Reddivari, B., Layden, B. T., Dugas, L., & Chlipala, G. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194171 · Cited by 111
Debode, J., Tender, C. A. D., Soltaninejad, S., Malderghem, C. V., Haegeman, A., der Linden, I. V., Cottyn, B. G., Heyndrickx, M., & Maes, M. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00565 · Cited by 110
Salvetti, E., Orrù, L., Capozzi, V., Martina, A., Lamontanara, A., Keller, D., Cash, H. A., Felis, G. E., Cattivelli, L., Torriani, S., et al. (2016) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-016-7416-9 · Cited by 112
Chanda, D., & De, D. (2024) · Gut Microbes · doi:10.1080/19490976.2024.2304900 · Cited by 122
Uyaguari-Diaz, M. I., Chan, M., Chaban, B. L., Croxen, M. A., Finke, J. F., Hill, J. E., Peabody, M. A., Van Rossum, T., Suttle, C. A., Brinkman, F. S. L., et al. (2016) · Microbiome · doi:10.1186/s40168-016-0166-1 · Cited by 104
What Is the Role of Archaea in Plants? New Insights from the Vegetation of Alpine Bogs
Taffner, J., Erlacher, A., Bragina, A., Berg, C., Moissl‐Eichinger, C., & Berg, G. (2018) · mSphere · doi:10.1128/msphere.00122-18 · Cited by 106
Novel foods: a risk profile for the house cricket (Acheta domesticus)
Fernández-Cassi, X., Supeanu, A., Jansson, A., Boqvist, S., & Vågsholm, I. (2018) · EFSA Journal · doi:10.2903/j.efsa.2018.e16082 · Cited by 103
Auer, L., Lazuka, A., Sillam‐Dussès, D., Miambi, E., O’Donohue, M. J., & Hernandez‐Raquet, G. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02623 · Cited by 102
McCarthy, A., Chiang, E., Schmidt, M. L., & Denef, V. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121659 · Cited by 104
The Human Gut Microbiome as a Potential Factor in Autism Spectrum Disorder
Alharthi, A., Alhazmi, S., Alburae, N. A., & Bahieldin, A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23031363 · Cited by 126
Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine
Li, Y., Ma, L., Wu, D., & Chen, G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab024 · Cited by 111
A metagenomic assessment of microbial eukaryotic diversity in the global ocean
Obiol, A., Giner, C. R., Sánchez, P., Duarte, C. M., Acinas, S. G., & Massana, R. (2020) · Molecular Ecology Resources · doi:10.1111/1755-0998.13147 · Cited by 98
Barbosa‐Ribeiro, M., Arruda‐Vasconcelos, R., Louzada, L. M., dos Santos, D. G., Andreote, F. D., & Gomes, B. P. F. D. A. (2020) · Clinical Oral Investigations · doi:10.1007/s00784-020-03510-2 · Cited by 98
Linking Gut Microbiome and Lipid Metabolism: Moving beyond Associations
Lamichhane, S., Sen, P., Alves, M. A., Ribeiro, H. C., Raunioniemi, P., Hyötyläinen, T., & Orešič, M. (2021) · Metabolites · doi:10.3390/metabo11010055 · Cited by 101
Anaerobic Soil Disinfestation and Soilborne Pest Management
Rosskopf, E. N., Serrano-Pérez, P., Hong, J. C., Shrestha, U., Rodríguez‐Molina, M. D. C., Martin, K. J., Kokalis-Burelle, N., Shennan, C., Muramoto, J., & Butler, D. M. (2015) · Soil biology · doi:10.1007/978-3-319-23075-7_13 · Cited by 98
Rosselli, R., Romoli, O., Vitulo, N., Vezzi, A., Campanaro, S., Pascale, F. D., Schiavon, R., Tiarca, M., Poletto, F., Concheri, G., et al. (2016) · Scientific Reports · doi:10.1038/srep32165 · Cited by 102
Gut microbiota variations in patients diagnosed with major depressive disorder—A systematic review
Knudsen, J. K., Bundgaard‐Nielsen, C., Hjerrild, S., Nielsen, R. E., Leutscher, P., & Sørensen, S. (2021) · Brain and Behavior · doi:10.1002/brb3.2177 · Cited by 96
Beall, B., Twiss, M. R., Smith, D. E., Oyserman, B. O., Rozmarynowycz, M. J., Binding, C. E., Bourbonniere, R. A., Bullerjahn, G. S., Palmer, M. E., Reavie, E. D., et al. (2015) · Environmental Microbiology · doi:10.1111/1462-2920.12819 · Cited by 101
The gut microbiota as a mediator of metabolic benefits after bariatric surgery
Anhê, F. F., Varin, T. V., Schertzer, J. D., & Marette, A. (2017) · Canadian Journal of Diabetes · doi:10.1016/j.jcjd.2017.02.002 · Cited by 93
Holman, D. B., & Chénier, M. R. (2015) · Canadian Journal of Microbiology · doi:10.1139/cjm-2015-0239 · Cited by 97
DNA Metabarcoding for the Characterization of Terrestrial Microbiota—Pitfalls and Solutions
Francioli, D., Lentendu, G., Lewin, S. A., & Kolb, S. (2021) · Microorganisms · doi:10.3390/microorganisms9020361 · Cited by 107
Smith, K. F., Kohli, G. S., Murray, S. A., & Rhodes, L. L. (2017) · New Zealand Journal of Marine and Freshwater Research · doi:10.1080/00288330.2017.1298632 · Cited by 94
Evolutionary signal in the gut microbiomes of 74 bird species from Equatorial Guinea
Capunitan, D. C., Johnson, O. W., Terrill, R. S., & Hird, S. M. (2020) · Molecular Ecology · doi:10.1111/mec.15354 · Cited by 92
Emerging evidence of the role of gut microbiota in the development of allergic diseases
Sjödin, K. S., Vidman, L., Rydén, P., & West, C. E. (2016) · Current Opinion in Allergy and Clinical Immunology · doi:10.1097/aci.0000000000000277 · Cited by 91
Metagenome of a polluted river reveals a reservoir of metabolic and antibiotic resistance genes
Mittal, P., K., V., Dhakan, D. B., Kumar, S., & Sharma, V. K. (2019) · Environmental Microbiome · doi:10.1186/s40793-019-0345-3 · Cited by 92
Oral and Gut Dysbiosis Leads to Functional Alterations in Parkinson’s Disease
Jo, S., Kang, W., Hwang, Y. S., Lee, S. H., Park, K. W., Kim, M. S., Lee, H., Yoon, H. J., Park, Y. K., Chalita, M., et al. (2022) · npj Parkinson s Disease · doi:10.1038/s41531-022-00351-6 · Cited by 85
Mendoza, M. L. Z., Sicheritz‐Pontén, T., & Gilbert, M. T. P. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv001 · Cited by 92
Wiseschart, A., Mhuantong, W., Tangphatsornruang, S., Chantasingh, D., & Pootanakit, K. (2019) · BMC Microbiology · doi:10.1186/s12866-019-1521-8 · Cited by 90
The alteration of gut microbiota in newly diagnosed type 2 diabetic patients
Chen, P., Chien, Y., & Yang, S. (2018) · Nutrition · doi:10.1016/j.nut.2018.11.019 · Cited by 91
A Bioinformatics Guide to Plant Microbiome Analysis
Lucaciu, C. R., Pelikan, C., Gerner, S. M., Zioutis, C., Köstlbacher, S., Marx, H., Herbold, C. W., Schmidt, H., & Rattei, T. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01313 · Cited by 93
Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods
Bokulich, N. A., Ziemski, M., Robeson, M. S., & Kaehler, B. D. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.11.049 · Cited by 98
Kayani, M. U. R., Kan, Y., Qiu, Y., Shen, Y., Gao, C., Feng, R., Zeng, X., Wang, W., Chen, L., & Su, H. L. (2021) · Environmental Pollution · doi:10.1016/j.envpol.2021.116760 · Cited by 86
Dong, T. S., Guan, M., Mayer, E. A., Stains, J., Liu, X. C., Vora, P. P., Jacobs, J. P., Lagishetty, V., Chang, L., Barry, R. L., et al. (2022) · Gut Microbes · doi:10.1080/19490976.2022.2051999 · Cited by 88
Staphylococcal Biofilms in Atopic Dermatitis
Gonzalez, T., Myers, J. M. B., Herr, A. B., & Hershey, G. K. K. (2017) · Current Allergy and Asthma Reports · doi:10.1007/s11882-017-0750-x · Cited by 109
Microbiome changes: an indicator of Parkinson’s disease?
Haikal, C., Chen, Q., & Li, J. (2019) · Translational Neurodegeneration · doi:10.1186/s40035-019-0175-7 · Cited by 83
Graham, E. B., Crump, A. R., Kennedy, D. W., Arntzen, E. V., Fansler, S. J., Purvine, S. O., Nicora, C., Nelson, W. C., Tfaily, M., & Stegen, J. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.05.256 · Cited by 84
Bennett, K. L., Martínez, C. G., Chin, Y., Saltonstall, K., McMillan, W. O., Rovira, J. R., & Loaiza, J. R. (2019) · Scientific Reports · doi:10.1038/s41598-019-48414-8 · Cited by 81
Tilocca, B., Pieroni, L., Soggiù, A., Britti, D., Bonizzi, L., Roncada, P., & Greco, V. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21114045 · Cited by 86
Vrieze, J. D., Regueiro, L., Props, R., Vilchez‐Vargas, R., Jáuregui, R., Pieper, D. H., Lema, J. M., & Carballa, M. (2016) · Biotechnology for Biofuels · doi:10.1186/s13068-016-0652-5 · Cited by 81
Dunlop, A. L., Satten, G. A., Hu, Y., Knight, A. K., Hill, C. C., Wright, M. L., Smith, A. K., Read, T. D., Pearce, B. D., & Corwin, E. J. (2021) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2021.641005 · Cited by 86
Chua, L. L., Rajasuriar, R., Lim, Y. A. L., Woo, Y. L., Loke, P., & Ariffin, H. (2020) · BMC Cancer · doi:10.1186/s12885-020-6654-5 · Cited by 80
Delforno, T. P., Lacerda-Júnior, G. V., Noronha, M. F., Sakamoto, I. K., Varesche, M. B. A., & de Oliveira, V. M. (2017) · MicrobiologyOpen · doi:10.1002/mbo3.443 · Cited by 80
Detection and Enumeration of Spore-Forming Bacteria in Powdered Dairy Products
McHugh, A. J., Feehily, C., Hill, C., & Cotter, P. D. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.00109 · Cited by 84
Campanaro, S., Treu, L., Kougias, P., Zhu, X., & Angelidaki, İ. (2018) · Scientific Reports · doi:10.1038/s41598-018-20414-0 · Cited by 79
Allali, I., Abotsi, R. E., Tow, L. A., Thabane, L., Zar, H. J., Mulder, N., & Nicol, M. P. (2021) · Microbiome · doi:10.1186/s40168-021-01195-7 · Cited by 85
Martemucci, G., Fracchiolla, G., Muraglia, M., Tardugno, R., Dibenedetto, R. S., & D‘Alessandro, A. G. (2023) · Antioxidants · doi:10.3390/antiox12122091 · Cited by 89
The role of gut microbiota in the resistance to obesity in mice fed a high fat diet
Cao, W., Chin, Y., Chen, X., Mi, Y., Xue, C., Wang, Y., & Tang, Q. (2019) · International Journal of Food Sciences and Nutrition · doi:10.1080/09637486.2019.1686608 · Cited by 73
Simmons, T., Caddell, D. F., Deng, S., & Coleman‐Derr, D. (2018) · Journal of Visualized Experiments · doi:10.3791/57561 · Cited by 83
Jiang, X., Li, Y., Ye, J., Lv, L., Yang, L.-Y., Bian, X., Wu, W., Wu, J., Shi, D., Wang, Q., et al. (2020) · World Journal of Gastroenterology · doi:10.3748/wjg.v26.i40.6224 · Cited by 71
Gastrointestinal Microbiota and Type 1 Diabetes Mellitus: The State of Art
Durazzo, M., Ferro, A., & Gruden, G. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8111843 · Cited by 72
Papke, E., Carreiro, A. M., Dennison, C. E., Deutsch, J. M., Isma, L. M., Meiling, S. S., Rossin, A. M., Baker, A. C., Brandt, M. E., Garg, N., et al. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1321271 · Cited by 71
Yoo, J. Y., Rho, M., You, Y., Kwon, E. J., Kim, M., Kym, S., Jee, Y., Kim, Y.-K., & Kim, Y. J. (2016) · Experimental & Molecular Medicine · doi:10.1038/emm.2015.110 · Cited by 68
— Invited Review — Metagenomic investigation of gastrointestinal microbiome in cattle
Kim, M., Park, T., & Yu, Z. (2017) · Asian-Australasian Journal of Animal Sciences · doi:10.5713/ajas.17.0544 · Cited by 75
Falardeau, J., Keeney, K., Trmčić, A., Kitts, D., & Wang, S. (2019) · Food Microbiology · doi:10.1016/j.fm.2019.04.002 · Cited by 69
Metadata
- Authors
- Poretsky, R., Rodriguez-R, L. M., Luo, C., Tsementzi, D., & Konstantinidis, K. T.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Poretsky, Rachel
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-04-08
- Volume
- 9
- Issue
- 4
- Pages
- e93827
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3979728
- OpenAlex
- W2019163239
- Cited by
- 684
- References
- 63
- Citations by year
- 2026: 50 · 2025: 70 · 2024: 45 · 2023: 78 · 2022: 71 · 2021: 83 · 2020: 69 · 2019: 67 · 2018: 62 · 2017: 37 · 2016: 29 · 2015: 20 · 2014: 2
- FWCI
- 25.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- 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)
- Funders
- National Science Foundation (1241046)
- U.S. Department of Energy (DESC0006662)
- Emory University
- Topics
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- 16S rRNA gene amplicon sequencing, metagenomics, planktonic microbial communities, freshwater microbial communities, Lake Lanier, community resilience, sequencing bias, PCR bias, seasonal mixing, storm disturbance, microbial succession, community diversity
- MeSH terms
- Bacteria, Fresh Water, Plankton, RNA, Ribosomal, 16S, Metagenome, Metagenomics
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 ›
Are you an author, or do you see a problem with this page? Request a review
