American Gut: an Open Platform for Citizen-Science Microbiome Research
Open accessPreprint — published as 10.1128/msystems.00031-18 · Paper page
Abstract
Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the USA, UK, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes as compared to environmental samples, demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations, uncover new molecules and kinds of molecular communities in the human stool metabolome, and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness, and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. Importance: We show that a citizen-science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n=1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet which we confirm with untargeted metabolomics on hundreds of samples.
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American Gut Project participants also complete surveys about general health status, disease history, lifestyle data and food frequency.
“These participants also complete surveys about general health status, disease history, lifestyle data and food frequency.”
From Multimodal biomedical AI (2022) · cites this paper as [94]
The Results section (“Cohort characteristics”) says participants reported general health status, disease history, and lifestyle data through a survey. It also says all survey questions were optional and only 14.8% completed the food-frequency questionnaire, so the sentence overstates how broadly participants completed it. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe data featured in this article come from a set of 953,000 rRNA fragments sequenced from stool samples submitted by participants in the American Gut Project.
“The data featured in this article come from a set of 953,000 rRNA fragments sequenced from stool samples submitted by participants in the AGP25, powered by TMI (https://microsetta.ucsd.edu/).”
From Improving microbial phylogeny with citizen science within a mass-market video game (2025) · cites this paper as [25]
The Methods sections on sample selection and participant recruitment describe fecal samples submitted by American Gut Project participants and sequencing of 16S rRNA gene fragments. The Introduction reports over 467 million fragments across the project as of May 2017, but our copy does not establish that the citing article’s dataset contains 953,000 fragments. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe American Gut Project collects microbiome data from self-selected participants across several countries.
“As another example, the American Gut Project collects microbiome data from self-selected participants across several countries 94 .”
From Multimodal biomedical AI (2022) · cites this paper as [94]
BLS is designed to improve a reference multiple alignment of 1 million 16S rRNA sequences from The Microsetta Initiative's American Gut Project.
“BLS is designed to improve a reference multiple alignment of 1 million 16S ribosomal RNA (rRNA) sequences from The Microsetta Initiative’s (TMI) American Gut Project (AGP)25, a citizen science initiative collecting gut microbial genomic data.”
From Improving microbial phylogeny with citizen science within a mass-market video game (2025) · cites this paper as [25]
BLS outperforms SEPP on many variables, including several linked to gut microbe diversity and human health.
“We observed that BLS outperforms SEPP on many variables, including several that have been linked to gut microbe diversity and human health25,39–42.”
From Improving microbial phylogeny with citizen science within a mass-market video game (2025) · cites this paper as [25]
The human gut microbiome is influenced by diet and medication use.
“The human gut microbiome is itself shaped by a wide variety of environmental exposures, including diet (8, 9) and medication use (10–13)”
From Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response (2021) · cites this paper as [9]
A diverse diet, particularly a greater number of different types of plant foods consumed, is associated with greater microbial alpha-diversity.
“A diverse diet, and in particular, the number of different types of plant foods consumed [35,78], has been associated with greater microbial alpha-diversity thought to provide an increased variety of substrates for numerous taxa proliferation [78,84].”
From Effect of Diet on the Gut Microbiota: Rethinking Intervention Duration (2019) · cites this paper as [78]
Species Facealibacterium prausnitzii and genus Oscillospira were increased in individuals who consumed more than 30 plant types a week compared to those who consumed less than 10 plant types.
“Species Facealibacterium prausnitzii and genus Oscillospira, typically known to be SCFA fermenters, were increased in individuals who consumed more than 30 plant types a week in comparison to those who consumed less than 10 plant types [78].”
From Effect of Diet on the Gut Microbiota: Rethinking Intervention Duration (2019) · cites this paper as [78]
The American Gut Project confirmed the observation of a positive relationship between dietary diversity and microbial stability.
“The American Gut Project, a large citizen science open platform study, collected self-report dietary data (FFQs) and faecal samples amongst other measures from over 10,000 individuals, predominantly UK, USA, and Australian residents, confirmed these observations.”
From Effect of Diet on the Gut Microbiota: Rethinking Intervention Duration (2019) · cites this paper as [78]
Findings from the American Gut Project showed that individuals who reported a mental disorder such as depression, schizophrenia, post-traumatic stress disorder (PTSD) or bipolar disorder had more in common with other people with mental disorders, in terms of the bacteria makeup of their gut microbiomes, than they did with their mentally healthy pairs.
“Consistent with this hypothesis are reported findings from the American Gut Project showing that individuals who reported a mental disorder such as depression, schizophrenia, post-traumatic stress disorder (PTSD) or bipolar disorder had more in common with other people with mental disorders, in terms of the bacteria makeup of their gut microbiomes, than they did with their mentally healthy pairs.”
From Role of diet and its effects on the gut microbiome in the pathophysiology of mental disorders (2022) · cites this paper as [133]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835 · see citing works
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
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 · see citing works
Self-citationSPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing
Bankevich, A., Nurk, S., Antipov, D., Gurevich, A. A., Dvorkin, M. V., Kulikov, A. S., Lesin, V. M., Nikolenko, S., Pham, S., Prjibelski, A. D., et al. (2012) · Journal of Computational Biology · doi:10.1089/cmb.2012.0021 · Cited by 25,691 · see citing works
Self-citationNaive 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 19,770 · see citing works
Prokka: rapid prokaryotic genome annotation
Seemann, T. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu153 · Cited by 18,512 · see citing works
A new method for non-parametric multivariate analysis of variance
Anderson, M. J. (2001) · Austral Ecology · doi:10.1111/j.1442-9993.2001.01070.pp.x · Cited by 13,828 · see citing works
An Ordination of the Upland Forest Communities of Southern Wisconsin
Bray, J. R., & Curtis, J. T. (1957) · Ecological Monographs · doi:10.2307/1942268 · Cited by 11,736 · see citing works
Structure, function and diversity of the healthy human microbiome
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726 · see citing works
Self-citationA human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624 · see citing works
Diet rapidly and reproducibly alters the human gut microbiome
David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., Ling, A. V., Devlin, A. S., Varma, Y., Fischbach, M. A., et al. (2014) · Nature · doi:10.1038/nature12820 · Cited by 10,100 · see citing works
PLS-regression: a basic tool of chemometrics
Wold, S., Sjöström, M., & Eriksson, L. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00155-1 · Cited by 10,084 · see citing works
Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample
Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C., Turnbaugh, P. J., Fierer, N., & Knight, R. D. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000080107 · Cited by 9,633 · see citing works
Self-citationUltra-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 · see citing works
Self-citationCohen, J. (1992) · Psychological Bulletin · doi:10.1037//0033-2909.112.1.155 · Cited by 8,771 · see citing works
NOTES ON CONTINUOUS STOCHASTIC PHENOMENA
MORAN, P. A. P. (1950) · Biometrika · doi:10.1093/biomet/37.1-2.17 · Cited by 8,622 · see citing works
UniFrac: a New Phylogenetic Method for Comparing Microbial Communities
Lozupone, C., & Knight, R. (2005) · Applied and Environmental Microbiology · doi:10.1128/aem.71.12.8228-8235.2005 · Cited by 8,227 · see citing works
Self-citationHuman gut microbiome viewed across age and geography
Yatsunenko, T., Rey, F. E., Manary, M. J., Trehan, I., Dominguez-Bello, M. G., Contreras, M., Magris, M., Hidalgo, G., Baldassano, R. N., Anokhin, A. P., et al. (2012) · Nature · doi:10.1038/nature11053 · Cited by 7,815 · see citing works
Self-citationAccelerated Profile HMM Searches
Eddy, S. R. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002195 · Cited by 6,975 · see citing works
The carbohydrate-active enzymes database (CAZy) in 2013
Lombard, V., Ramulu, H. G., Drula, É., Coutinho, P. M., & Henrissat, B. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1178 · Cited by 6,355 · see citing works
HMMER web server: interactive sequence similarity searching
Finn, R. D., Clements, J., & Eddy, S. R. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr367 · Cited by 5,952 · see citing works
Conservation evaluation and phylogenetic diversity
Faith, D. (1992) · Biological Conservation · doi:10.1016/0006-3207(92)91201-3 · Cited by 5,495 · see citing works
Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., Fan, T. W.-M., Fiehn, O., Goodacre, R., Griffin, J. L., et al. (2007) · Metabolomics · doi:10.1007/s11306-007-0082-2 · Cited by 5,368 · see citing works
McDonald, D. T., Price, M. N., Goodrich, J. K., Nawrocki, E. P., DeSantis, T. Z., Probst, A. J., Andersen, G. L., Knight, R. D., & Hugenholtz, P. (2011) · The ISME Journal · doi:10.1038/ismej.2011.139 · Cited by 5,010 · see citing works
Self-citationWang, M., Carver, J. J., Phelan, V. V., Sanchez, L. M., Garg, N., Peng, Y., Nguyen, D. D., Watrous, J., Kapono, C. A., Luzzatto-Knaan, T., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3597 · Cited by 4,629 · see citing works
Self-citationKraken: ultrafast metagenomic sequence classification using exact alignments
Wood, D. E., & Salzberg, S. L. (2014) · Genome Biology · doi:10.1186/gb-2014-15-3-r46 · Cited by 4,521 · see citing works
metaSPAdes: a new versatile metagenomic assembler
Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017) · Genome Research · doi:10.1101/gr.213959.116 · Cited by 4,496 · see citing works
Self-citationTHE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED
WELCH, B. L. (1947) · Biometrika · doi:10.1093/biomet/34.1-2.28 · Cited by 4,135 · see citing works
Pluskal, T., Castillo, S., Villar‐Briones, A., & Orešič, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-395 · Cited by 3,852 · see citing works
Current Cigarette Smoking Among Adults — United States, 2016
Jamal, A., Phillips, E., Gentzke, A. S., Homa, D. M., Babb, S. D., King, B. A., & Neff, L. J. (2018) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6702a1 · Cited by 3,060 · see citing works
UniFrac: an effective distance metric for microbial community comparison
Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J., & Knight, R. (2011) · The ISME Journal · doi:10.1038/ismej.2010.133 · Cited by 2,825 · see citing works
Self-citationMaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets
Wu, Y., Simmons, B. A., & Singer, S. W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv638 · Cited by 2,809 · see citing works
Lozupone, C., Hamady, M., Kelley, S. T., & Knight, R. D. (2007) · Applied and Environmental Microbiology · doi:10.1128/aem.01996-06 · Cited by 2,717 · see citing works
Self-citationApprill, A., McNally, S., Parsons, R., & Weber, L. (2015) · Aquatic Microbial Ecology · doi:10.3354/ame01753 · Cited by 2,707 · see citing works
A communal catalogue reveals Earth's multiscale microbial diversity
Thompson, L. R., Sanders, J. G., McDonald, D., Amir, A., Ladau, J., Locey, K. J., Prill, R. J., Tripathi, A., Gibbons, S. M., Ackermann, G., et al. (2017) · Nature · doi:10.1038/nature24621 · Cited by 2,684 · see citing works
Self-citationTrends in GPCR drug discovery: new agents, targets and indications
Hauser, A. S., Attwood, M. M., Rask‐Andersen, M., Schiöth, H. B., & Gloriam, D. E. I. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.178 · Cited by 2,609 · see citing works
Succession of microbial consortia in the developing infant gut microbiome
Koenig, J. E., Spor, A., Scalfone, N., Fricker, A. D., Stombaugh, J., Knight, R., Angenent, L. T., & Ley, R. E. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000081107 · Cited by 2,540 · see citing works
Self-citationPopulation-level analysis of gut microbiome variation
Falony, G., Joossens, M., Vieira-Silva, S., Wang, J., Darzi, Y., Faust, K., Kurilshikov, A., Bonder, M. J., Valles-Colomer, M., Vandeputte, D., et al. (2016) · Science · doi:10.1126/science.aad3503 · Cited by 2,400 · see citing works
Prevalence of and Trends in Diabetes Among Adults in the United States, 1988-2012
Menke, A., Casagrande, S., Geiss, L., & Cowie, C. C. (2015) · JAMA · doi:10.1001/jama.2015.10029 · Cited by 2,362 · see citing works
Bracken: estimating species abundance in metagenomics data
(2016) · doi:10.7717/peerj-cs.104 · Cited by 2,333 · see citing works
Altered fecal microbiota composition in patients with major depressive disorder
Jiang, H., Ling, Z., Zhang, Y., Mao, H., Ma, Z., Yin, Y., Wang, W., Tang, W., Tan, Z., Shi, J., et al. (2015) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2015.03.016 · Cited by 2,275 · see citing works
Normalization and microbial differential abundance strategies depend upon data characteristics
Weiss, S. J., Xu, Z. Z., Peddada, S. D., Amir, A., Bittinger, K. L., González, A., Lozupone, C., Zaneveld, J., Vázquez‐Baeza, Y., Birmingham, A., et al. (2017) · Microbiome · doi:10.1186/s40168-017-0237-y · Cited by 2,121 · see citing works
Self-citationZheng, P., Zeng, B., Zhou, C., Liu, M., Fang, Z., Xu, X., Zeng, L., Chen, J., Fan, S., Du, X., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2016.44 · Cited by 2,023 · see citing works
Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity
Zhernakova, A., Kurilshikov, A., Bonder, M. J., Tigchelaar, E. F., Schirmer, M., Vatanen, T., Mujagic, Z., Vila, A. V., Falony, G., Vieira-Silva, S., et al. (2016) · Science · doi:10.1126/science.aad3369 · Cited by 1,914 · see citing works
Self-citationDeblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns
Amir, A., McDonald, D., Navas-Molina, J. A., Kopylova, E., Morton, J. T., Zech Xu, Z., Kightley, E. P., Thompson, L. R., Hyde, E. R., Gonzalez, A., et al. (2017) · mSystems · doi:10.1128/msystems.00191-16 · Cited by 1,881 · see citing works
Self-citationThe abundance and variety of carbohydrate-active enzymes in the human gut microbiota
Kaoutari, A. E., Armougom, F., Gordon, J. I., Raoult, D., & Henrissat, B. (2013) · Nature Reviews Microbiology · doi:10.1038/nrmicro3050 · Cited by 1,683 · see citing works
Henderson, G., Cox, F., Ganesh, S., Jonker, A., Young, W., Collaborators, G. R. C., Abecia, L., Angarita, E., Aravena, P., Nora Arenas, G., et al. (2015) · Scientific Reports · doi:10.1038/srep14567 · Cited by 1,682 · see citing works
Worlds within worlds: evolution of the vertebrate gut microbiota
Ley, R. E., Lozupone, C., Hamady, M., Knight, R. D., & Gordon, J. I. (2008) · Nature Reviews Microbiology · doi:10.1038/nrmicro1978 · Cited by 1,652 · see citing works
Self-citationUsing MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis
Xia, J., & Wishart, D. S. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.11 · Cited by 1,537 · see citing works
The control and consequences of bacterial fermentation in the human colon
Cummings, J. H., & Macfarlane, G. T. (1991) · Journal of Applied Bacteriology · doi:10.1111/j.1365-2672.1991.tb02739.x · Cited by 1,499 · see citing works
Mathematical Methods of Statistics (PMS-9)
Cramér, H. (1946) · Princeton University Press eBooks · doi:10.1515/9781400883868 · Cited by 1,345 · see citing works
EMPeror: a tool for visualizing high-throughput microbial community data
Vázquez‐Baeza, Y., Pirrung, M., Gonzalez, A., & Knight, R. D. (2013) · GigaScience · doi:10.1186/2047-217x-2-16 · Cited by 1,272 · see citing works
Self-citationStrains, functions and dynamics in the expanded Human Microbiome Project
Lloyd-Price, J., Mahurkar, A., Rahnavard, G., Crabtree, J., Orvis, J., Hall, A. B., Brady, A., Creasy, H. H., McCracken, C., Giglio, M. G., et al. (2017) · Nature · doi:10.1038/nature23889 · Cited by 1,267 · see citing works
Self-citationDynamics of the human gut microbiome in inflammatory bowel disease
Halfvarson, J., Brislawn, C. J., Lamendella, R., Vázquez-Baeza, Y., Walters, W. A., Bramer, L. M., D'Amato, M., Bonfiglio, F., McDonald, D., Gonzalez, A., et al. (2017) · Nature Microbiology · doi:10.1038/nmicrobiol.2017.4 · Cited by 1,204 · see citing works
Self-citationMinireview: Gut microbiota: The neglected endocrine organ
Clarke, G., Stilling, R. M., Kennedy, P. J., Stanton, C., Cryan, J. F., & Dinan, T. G. (2014) · Molecular Endocrinology · doi:10.1210/me.2014-1108 · Cited by 1,192 · see citing works
Searching molecular structure databases with tandem mass spectra using CSI:FingerID
Dührkop, K., Shen, H., Meusel, M., Rousu, J., & Böcker, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509788112 · Cited by 1,190 · see citing works
<i>Escherichia coli</i> Induces DNA Double-Strand Breaks in Eukaryotic Cells
Nougayrède, J.-P., Homburg, S., Taieb, F., Boury, M., Brzuszkiewicz, E., Gottschalk, G., Buchrieser, C., Hacker, J., Dobrindt, U., & Oswald, E. (2006) · Science · doi:10.1126/science.1127059 · Cited by 1,188 · see citing works
Cohabiting family members share microbiota with one another and with their dogs
Song, S. J., Lauber, C. L., Costello, E. K., Lozupone, C., Humphrey, G., Berg-Lyons, D., Caporaso, J. G., Knights, D., Clemente, J. C., Nakielny, S., et al. (2013) · eLife · doi:10.7554/elife.00458 · Cited by 1,075 · see citing works
Self-citationMass spectral molecular networking of living microbial colonies
Watrous, J. D., Roach, P. J., Alexandrov, T., Heath, B. S., Yang, J. Y., Kersten, R. D., van der Voort, M., Pogliano, K., Gross, H., Raaijmakers, J. M., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1203689109 · Cited by 974 · see citing works
Self-citationSeasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania
Smits, S. A., Leach, J., Sonnenburg, E. D., Gonzalez, C. G., Lichtman, J. S., Reid, G., Knight, R., Manjurano, A., Changalucha, J., Elias, J. E., et al. (2017) · Science · doi:10.1126/science.aan4834 · Cited by 886 · see citing works
Self-citationThe microbiome of uncontacted Amerindians
Clemente, J. C., Pehrsson, E. C., Blaser, M. J., Sandhu, K., Gao, Z., Wang, B., Magris, M., Hidalgo, G., Contreras, M., Noya-Alarcón, Ó., et al. (2015) · Science Advances · doi:10.1126/sciadv.1500183 · Cited by 851 · see citing works
Self-citationFragGeneScan: predicting genes in short and error-prone reads
Rho, M., Tang, H., & Ye, Y. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq747 · Cited by 838 · see citing works
Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen.
Hellinger, E. (1909) · Journal für die reine und angewandte Mathematik (Crelles Journal) · doi:10.1515/crll.1909.136.210 · Cited by 823 · see citing works
How Many Adults Identify as Transgender in the United States
Flores, A. R., Herman, J. L., Gates, G. J., & Brown, T. N. T. (2016) · eScholarship (California Digital Library) · Cited by 688 · see citing works
OpenMS: a flexible open-source software platform for mass spectrometry data analysis
Röst, H. L., Sachsenberg, T. T., Aiche, S., Bielow, C., Weisser, H., Aicheler, F., Andreotti, S., Ehrlich, H., Gutenbrunner, P., Kenar, E., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3959 · Cited by 680 · see citing works
Gibson, M. K., Forsberg, K. J., & Dantas, G. A. (2014) · The ISME Journal · doi:10.1038/ismej.2014.106 · Cited by 656 · see citing works
Molecular Networking as a Dereplication Strategy
Yang, J. Y., Sanchez, L. M., Rath, C. M. B., Liu, X., Boudreau, P. D., Bruns, N., Glukhov, E., Wodtke, A., de Felício, R., Fenner, A. M., et al. (2013) · Journal of Natural Products · doi:10.1021/np400413s · Cited by 613 · see citing works
Self-citationSubsistence strategies in traditional societies distinguish gut microbiomes
Obregon-Tito, A. J., Tito, R. Y., Metcalf, J., Sankaranarayanan, K., Clemente, J. C., Ursell, L. K., Zech Xu, Z., Van Treuren, W., Knight, R., Gaffney, P. M., et al. (2015) · Nature Communications · doi:10.1038/ncomms7505 · Cited by 587 · see citing works
Self-citationRapid and Accurate Large-Scale Coestimation of Sequence Alignments and Phylogenetic Trees
Liu, K., Raghavan, S., Nelesen, S. M., Linder, C. R., & Warnow, T. J. (2009) · Science · doi:10.1126/science.1171243 · Cited by 513 · see citing works
Commensal bacteria make GPCR ligands that mimic human signalling molecules
Cohen, L. J., Esterhazy, D., Kim, S.-H., Lemetre, C., Aguilar, R. R., Gordon, E. A., Pickard, A. J., Cross, J. R., Emiliano, A. B., Han, S. M., et al. (2017) · Nature · doi:10.1038/nature23874 · Cited by 500 · see citing works
Preservation Methods Differ in Fecal Microbiome Stability, Affecting Suitability for Field Studies
Song, S. J., Amir, A., Metcalf, J. L., Amato, K. R., Xu, Z. Z., Humphrey, G. C., & Knight, R. D. (2016) · mSystems · doi:10.1128/msystems.00021-16 · Cited by 484 · see citing works
Self-citationTemporal variability is a personalized feature of the human microbiome
Flores, G. E., Caporaso, J. G., Henley, J. B., Rideout, J. R., Domogala, D., Chase, J., Leff, J. W., Vázquez-Baeza, Y., Gonzalez, A., Knight, R., et al. (2014) · Genome Biology · doi:10.1186/s13059-014-0531-y · Cited by 446 · see citing works
Self-citationExtreme Dysbiosis of the Microbiome in Critical Illness
McDonald, D., Ackermann, G., Khailova, L., Baird, C., Heyland, D., Kozar, R., Lemieux, M., Derenski, K., King, J., Vis-Kampen, C., et al. (2016) · mSphere · doi:10.1128/msphere.00199-16 · Cited by 426 · see citing works
Self-citation<i>Oscillospira</i> and related bacteria – From metagenomic species to metabolic features
Gophna, U., Konikoff, T., & Nielsen, H. B. (2017) · Environmental Microbiology · doi:10.1111/1462-2920.13658 · Cited by 409 · see citing works
Marked seasonal variation in the wild mouse gut microbiota
Maurice, C. F., Knowles, S. C. L., Ladau, J., Pollard, K. S., Fenton, A., Pedersen, A. B., & Turnbaugh, P. J. (2015) · The ISME Journal · doi:10.1038/ismej.2015.53 · Cited by 406 · see citing works
Self-citationPolyunsaturated fatty acid saturation by gut lactic acid bacteria affecting host lipid composition
Kishino, S., Takeuchi, M., Park, S.-B., Hirata, A., Kitamura, N., Kunisawa, J., Kiyono, H., Iwamoto, R., Isobe, Y., Arita, M., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312937110 · Cited by 395 · see citing works
How gut microbes talk to organs: The role of endocrine and nervous routes
Cani, P. D., & Knauf, C. (2016) · Molecular Metabolism · doi:10.1016/j.molmet.2016.05.011 · Cited by 329 · see citing works
Putze, J., Hennequin, C., Nougayrède, J., Zhang, W., Homburg, S., Karch, H., Bringer, M.-A., Fayolle, C., Carniel, É., Rabsch, W., et al. (2009) · Infection and Immunity · doi:10.1128/iai.00522-09 · Cited by 319 · see citing works
Lin, P., Ding, B., Feng, C., Yin, S., Zhang, T., Qi, X., Lv, H., Guo, X., Dong, K., Zhu, Y., et al. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.09.051 · Cited by 311 · see citing works
Conjugated linoleic acid biosynthesis by human-derived Bifidobacterium species
Coakley, M., Ross, R. P., Nordgren, M., Fitzgerald, G., Devery, R., & Stanton, C. (2003) · Journal of Applied Microbiology · doi:10.1046/j.1365-2672.2003.01814.x · Cited by 299 · see citing works
Filtering for increased power for microarray data analysis
Hackstadt, A. J., & Hess, A. M. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-11 · Cited by 291 · see citing works
The ecology of microscopic life in household dust
Barberán, A., Dunn, R. R., Reich, B. J., Pacifici, K., Laber, E. B., Menninger, H., Morton, J. M., Henley, J. B., Leff, J. W., Miller, S. L., et al. (2015) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2015.1139 · Cited by 291 · see citing works
Self-citationAtropos: specific, sensitive, and speedy trimming of sequencing reads
Didion, J. P., Martin, M., & Collins, F. S. (2017) · PeerJ · doi:10.7717/peerj.3720 · Cited by 260 · see citing works
Balance Trees Reveal Microbial Niche Differentiation
Morton, J. T., Sanders, J., Quinn, R. A., McDonald, D., Gonzalez, A., Vázquez-Baeza, Y., Navas-Molina, J. A., Song, S. J., Metcalf, J. L., Hyde, E. R., et al. (2017) · mSystems · doi:10.1128/msystems.00162-16 · Cited by 254 · see citing works
Self-citationSEPP: SATé-Enabled Phylogenetic Placement
MIRARAB, S., NGUYEN, N., & WARNOW, T. (2011) · Biocomputing 2012 · doi:10.1142/9789814366496_0024 · Cited by 251 · see citing works
Weingarden, A. R., González, A., Vázquez‐Baeza, Y., Weiss, S. J., Humphry, G., Berg-Lyons, D., Knights, D., Unno, T., Bobr, A., Kang, J., et al. (2015) · Microbiome · doi:10.1186/s40168-015-0070-0 · Cited by 249 · see citing works
Self-citationGlobal marine bacterial diversity peaks at high latitudes in winter
Ladau, J., Sharpton, T. J., Finucane, M. M., Jospin, G., Kembel, S. W., O’Dwyer, J. P., Koeppel, A. F., Green, J. L., & Pollard, K. S. (2013) · The ISME Journal · doi:10.1038/ismej.2013.37 · Cited by 219 · see citing works
Self-citationCohen, L. J., Kang, H., Chu, J., Huang, Y.-H., Gordon, E. A., Reddy, B. V. B., Ternei, M. A., Craig, J. W., & Brady, S. F. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508737112 · Cited by 206 · see citing works
Sécher, T., Samba‐Louaka, A., Oswald, É., & Nougayrède, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077157 · Cited by 134 · see citing works
Nguyen, D. D., Melnik, A. V., Koyama, N., Lu, X., Schorn, M., Fang, J., Aguinaldo, K., Lincecum, T. L., Ghequire, M. G. K., Carrión, V. J., et al. (2016) · Nature Microbiology · doi:10.1038/nmicrobiol.2016.197 · Cited by 134 · see citing works
Self-citationBracken: Estimating species abundance in metagenomics dataPreprint
Lu, J., Breitwieser, F. P., Thielen, P., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/051813 · Cited by 129 · see citing works
the Millennium Cohort Study Team, Smith, B., Chu, L. K., Smith, T. C., Amoroso, P. J., Boyko, E. J., Hooper, T. I., Gackstetter, G. D., & Ryan, M. A. K. (2008) · BMC Medical Research Methodology · doi:10.1186/1471-2288-8-37 · Cited by 121 · see citing works
Donnell, M. M. O., Harris, H. M. B., Ross, R. P., & O’Toole, P. W. (2017) · MicrobiologyOpen · doi:10.1002/mbo3.509 · Cited by 115 · see citing works
Ejigu, B. A., Valkenborg, D., Baggerman, G., Vanaerschot, M., Witters, E., Dujardin, J., Burzykowski, T., & Berg, M. (2013) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2013.0010 · Cited by 113 · see citing works
The WHO World Health Report 2001 on mental health
Saraceno, B. (2002) · Epidemiologia e Psichiatria Sociale · doi:10.1017/s1121189x00005546 · Cited by 104 · see citing works
Gurevich, A. A., Mikheenko, A., Shlemov, A., Korobeynikov, A., Mohimani, H., & Pevzner, P. A. (2018) · Nature Microbiology · doi:10.1038/s41564-017-0094-2 · Cited by 99 · see citing works
Self-citationCorrecting for Microbial Blooms in Fecal Samples during Room-Temperature Shipping
Amir, A., McDonald, D. T., Navas-Molina, J. A., Debelius, J. W., Morton, J. T., Hyde, E. R., Robbins‐Pianka, A., & Knight, R. D. (2017) · mSystems · doi:10.1128/msystems.00199-16 · Cited by 98 · see citing works
Self-citationDiscrete False-Discovery Rate Improves Identification of Differentially Abundant Microbes
Jiang, L., Amir, A., Morton, J. T., Heller, R., Ery Arias-castro, A., & Knight, R. D. (2017) · mSystems · doi:10.1128/msystems.00092-17 · Cited by 95 · see citing works
Self-citationBringing the Dynamic Microbiome to Life with Animations
Vázquez‐Baeza, Y., Gonzalez, A., Smarr, L. L., McDonald, D. T., Morton, J. T., Navas-Molina, J. A., & Knight, R. D. (2017) · Cell Host & Microbe · doi:10.1016/j.chom.2016.12.009 · Cited by 93 · see citing works
Self-citationStoops, N. (2004) · Cited by 66 · see citing works
Kohl, K. D., Brun, A., Magallanes, M. E., Brinkerhoff, J., Laspiur, A., Acosta, J. C., Bordenstein, S. R., & Caviedes‐Vidal, E. (2016) · Journal of Experimental Biology · doi:10.1242/jeb.138370 · Cited by 63 · see citing works
Guo, W., Sarkar, S. K., & Peddada, S. D. (2009) · Biometrics · doi:10.1111/j.1541-0420.2009.01292.x · Cited by 55 · see citing works
Self-citationEducational Attainment in the United States: Population Characteristics. Current Population Reports.
Newburger, E. C., & Curry, A. (1999) · Cited by 44 · see citing works
Functional Metagenomics to Study Antibiotic Resistance
Boolchandani, M., Patel, S., & Dantas, G. A. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6634-9_19 · Cited by 44 · see citing works
Colicin E2 Production and Release by Escherichia coli K12 and Other Enterobacteriaceae
Pugsley, A. P., GOLDZAHL, N., & Barker, R. M. (1985) · Microbiology · doi:10.1099/00221287-131-10-2673 · Cited by 16 · see citing works
Prevalence of and trends in diabetes among adults in the United States, 1988-2012.
Gao, H. X., Regier, E. E., & Close, K. L. (2016) · PubMed · Cited by 15 · see citing works
Dalapicolla, J., do Prado, J. R., Percequillo, A. R., & Knowles, L. L. (2021) · Journal of Biogeography · doi:10.1111/jbi.14281 · Cited by 12 · see citing works
American Gut Project fecal sOTU counts table
McDonald, D. T., McDonald, D. T., Robbins‐Pianka, A., Mann, A. E., Vrbanac, A. F., Amir, A., Frazier, A., Antonio, G., Tripathi, A., Fahimipour, A. K., et al. (2018) · Figshare · doi:10.6084/m9.figshare.6137192 · Cited by 5 · see citing works
Self-citationFull American Gut Project mapping file
McDonald, D. T., McDonald, D. T., Robbins‐Pianka, A., Mann, A. E., Vrbanac, A. F., Amir, A., Frazier, A., Gonzalez, A., Tripathi, A., Fahimipour, A. K., et al. (2018) · Figshare · doi:10.6084/m9.figshare.6137315 · Cited by 5 · see citing works
Self-citationAmerican Gut collated alpha diversities
McDonald, D., McDonald, D., Robbins‐Pianka, A., Mann, A. E., Vrbanac, A. F., Amir, A., Frazier, A., Gonzalez, A., Tripathi, A., Fahimipour, A. K., et al. (2018) · Figshare · doi:10.6084/m9.figshare.6137312 · Cited by 1 · see citing works
Self-citationAmerican Gut Project fecal sOTU relative abundance table
McDonald, D. T., McDonald, D. T., Robbins‐Pianka, A., Mann, A. E., Vrbanac, A. F., Amir, A., Frazier, A., Antonio, G., Tripathi, A., Fahimipour, A. K., et al. (2018) · Figshare · doi:10.6084/m9.figshare.6137198 · Cited by 1 · see citing works
Self-citationMcDonald, D. T., McDonald, D. T., Robbins‐Pianka, A., Mann, A. E., Vrbanac, A. F., Amir, A., Frazier, A., Gonzalez, A., Tripathi, A., Fahimipour, A. K., et al. (2018) · Figshare · doi:10.6084/m9.figshare.6131024 · Cited by 1 · see citing works
Self-citationWeighted normalized UniFrac distances
McDonald, D. T., McDonald, D. T., Robbins‐Pianka, A., Mann, A. E., Vrbanac, A. F., Amir, A., Frazier, A., Gonzalez, A., Tripathi, A., Fahimipour, A. K., et al. (2018) · Figshare · doi:10.6084/m9.figshare.6133121 · Cited by 1 · see citing works
Self-citationMcDonald, D. T., McDonald, D. T., Robbins‐Pianka, A., Mann, A. E., Vrbanac, A. F., Amir, A., Frazier, A., Gonzalez, A., Tripathi, A., Fahimipour, A. K., et al. (2018) · Figshare · doi:10.6084/m9.figshare.6137270 · Cited by 1 · see citing works
Self-citationMcDonald, D., Robbins‐Pianka, A., Mann, A. E., Vrbanac, A. F., Amir, A., Frazier, A., Gonzalez, A., Tripathi, A., Fahimipour, A. K., Brennen, C., et al. (2018) · Figshare · doi:10.6084/m9.figshare.5936479 · Cited by 1 · see citing works
Self-citationMcDonald, D., Robbins‐Pianka, A., Mann, A. E., Vrbanac, A. F., Amir, A., Frazier, A., Antonio, G., Tripathi, A., Fahimipour, A. K., Brennen, C., et al. (2018) · Figshare · doi:10.6084/m9.figshare.5936482 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
2 of the 121 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [59]National Center for Health Statistics 2016 Table 58. In Health, United States, 2015: with special feature on racial and ethnic health disparities. National Center for Health Statistics, Hyattsville, MD https://www.cdc.gov/nchs/data/hus/2015/058.pdf. 27308685
- [68]Mantel N 1967 The detection of disease clustering and a generalized regression approach. Cancer Res 27: 209– 220. 6018555
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 · see citing works
The neuroactive potential of the human gut microbiota in quality of life and depression
Valles-Colomer, M., Falony, G., Darzi, Y., Tigchelaar, E. F., Wang, J., Tito, R. Y., Schiweck, C., Kurilshikov, A., Joossens, M., Wijmenga, C., et al. (2019) · Nature Microbiology · doi:10.1038/s41564-018-0337-x · Cited by 1,963 · see citing works
You are what you eat: Diet, health and the gut microbiota
Zmora, N., Suez, J., & Elinav, E. (2019) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-018-0061-2 · Cited by 1,792 · see citing works
Feature-based molecular networking in the GNPS analysis environment
Nothias-Scaglia, L.-F., Petras, D., Schmid, R., Dührkop, K., Rainer, J., Sarvepalli, A., Protsyuk, I., Ernst, M., Tsugawa, H., Fleischauer, M., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0933-6 · Cited by 1,658 · see citing works
Self-citationChiu, C. Y., & Miller, S. A. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0113-7 · Cited by 1,620 · see citing works
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · see citing works
Deciphering cell–cell interactions and communication from gene expression
Armingol, E., Officer, A., Harismendy, O., & Lewis, N. E. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00292-x · Cited by 1,267 · see citing works
The microbiome, cancer, and cancer therapy
Helmink, B. A., Khan, M. A. W., Hermann, A., Gopalakrishnan, V., & Wargo, J. A. (2019) · Nature Medicine · doi:10.1038/s41591-019-0377-7 · Cited by 1,200 · see citing works
GABA-modulating bacteria of the human gut microbiota
Strandwitz, P., Kim, K. H., Terekhova, D., Liu, J. K., Sharma, A., Levering, J., McDonald, D., Dietrich, D., Ramadhar, T. R., Lekbua, A., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0307-3 · Cited by 1,170 · see citing works
Self-citationRegional variation limits applications of healthy gut microbiome reference ranges and disease models
He, Y., Wu, W., Zheng, H.-M., Li, P., McDonald, D., Sheng, H.-F., Chen, M.-X., Chen, Z.-H., Ji, G.-Y., Zheng, Z.-D.-X., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0164-x · Cited by 894 · see citing works
Self-citationDietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response
Spencer, C. N., McQuade, J. L., Gopalakrishnan, V., McCulloch, J. A., Vetizou, M., Cogdill, A. P., Khan, M. A. W., Zhang, X., White, M. G., Peterson, C. B., et al. (2021) · Science · doi:10.1126/science.aaz7015 · Cited by 966 · see citing works
Self-citationEffect of Diet on the Gut Microbiota: Rethinking Intervention Duration
Leeming, E. R., Johnson, A. J., Spector, T. D., & Le Roy, C. I. (2019) · Nutrients · doi:10.3390/nu11122862 · Cited by 817 · see citing works
Self-citationReproducible molecular networking of untargeted mass spectrometry data using GNPS
Aron, A. T., Gentry, E. C., McPhail, K. L., Nothias-Scaglia, L.-F., Nothias-Esposito, M., Bouslimani, A., Petras, D., Gauglitz, J. M., Sikora, N. C., Vargas, F., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0317-5 · Cited by 737 · see citing works
Self-citationGreengenes2 unifies microbial data in a single reference tree
McDonald, D. T., Jiang, Y., Balaban, M., Cantrell, K., Zhu, Q., González, A., Morton, J. T., Nicolaou, G., Parks, D. H., Karst, S. M., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01845-1 · Cited by 749 · see citing works
Self-citationGut microbiome pattern reflects healthy ageing and predicts survival in humans
Wilmanski, T. M., Diener, C., Rappaport, N., Patwardhan, S., Wiedrick, J. T., Lapidus, J., Earls, J. C., Zimmer, A., Glusman, G., Robinson, M., et al. (2021) · Nature Metabolism · doi:10.1038/s42255-021-00348-0 · Cited by 693 · see citing works
FEAST: fast expectation-maximization for microbial source tracking
Shenhav, L., Thompson, M., Joseph, T., Briscoe, L., Furman, O., Bogumil, D. D., Mizrahi, I., Pe’er, I., & Halperin, E. (2019) · Nature Methods · doi:10.1038/s41592-019-0431-x · Cited by 652 · see citing works
The Gut Microbiota in the First Decade of Life
Derrien, M., Alvarez, A., & de Vos, W. M. (2019) · Trends in Microbiology · doi:10.1016/j.tim.2019.08.001 · Cited by 648 · see citing works
NetCoMi: network construction and comparison for microbiome data in R
Peschel, S., Müller, C. L., von Mutius, E., Boulesteix, A., & Depner, M. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa290 · Cited by 618 · see citing works
Host variables confound gut microbiota studies of human disease
Vujkovic-Cvijin, I., Sklar, J., Jiang, L., Natarajan, L., Knight, R., & Belkaid, Y. (2020) · Nature · doi:10.1038/s41586-020-2881-9 · Cited by 614 · see citing works
Self-citationGlobal chemical effects of the microbiome include new bile-acid conjugations
Quinn, R. A., Melnik, A. V., Vrbanac, A., Fu, T., Patras, K. A., Christy, M. P., Bodai, Z., Belda-Ferre, P., Tripathi, A., Chung, L. K., et al. (2020) · Nature · doi:10.1038/s41586-020-2047-9 · Cited by 579 · see citing works
Self-citationSystematic Review of Gut Microbiota and Major Depression
Cheung, S. G., Goldenthal, A. R., Uhlemann, A.-C., Mann, J. J., Miller, J. M., & Sublette, M. E. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00034 · Cited by 597 · see citing works
Personalized Mapping of Drug Metabolism by the Human Gut Microbiome
Javdan, B., Lopez, J. G., Chankhamjon, P., Lee, Y.-C. J., Hull, R., Wu, Q., Wang, X., Chatterjee, S., & Donia, M. S. (2020) · Cell · doi:10.1016/j.cell.2020.05.001 · Cited by 486 · see citing works
McCulloch, J. A., Davar, D., Rodrigues, R. R., Badger, J. H., Fang, J. R., Cole, A. M., Balaji, A. K., Vetizou, M., Prescott, S. M., Fernandes, M. R., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01698-2 · Cited by 472 · see citing works
Karst, S. M., Ziels, R. M., Kirkegaard, R. H., Sørensen, E. A., McDonald, D. T., Zhu, Q., Knight, R. D., & Albertsen, M. (2021) · Nature Methods · doi:10.1038/s41592-020-01041-y · Cited by 410 · see citing works
Self-citationMarcos-Zambrano, L. J., Karađuzović-Hadžiabdić, K., Lončar-Turukalo, T., Przymus, P., Trajkovik, V., Aasmets, O., Berland, M., Gruca, A., Telalović, J. H., Hron, K., et al. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.634511 · Cited by 429 · see citing works
LinDA: linear models for differential abundance analysis of microbiome compositional data
Zhou, H., He, K., Chen, J., & Zhang, X. (2022) · Genome biology · doi:10.1186/s13059-022-02655-5 · Cited by 419 · see citing works
Age- and Sex-Dependent Patterns of Gut Microbial Diversity in Human Adults
de la Cuesta‐Zuluaga, J., Kelley, S. T., Chen, Y., Escobar, J. S., Mueller, N. T., Ley, R. E., McDonald, D. T., Huang, S. W., Swafford, A. D.-E., Knight, R. D., et al. (2019) · mSystems · doi:10.1128/msystems.00261-19 · Cited by 400 · see citing works
Self-citationModulating the microbiome to improve therapeutic response in cancer
McQuade, J. L., Daniel, C. R., Helmink, B. A., & Wargo, J. A. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30952-5 · Cited by 376 · see citing works
The Healthy Microbiome—What Is the Definition of a Healthy Gut Microbiome?
Shanahan, F., Ghosh, T. S., & O’Toole, P. W. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.09.057 · Cited by 383 · see citing works
Gut microbiome composition may be an indicator of preclinical Alzheimer’s disease
Ferreiro, A. L., Choi, J., Ryou, J., Newcomer, E. P., Thompson, R., Bollinger, R. M., Hall-Moore, C. M., Ndao, I. M., Sax, L., Benzinger, T. L. S., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.abo2984 · Cited by 369 · see citing works
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 · see citing works
Elevated rates of horizontal gene transfer in the industrialized human microbiome
Groussin, M., Poyet, M., Sistiaga, A., Kearney, S. M., Moniz, K., Noel, M., Hooker, J., Gibbons, S. M., Ségurel, L., Froment, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.02.052 · Cited by 331 · see citing works
Defining Dysbiosis for a Cluster of Chronic Diseases
Wilkins, L. J., Monga, M., & Miller, A. W. (2019) · Scientific Reports · doi:10.1038/s41598-019-49452-y · Cited by 328 · see citing works
Microbiome 101: Studying, Analyzing, and Interpreting Gut Microbiome Data for Clinicians
Allaband, C., McDonald, D. T., Vázquez‐Baeza, Y., Minich, J. J., Tripathi, A., Brenner, D. A., Loomba, R. S., Smarr, L. L., Sandborn, W. J., Schnabl, B., et al. (2018) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2018.09.017 · Cited by 317 · see citing works
Self-citationAdvancing human gut microbiota research by considering gut transit time
Procházková, N., Falony, G., Dragsted, L. O., Licht, T. R., Raes, J., & Roager, H. M. (2022) · Gut · doi:10.1136/gutjnl-2022-328166 · Cited by 322 · see citing works
Targeting the gut microbiota for cancer therapy
Fernandes, M. R., Aggarwal, P., Costa, R. G. F., Cole, A. M., & Trinchieri, G. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00513-x · Cited by 290 · see citing works
Microbiota succession throughout life from the cradle to the grave
Martino, C., Dilmore, A. H., Burcham, Z. M., Metcalf, J. L., Jeste, D., & Knight, R. (2022) · Nature Reviews Microbiology · doi:10.1038/s41579-022-00768-z · Cited by 275 · see citing works
Self-citationMetagenomics-enabled microbial surveillance
Ko, K. K. K., Chng, K. R., & Nagarajan, N. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01089-w · Cited by 280 · see citing works
Identifying biases and their potential solutions in human microbiome studies
Nearing, J. T., Comeau, A. M., & Langille, M. G. I. (2021) · Microbiome · doi:10.1186/s40168-021-01059-0 · Cited by 240 · see citing works
Gut microbiota phenotypes of obesity
Stanislawski, M. A., Dabelea, D., Lange, L. A., Wagner, B. D., & Lozupone, C. (2019) · npj Biofilms and Microbiomes · doi:10.1038/s41522-019-0091-8 · Cited by 269 · see citing works
Ma, Z., Zuo, T., Frey, N., & Rangrez, A. Y. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01946-6 · Cited by 297 · see citing works
Nguyen, T. T., Kosciolek, T., Maldonado, Y., Daly, R. E., Martin, A. S., McDonald, D., Knight, R., & Jeste, D. V. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2018.09.014 · Cited by 260 · see citing works
Self-citationHughes, D. A., Bacigalupe, R., Wang, J., Rühlemann, M. C., Tito, R. Y., Falony, G., Joossens, M., Vieira‐Silva, S., Henckaerts, L., Rymenans, L., et al. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0743-8 · Cited by 242 · see citing works
Tito, R. Y., Chaffron, S., Caenepeel, C., Lima‐Mendez, G., Wang, J., Vieira‐Silva, S., Falony, G., Hildebrand, F., Darzi, Y., Rymenans, L., et al. (2018) · Gut · doi:10.1136/gutjnl-2018-316106 · Cited by 226 · see citing works
Roy, T. L., de Hase, E. M., Hul, M. V., Paquot, A., Pelicaen, R., Régnier, M., Depommier, C., Druart, C., Everard, A., Maiter, D. M., et al. (2021) · Gut · doi:10.1136/gutjnl-2020-323778 · Cited by 227 · see citing works
Vandeputte, D., Commer, L. D., Tito, R. Y., Kathagen, G., Sabino, J., Vermeire, S., Faust, K., & Raes, J. (2021) · Nature Communications · doi:10.1038/s41467-021-27098-7 · Cited by 217 · see citing works
You’ve got male: Sex and the microbiota-gut-brain axis across the lifespan
Jaggar, M., Rea, K., Spichak, S., Dinan, T. G., & Cryan, J. F. (2020) · Frontiers in Neuroendocrinology · doi:10.1016/j.yfrne.2019.100815 · Cited by 200 · see citing works
Taylor, B. C., Lejzerowicz, F., Poirel, M., Shaffer, J. P., Jiang, L., Aksenov, A., Litwin, N., Humphrey, G., Martino, C., Miller-Montgomery, S., et al. (2020) · mSystems · doi:10.1128/msystems.00901-19 · Cited by 203 · see citing works
Self-citationFaecalibacterium prausnitzii Attenuates CKD via Butyrate-Renal GPR43 Axis
Li, H., Xu, M., Xu, X., Tang, Y., Jiang, H., Li, L., Xia, W., Cui, N., Bai, J., Dai, Z.-M., et al. (2022) · Circulation Research · doi:10.1161/circresaha.122.320184 · Cited by 205 · see citing works
Synthetic ecology of the human gut microbiota
Vrancken, G., Gregory, A., Huys, G. R. B., Faust, K., & Raes, J. (2019) · Nature Reviews Microbiology · doi:10.1038/s41579-019-0264-8 · Cited by 192 · see citing works
Role of diet and its effects on the gut microbiome in the pathophysiology of mental disorders
Horn, J., Mayer, D. E., Chen, S., & Mayer, E. A. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01922-0 · Cited by 193 · see citing works
Yang, L., & Chen, J. (2022) · Microbiome · doi:10.1186/s40168-022-01320-0 · Cited by 181 · see citing works
Patterns of Oral Microbiota Diversity in Adults and Children: A Crowdsourced Population Study
Burcham, Z. M., Garneau, N. L., Comstock, S. S., Tucker, R. M., Knight, R., Metcalf, J. L., Scientists, G. O. T. L. C., Miranda, A., Reinhart, B., Meyers, D., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-59016-0 · Cited by 183 · see citing works
Self-citationSpatial, Temporal, and Phylogenetic Scales of Microbial Ecology
Ladau, J., & Eloe‐Fadrosh, E. A. (2019) · Trends in Microbiology · doi:10.1016/j.tim.2019.03.003 · Cited by 185 · see citing works
Self-citationTackmann, J., Rodrigues, J. F. M., & von Mering, C. (2019) · Cell Systems · doi:10.1016/j.cels.2019.08.002 · Cited by 175 · see citing works
Quantifying and Understanding Well-to-Well Contamination in Microbiome Research
Minich, J. J., Sanders, J. G., Amir, A., Humphrey, G. C., Gilbert, J. A., & Knight, R. D. (2019) · mSystems · doi:10.1128/msystems.00186-19 · Cited by 178 · see citing works
Self-citationMicronutrient Requirements and Sharing Capabilities of the Human Gut Microbiome
Rodionov, D. A., Arzamasov, A. A., Khoroshkin, M., Iablokov, S. N., Leyn, S. A., Peterson, S. N., Novichkov, P. S., & Osterman, A. L. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01316 · Cited by 177 · see citing works
Zhou, Q., Zhang, Y., Wang, X., Yang, R., Zhu, X., Zhang, Y., Chen, C., Yuan, H., Yang, Z., & Sun, L. (2020) · Nutrition & Metabolism · doi:10.1186/s12986-020-00516-1 · Cited by 175 · see citing works
Oliver, A., Chase, A. B., Weihe, C., Orchanian, S. B., Riedel, S. F., Hendrickson, C. L., Lay, M., Sewall, J. M., Martiny, J. B. H., & Whiteson, K. (2021) · mSystems · doi:10.1128/msystems.00115-21 · Cited by 171 · see citing works
Holmes, Z. C., Villa, M. M., Durand, H. K., Jiang, S., Dallow, E. P., Petrone, B. L., Silverman, J. D., Lin, P., & David, L. A. (2022) · Microbiome · doi:10.1186/s40168-022-01307-x · Cited by 177 · see citing works
Taxonomic signatures of cause-specific mortality risk in human gut microbiome
Salosensaari, A., Laitinen, V., Havulinna, A. S., Méric, G., Cheng, S. C., Perola, M., Valsta, L. M., Alfthan, G., Inouye, M., Watrous, J. D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22962-y · Cited by 168 · see citing works
Self-citationMicrobiota-Centered Interventions: The Next Breakthrough in Immuno-Oncology?
Derosa, L., Routy, B., Desilets, A., Daillère, R., Terrisse, S., Kroemer, G., & Zitvogel, L. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0236 · Cited by 170 · see citing works
Kaplan, R., Wang, Z., Usyk, M., Sotres-Alvarez, D., Daviglus, M., Schneiderman, N., Talavera, G., Gellman, M., Thyagarajan, B., Moon, J.-Y., et al. (2019) · Genome Biology · doi:10.1186/s13059-019-1831-z · Cited by 146 · see citing works
Self-citationHuman Skin, Oral, and Gut Microbiomes Predict Chronological Age
Huang, S., Haiminen, N., Carrieri, A.-P., Hu, R., Jiang, L., Parida, L., Russell, B., Allaband, C., Zarrinpar, A., Vázquez-Baeza, Y., et al. (2020) · mSystems · doi:10.1128/msystems.00630-19 · Cited by 167 · see citing works
Self-citationPursuing Human-Relevant Gut Microbiota-Immune Interactions
Spencer, S. P., Fragiadakis, G. K., & Sonnenburg, J. L. (2019) · Immunity · doi:10.1016/j.immuni.2019.08.002 · Cited by 160 · see citing works
A Guide to Diet-Microbiome Study Design
Johnson, A. J., Zheng, J. J., Kang, J. W., Saboe, A., Knights, D., & Zivkovic, A. M. (2020) · Frontiers in Nutrition · doi:10.3389/fnut.2020.00079 · Cited by 161 · see citing works
Self-citationRole of dietary fiber in promoting immune health—An EAACI position paper
Venter, C., Meyer, R. W., Greenhawt, M., Pali‐Schöll, I., Nwaru, B. I., Roduit, C., Untersmayr, E., Adel‐Patient, K., Agache, I. O., Agostoni, C. V., et al. (2022) · Allergy · doi:10.1111/all.15430 · Cited by 161 · see citing works
The multiple sclerosis gut microbiota: A systematic review
Mirza, A., Forbes, J. D., Zhu, F., Bernstein, C. N., Domselaar, G. V., Graham, M., Waubant, E. L., & Tremlett, H. L. (2019) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2019.101427 · Cited by 152 · see citing works
Lee, P. C., Yacyshyn, B., & Yacyshyn, M. B. (2018) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13561 · Cited by 153 · see citing works
Urban-associated diseases: Candidate diseases, environmental risk factors, and a path forward
Flies, E. J., Mavoa, S., Zosky, G. R., Mantzioris, E., Williams, C. R., Eri, R. D., Brook, B. W., & Buettel, J. C. (2019) · Environment International · doi:10.1016/j.envint.2019.105187 · Cited by 154 · see citing works
Towards modulating the gut microbiota to enhance the efficacy of immune-checkpoint inhibitors
Simpson, R. C., Shanahan, E. R., Scolyer, R. A., & Long, G. V. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00803-9 · Cited by 153 · see citing works
Perspective: Dietary Biomarkers of Intake and Exposure—Exploration with Omics Approaches
Maruvada, P., Lampe, J. W., Wishart, D. S., Barupal, D. K., Chester, D. N., Dodd, D., Djoumbou-Feunang, Y., Dorrestein, P. C., Dragsted, L. O., Draper, J., et al. (2019) · Advances in Nutrition · doi:10.1093/advances/nmz075 · Cited by 151 · see citing works
Self-citationAnalysis of the gut microbiome in dogs and cats
Suchodolski, J. S. (2021) · Veterinary Clinical Pathology · doi:10.1111/vcp.13031 · Cited by 153 · see citing works
Intestinal transgene delivery with native E. coli chassis allows persistent physiological changes
Russell, B. J., Brown, S. D., Siguenza, N., Mai, I., Saran, A. R., Lingaraju, A., Maissy, E. S., Dantas Machado, A. C., Pinto, A. F. M., Sanchez, C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.06.050 · Cited by 151 · see citing works
Self-citationEstimating the postmortem interval using microbes: Knowledge gaps and a path to technology adoption
Metcalf, J. L. (2018) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2018.11.004 · Cited by 147 · see citing works
Self-citationThe gut microbiome: what the oncologist ought to know
Lee, K. A., Luong, M., Shaw, H. M., Nathan, P., Bataille, V. A., & Spector, T. D. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01467-x · Cited by 151 · see citing works
Self-citationEffects of dietary fibre on metabolic health and obesity
Deehan, E. C., Mocanu, V., & Madsen, K. L. (2024) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-023-00891-z · Cited by 155 · see citing works
Machine learning for data integration in human gut microbiome
Li, P., Luo, H., Ji, B., & Nielsen, J. (2022) · Microbial Cell Factories · doi:10.1186/s12934-022-01973-4 · Cited by 150 · see citing works
Gao, M., Wang, J., Liu, P., Tu, H., Zhang, R., Zhang, Y., Sun, N., & Zhang, K. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02670-5 · Cited by 149 · see citing works
Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease
Aryal, S., Alimadadi, A., Manandhar, I., Joe, B., & Cheng, X. (2020) · Hypertension · doi:10.1161/hypertensionaha.120.15885 · Cited by 136 · see citing works
The microbiome and cancer for clinicians
Picardo, S. L., Coburn, B. A., & Hansen, A. R. (2019) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2019.06.004 · Cited by 135 · see citing works
Yoon, G., Gaynanova, I., & Müller, C. L. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00516 · Cited by 131 · see citing works
Toward an improved definition of a healthy microbiome for healthy aging
Ghosh, T. S., Shanahan, F., & O’Toole, P. W. (2022) · Nature Aging · doi:10.1038/s43587-022-00306-9 · Cited by 128 · see citing works
Minich, J. J., Poore, G. D., Jantawongsri, K., Johnston, C., Bowie, K., Bowman, J. P., Knight, R. D., Nowak, B. F., & Allen, E. E. (2020) · Applied and Environmental Microbiology · doi:10.1128/aem.00411-20 · Cited by 123 · see citing works
Self-citationDueholm, M. S., Andersen, K. S., McIlroy, S. J., Kristensen, J. M., Yashiro, E., Karst, S. M., Albertsen, M., & Nielsen, P. H. (2020) · mBio · doi:10.1128/mbio.01557-20 · Cited by 127 · see citing works
Changes in Gut Microbiota and Multiple Sclerosis: A Systematic Review
Ordoñez-Rodriguez, A., Román, P., Rueda‐Ruzafa, L., Campos-Ríos, A., & Cardona, D. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20054624 · Cited by 138 · see citing works
MolDiscovery: learning mass spectrometry fragmentation of small molecules
Cao, L., Güler, M., Tagirdzhanov, A., Lee, Y.-Y., Gurevich, A. A., & Mohimani, H. (2021) · Nature Communications · doi:10.1038/s41467-021-23986-0 · Cited by 119 · see citing works
Self-citationMinich, J. J., Härer, A., Vechinski, J., Frable, B. W., Skelton, Z. R., Kunselman, E., Shane, M. A., Perry, D. S., Gonzalez, A., McDonald, D. T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34557-2 · Cited by 116 · see citing works
Self-citationGenomic reconstruction of short-chain fatty acid production by the human gut microbiota
Frolova, M. S., Suvorova, I. A., Iablokov, S. N., Petrov, S. N., & Rodionov, D. A. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.949563 · Cited by 119 · see citing works
Integration of 168,000 samples reveals global patterns of the human gut microbiome
Abdill, R. J., Graham, S. P., Rubinetti, V., Ahmadian, M., Hicks, P., Chetty, A., McDonald, D., Ferretti, P., Gibbons, E., Rossi, M., et al. (2025) · Cell · doi:10.1016/j.cell.2024.12.017 · Cited by 130 · see citing works
Association Between the Gut Microbiota and Blood Pressure in a Population Cohort of 6953 Individuals
Palmu, J., Salosensaari, A., Havulinna, A. S., Cheng, S. C., Inouye, M., Jain, M., Salido, R. A., Sanders, K., Brennan, C., Humphrey, G., et al. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.016641 · Cited by 116 · see citing works
Self-citationChemically informed analyses of metabolomics mass spectrometry data with Qemistree
Tripathi, A., Vázquez‐Baeza, Y., Gauglitz, J. M., Wang, M., Dührkop, K., Nothias-Esposito, M., Acharya, D. D., Ernst, M., van der Hooft, J. J. J., Zhu, Q., et al. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-00677-3 · Cited by 115 · see citing works
Self-citationGut microbiome signatures reflect different subtypes of irritable bowel syndrome
Su, Q., Tun, H. M., Liu, Q., Yeoh, Y. K., Mak, J. W. Y., Chan, F. K. L., & Ng, S. C. (2022) · Gut Microbes · doi:10.1080/19490976.2022.2157697 · Cited by 114 · see citing works
Daylight exposure modulates bacterial communities associated with household dust
Fahimipour, A. K., Hartmann, E. M., Siemens, A., Kline, J., Levin, D., Wilson, H., Betancourt‐Román, C. M., Brown, G., Fretz, M., Northcutt, D., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0559-4 · Cited by 113 · see citing works
Self-citationZhu, C. S., Grandhi, R., Patterson, T. F., & Nicholson, S. E. (2018) · Brain Sciences · doi:10.3390/brainsci8060113 · Cited by 110 · see citing works
Striped UniFrac: enabling microbiome analysis at unprecedented scale
McDonald, D. T., Vázquez‐Baeza, Y., Koslicki, D., McClelland, J., Reeve, N., Xu, Z. Z., González, A., & Knight, R. D. (2018) · Nature Methods · doi:10.1038/s41592-018-0187-8 · Cited by 105 · see citing works
Self-citationCoker, J. K. C., Moyne, O., Rodionov, D. A., & Zengler, K. (2021) · Gut Microbes · doi:10.1080/19490976.2020.1869502 · Cited by 112 · see citing works
Nuzum, N. D., Loughman, A., Szymlek-Gay, E. A., Hendy, A., Teo, W.-P., & Macpherson, H. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.02.003 · Cited by 103 · see citing works
Effects of the microalgae Chlamydomonas on gastrointestinal health
Fields, F. J., Lejzerowicz, F., Schroeder, D., Ngoi, S. M., Tran, M., McDonald, D., Jiang, L., Chang, J. T., Knight, R., & Mayfield, S. (2020) · Journal of Functional Foods · doi:10.1016/j.jff.2019.103738 · Cited by 104 · see citing works
Self-citationReynoso-García, J., Miranda-Santiago, A. E., Meléndez-Vázquez, N. M., Acosta-Pagán, K. T., Rosado, M. R. S., Díaz-Rivera, J., Rosado-Quiñones, A. M., Acevedo-Márquez, L., Cruz-Roldán, L., Tosado-Rodríguez, E. L., et al. (2022) · Frontiers in Systems Biology · doi:10.3389/fsysb.2022.951403 · Cited by 107 · see citing works
- Authors
- McDonalda, D., Hyde, E., Debelius, J. W., Morton, J. T., Gonzalez, A., Ackermann, G., Aksenov, A. A., Behsaz, B., Brennan, C., Chen, Y., Goldasich, L. D., Dorrestein, P. C., Dunn, R. R., Fahimipour, A. K., Gaffney, J., Gilbert, J. A., Gogul, G., Green, J. L., Hugenholtz, P., Humphrey, G., Huttenhower, C., Jackson, M. A., Janssen, S., Jeste, D. V., Jiang, L., Kelley, S. T., Knights, D., Kosciolek, T., Ladau, J., Leach, J., Marotz, C., Meleshko, D., Melnik, A. V., Metcalf, J. L., Mohimani, H., Montassier, E., Navas-Molina, J., Nguyen, T. T., Peddada, S., Pevzner, P., Pollard, K. S., Rahnavard, G., Robbins-Pianka, A., Sangwan, N., Shorenstein, J., Smarr, L., Song, S. J., Spector, T., Swafford, A. D., Thackray, V. G., Thompson, L. R., Tripathi, A., Vazquez-Baeza, Y., Vrbanac, A., Wischmeyer, P., Wolfe, E., Zhu, Q., The American Gut Consortium, & Knight, R.
- Affiliations
- University of California San Diego (US)
- San Diego State University (US)
- North Carolina State University (US)
- University of Oregon (US)
- Argonne National Laboratory (US)
- The University of Queensland (AU)
- Broad Institute (US)
- King's College London (GB)
- University of Minnesota (US)
- Gladstone Institutes (US)
- St Petersburg University (RU)
- Colorado State University (US)
- Carnegie Mellon University (US)
- University of Pittsburgh (US)
- National Oceanic and Atmospheric Administration (US)
- Duke University (US)
- Corresponding author
- Knight, Rob
- ORCID
- McDonalda, Daniel
- Debelius, Justine W.
- Morton, James T.
- Ackermann, Gail
- Aksenov, Alexander A.
- Brennan, Caitriona
- Chen, Yingfeng
- Goldasich, Lindsay DeRight
- Dorrestein, Pieter C.
- Dunn, Robert R.
- Fahimipour, Ashkaan K.
- Gaffney, James
- Gilbert, Jack A
- Gogul, Grant
- Hugenholtz, Philip
- Huttenhower, Curtis
- Jackson, Matthew A.
- Janssen, Stefan
- Jeste, Dilip V.
- Jiang, Lingjing
- Kelley, Scott T.
- Knights, Dan
- Ladau, Joshua
- Leach, Jeff
- Marotz, Clarisse
- Meleshko, Dmitry
- Melnik, Alexey V.
- Metcalf, Jessica L.
- Mohimani, Hosein
- Montassier, Emmanuel
- Navas-Molina, Jose
- Nguyen, Tanya T.
- Peddada, Shyamal
- Pevzner, Pavel
- Pollard, Katherine S.
- Sangwan, Naseer
- Shorenstein, Joshua
- Smarr, Larry
- Song, Se Jin
- Swafford, Austin D.
- Thackray, Varykina G.
- Thompson, Luke R.
- Tripathi, Anupriya
- Vrbanac, Alison
- Wischmeyer, Paul
- Wolfe, Elaine
- Zhu, Qiyun
- Knight, Rob
- Venue
- mSystems
- Publisher
- openRxiv
- Published
- 2018-03-07
- Volume
- 3
- Issue
- 3
- ISSN
- 2379-5077 (ISSN-L)
- PMC
- PMC5954204
- OpenAlex
- W2794407684
- Cited by
- 878
- References
- 121
- Citations by year
- 2026: 59 · 2025: 114 · 2024: 106 · 2023: 100 · 2022: 137 · 2021: 132 · 2020: 108 · 2019: 90 · 2018: 30 · 2013: 1
- FWCI
- 32.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Locations
- mSystems (publishedVersion, public-domain)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (publishedVersion, other-oa)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion, public-domain)
- NOAA Institutional Repository (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, public-domain)
- Digital Access to Libraries (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- National Science Foundation (1144807, DE-AC02–06CH11357, 1053575)
- U.S. Department of Energy (DE‐ AC02‐06CH11357, ACI-1053575, AC02‐06CH11357, 06CH11357, DE-AC02-)
- Alfred P. Sloan Foundation
- Wellcome Trust
- San Diego Supercomputer Center
- National Institute for Health and Care Research (NF-SI-0507-10228, NF-SI-0514-10027)
- King's College London
- Russian Science Foundation (14‐50‐00069, 14-50-)
- Société Nationale Française de Gastro-Entérologie
- Walloon excellence in life sciences and biotechnology
- University of Colorado Boulder (DE-AC02-06CH11357)
- University of California, San Diego (DE-AC02-06CH11357)
- Hansjörg Wyss Institute for Biologically Inspired Engineering, Harvard University
- Medical Research Council (MR/N030125/1, MR/N01183X/1)
- National Institute of General Medical Sciences (T32GM007752)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK042086, P30DK043351)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- citizen science, microbiome research, untargeted metabolomics, gut microbiota
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