A major challenge of analyzing the compositional structure of microbiome data is identifying its potential origins. Here, we introduce fast expectation-maximization microbial source tracking (FEAST), a ready-to-use scalable framework that can simultaneously estimate the contribution of thousands of potential source environments in a timely manner, thereby helping unravel the origins of complex microbial communities (https://github.com/cozygene/FEAST). The information gained from FEAST may provide insight into quantifying contamination, tracking the formation of developing microbial communities, as well as distinguishing and characterizing bacteria-related health conditions.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
All of the datasets analyzed in this paper are public and can be referenced at the following accession numbers: The first dataset was collected and studied by Backhed et al.16 (accession number ERP005989). The second dataset was collected and studied by Lax et al.15 (accession number ERP005806). The third dataset was collected and studied by Knights et al.10 (data from this study are stored in https://github.com/danknights/sourcetracker). The fourth dataset was collected and studied by McDonald et al.12 (accession number ERP012810) and the American Gut Project30 (EBI project number PRJEB11419). The fifth dataset was collected and studied by Taur et al.18 (data from this study are stored in http://www.ncbi.nlm.nih.gov/sra). In our simulations we used the Earth microbiome project (ftp://ftp.microbio.me/emp/release1/otu_tables/closed_ref_greengenes/).
Code is available at https://github.com/cozygene/FEAST
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
Kau, A. L., Ahern, P. P., Griffin, N. W., Goodman, A. L. & Gordon, J. I. Human nutrition, the gut microbiome and the immune system. Nature 474, 327–336 (2011).
Turnbaugh, P. J. & Gordon, J. I. The core gut microbiome, energy balance and obesity. J. Physiol. 587, 4153–4158 (2009).
Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).
Simpson, J. M., Santo Domingo, J. W. & Reasoner, D. J. Microbial source tracking: state of the science. Environ. Sci. Technol. 36, 5279–5288 (2002).
Wu, C. H. et al. Characterization of coastal urban watershed bacterial communities leads to alternative community-based indicators. PLoS ONE 5, e11285 (2010).
Greenberg, J., Price, B. & Ware, A. Alternative estimate of source distribution in microbial source tracking using posterior probabilities. Water Res. 44, 2629–2637 (2010).
Dufrêne, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Smith, A., Sterba-Boatwright, B. & Mott, J. Novel application of a statistical technique, Random Forests, in a bacterial source tracking study. Water Res. 44, 4067–4076 (2010).
Knights, D. et al. Bayesian community-wide culture-independent microbial source tracking. Nat. Methods 8, 761–763 (2011).
Devane, M. L., Weaver, L., Singh, S. K. & Gilpin, B. J. Fecal source tracking methods to elucidate critical sources of pathogens and contaminant microbial transport through New Zealand agricultural watersheds—a review. J. Environ. Manag. 222, 293–303 (2018).
McDonald, D. et al. Extreme dysbiosis of the microbiome in critical illness. mSphere 1, pii: e00199-16 (2016).
Dominguez-Bello, M. G. et al. Partial restoration of the microbiota of cesarean-born infants via vaginal microbial transfer. Nat. Med. 22, 250–253 (2016).
Teaf, C. M., Flores, D., Garber, M. & Harwood, V. J. Toward forensic uses of microbial source tracking. Microbiol. Spectr. 6, https://doi.org/10.1128/microbiolspec.EMF-0014-2017 (2018).
Lax, S. et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345, 1048–1052 (2014).
Backhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 690–703 (2015).
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
Taur, Y. et al. Intestinal domination and the risk of bacteremia in patients undergoing allogeneic hematopoietic stem cell transplantation. Clin. Infect. Dis. 55, 905–914 (2012).
Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).
Ley, R. E. Obesity and the human microbiome. Curr. Opin. Gastroenterol. 26, 5–11 (2010).
Turnbaugh, P. J., Bäckhed, F., Fulton, L. & Gordon, J. I. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3, 213–223 (2008).
Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005).
Koren, O. et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc. Natl Acad. Sci. USA 108, 4592–4598 (2011).
Clemente, J. C., Ursell, L. K., Parfrey, L. W. & Knight, R. The impact of the gut microbiota on human health: an integrative view. Cell 148, 1258–1270 (2012).
Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).
Clarke, S. F. et al. The gut microbiota and its relationship to diet and obesity: new insights. Gut Microbes 3, 186–202 (2012).
Jeffery, I. B., Quigley, E. M. M., Öhman, L., Simrén, M. & O’Toole, P. W. The microbiota link to irritable bowel syndrome: an emerging story. Gut Microbes 3, 572–576 (2012).
Marchesi, J. R. et al. Towards the human colorectal cancer microbiome. PLoS ONE 6, e20447 (2011).
Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).
McDonald, D. et al. American Gut: an open platform for citizen science microbiome research. mSystems 3, e00031-18 (2018).
Moon, T. K. The expectation-maximization algorithm. IEEE Signal Process. Mag. 13, 47–60 (1996).
Silverman, J. D., Shenhav, L., Halperin, E. A., Mukherjee, S. A. & David, L. A. Statistical considerations in the design and analysis of longitudinal microbiome studies. Preprint at bioRxiv: https://doi.org/10.1101/448332 (2018).
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
Tang, H., Peng, J., Wang, P. & Risch, N. J. Estimation of individual admixture: analytical and study design considerations. Genet. Epidemiol. 28, 289–301 (2005).
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).
Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18 (2012).
Deloger, M., El Karoui, M. & Petit, M.-A. A genomic distance based on MUM indicates discontinuity between most bacterial species and genera. J. Bacteriol. 191, 91–99 (2009).
Leung, H. C. M. et al. A robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio. Bioinformatics 27, 1489–1495 (2011).
Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009).
Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).
We thank S. Mukherjee for insightful comments on the manuscript. This research was partially supported by European Research Council under the European Union’s Horizon 2020 research and innovation program, project number 640384. This work was partially supported by the National Science Foundation (grant number 1705197). T.A.J. was supported by National Science Foundation (grant no. DGE-1644869).
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
Supplementary Figure 1 The accuracy of FEAST and SourceTracker using data-driven synthetic mixtures.
The accuracy of FEAST and SourceTracker on simulated data. Each simulation was performed using 10 real source environments and simulated sinks. The x-axis is average Jensen-Shannon divergence value across known sources. The y-axis represents correlation across all source environments between true and estimated mixing proportions, measured by (a) the squared Pearson correlation coefficient averaged across sources, and (b) the squared Spearman correlation coefficient averaged across sources.
Supplementary Figure 2 Evaluation of FEAST and SourceTracker through varying levels of sequencing depth.
Evaluation of FEAST and SourceTracker through varying levels of sequencing depth. Similarity of sequences remained constant (Jensen-Shannon divergence = 0.95, trivial to disambiguate), while sequencing depth was set to vary in the range 100–10,000.
The expected variance in FEAST's output using the dataset from McDonald et al. We used the gut microbiome of one, randomly selected, ICU patient as a sink, and the sources considered by McDonald et al.: 126 healthy controls, 126 samples of mammalian corpse decomposition, 126 samples of the gut from healthy children, and 126 samples from indoor house surfaces. By repeating this analysis 100 times and calculating the standard deviation of each source we demonstrate that the variance in FEAST’s output is very small (that is., sd(dust) = 7.7e-05, sd(healthy adults' feces) = 0.01, sd(healthy children's feces) = 0.01,sd(soil) = 5e-05, sd(unknown) = 8.5e-05).
The effect of noisy samples among sources on prediction accuracy (that is., estimation of the known and unknown sources). As we increase the number of samples per source, FEAST’s prediction accuracy improves, however this effect is moderate (squared Pearson correlation ranges from 0.9–0.99, Jensen-Shannon divergence values range from 0.87–0.92).
SourceTracker estimations of source contribution (the gut microbiome of mother, infant at 4 months and infant at birth) to the gut microbiome of 12-month-old infants. According to SourceTracker differences between C-section (n = 15) and Vaginally-delivered (n = 83) infants in terms of maternal contribution are not significant (two-sided t-test p-value = 0.6408). Box plots indicate the median (central lines), interquartile range (hinges), and the 5th and 95th percentiles (whiskers).
FEAST and SourceTracker report consistent proportions of contamination, despite minor discrepancies in a lab-setting (left: keyboard, right: Counter). Estimates on the top row were reported by SourceTracker and estimates on the bottom row were reported by FEAST.
Supplementary Figure 7 Gut microbiome samples from ICU patients are not reminiscent of gut samples from healthy individuals.
Gut samples from ICU patients are not reminiscent of gut samples from healthy individuals. We used the gut microbiome of each ICU patient (at discharge or after 10 days) as a sink, and the sources considered by the original study (McDonald et al. 2016): 126 samples from the American Gut Project (healthy controls), 126 samples of mammalian corpse decomposition, 126 samples of the gut from healthy children (Global Gut study), and 126 samples from indoor house surfaces.
Supplementary Figure 8 Unknown source distribution across sink samples (ICU patients vs. healthy individuals).
The distribution of the unknown source across sink samples—healthy individuals and ICU patients (n = 100).
The receiver operating characteristic curve (ROC curve) using FEAST, Weighted UniFrac, Bray-curtis and Jensen Shannon divergence to classify healthy individuals and ICU patients with dysbiosis. FEAST AUC = 0.91, Weighted UniFrac AUC = 0.78, Jensen Shannon divergence AUC = 0.87, Bray-curtis AUC = 0.86.
Distribution of the median random maternal rank in two scenarios: (a) all maternal and early infant samples (from all the infants in the study) were considered as potential sources (n = 293 sources), and (b) only the maternal samples were considered as potential sources (n = 98 sources). In both scenarios samples taken from infants at age 12 months were considered as sinks (n = 98 sinks). The red vertical line in each figure corresponds to the actual median rank of the maternal contribution.
About this article
Cite this article
Shenhav, L., Thompson, M., Joseph, T.A. et al. FEAST: fast expectation-maximization for microbial source tracking. Nat Methods 16, 627–632 (2019). https://doi.org/10.1038/s41592-019-0431-x
Microbiomes of air dust collected during the ground-based closed bioregenerative life support experiment "Lunar Palace 365"
Environmental Microbiome (2022)
Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches
Genome Medicine (2022)
Gut microbiota of ring-tailed lemurs (Lemur catta) vary across natural and captive populations and correlate with environmental microbiota
Animal Microbiome (2022)
A highly conserved core bacterial microbiota with nitrogen-fixation capacity inhabits the xylem sap in maize plants
Nature Communications (2022)
Scientific Reports (2022)