Bioinformatic Tools and Methods

Microbial communities cannot be observed directly. We use molecular techniques to measure DNA, RNA, protein, lipids, and small molecules, which allow us to infer the form and function of microbial systems. In particular, we employ high-throughput DNA sequencing to quantify the taxonomic and functional content of a microbial community. These data are highly complex, containing many zeros (i.e. sparse), in addition to several forms of technical, sampling, and biological biases/noise that are difficult to disentangle. In order to form an accurate picture of these communities, novel bioinformatic and statistical techniques are required. We develop tools and techniques for dealing with batch effects, compositionality, sparsity, and other issues that can hamper analyses. Furthermore, we try to integrate our tools into open source software packages, like QIIME2, so that they are accessible to the rest of the research community. Please visit the lab github page for more details.

Selected Publications

  • Diener, C., Gibbons, S.M., Resendis-Antonio, O. 2020. MICOM: metagenome-scale modeling to infer metabolic interactions in the gut microbiota. mSystems,
  • Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet, C.C., Al-Ghalith, G.A., Alexander, H., Alm, E.J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J.E., Bittinger, K., Brejnrod, A., Brislawn, C.J.,Brown, C.T., Callahan, B.J., Caraballo-Rodríguez, A.M., Chase, J., Cope, E.K., Da Silva, R., Diener, C., Dorrestein, P.C., Douglas, G.M., Durall, D.M., Duvallet, C., Edwardson, C.F., Ernst, M., Estaki, M., Fouquier, J., Gauglitz, J.M., Gibbons, S.M., Gibson, D.L., Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes, S., Holste, H., Huttenhower, C., Huttley, G.A., Janssen, S., Jarmusch, A.K., Jiang, L., Kaehler, B.D., Kang, K.B., Keefe, C.R., Keim, P., Kelley, S.T., Knights, D., Koester, I., Kosciolek, T., Kreps, J., Langille, M.G. I., Lee, J., Ley, R., Liu, Y., Loftfield, E., Lozupone, C., Maher, M., Marotz, C., Martin, B.D., McDonald, D., McIver, L.J., Melnik, A.V., Metcalf, J.L., Morgan, S.C., Morton, J.T., Naimey, A.T., Navas-Molina, J.A., Nothias, L.F., Orchanian, S.B.,Pearson, T., Peoples, S.L., Petras, D., Preuss, M.L., Pruesse, E., Rasmussen, L.B., Rivers, A., Robeson, M.S., Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha, R., Song, S.J., Spear, J.R., Swafford, A.D., Thompson, L.R., Torres, P.J., Trinh, P., Tripathi, A., Turnbaugh, P.J., Ul-Hasan, S., van der Hooft, J.J.J., Vargas, F., Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters, W., Wan, Y., Wang, M., Warren, J., Weber, K.C., Williamson, C.H.D., Willis, A.D., Xu, Z.Z., Zaneveld, J.R., Zhang, Y., Zhu, Q., Knight, R., and Caporaso, J.G. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology,
  • Carr, A., Diener, C., Baliga, N.S., and Gibbons, S.M. 2019. Use and abuse of correlation analyses in microbial ecology. ISME Journal,
  • Gibbons, S.M., Duvallet, C., Alm, E.J. 2018. Correcting for batch effects in case-control microbiome studies. PloS Computational Biology,
  • Duvallet, C., Gibbons, S.M., Gurry, T., Irizarry, R. and Alm, E.J. 2017. Meta-analysis of microbiome studies reveals disease-specific and shared responses. Nature Communications, 1784 (2017), doi:10.1038/s41467-017-01973-8
  • Gibbons, S.M., Kearney, S.M., Smillie, C.S., and Alm, E.J. 2017. Two dynamic regimes in the human gut microbiome. PloS Computational Biology,
  • Gibbons, S.M., 2015. Statistical Tools for Data Analysis. Hydrocarbon and Lipid Microbiology Protocols. Springer Protocols Handbooks, Humana Press
  • Lekberg, Y., Gibbons, S.M. and Rosendahl, S. 2014. Will different OTU delineation methods change interpretation of arbuscular mycorrhizal fungal community patterns? New Phytologist, 202(4), pp.1101-1104
  • Rideout, J.R., He, Y., Navas-Molina, J.A., Walters, W.A., Ursell, L.K., Gibbons, S.M., Chase, J., McDonald, D., Gonzalez, A., Robbins-Pianka, A. and Clemente, J.C. 2014. Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences. PeerJ, 2, p.e545
  • Larsen, P.E., Gibbons, S.M. and Gilbert, J.A. 2012. Modeling microbial community structure and functional diversity across time and space. FEMS Microbiology Letters, 332(2), pp.91-98

Selected Preprints


Featured Projects

  • Near-Causal Inference in Multi-omic Data

    Many diseases are associated with changes of the microbial composition in our gut. However, we are just beginning to understand the relationships between the bacteria living in our intestinal system and the physiology and health of our bodies. One of the particular challenges we face is determining the causal direction for host-microbial associations. For instance, we may observe that individuals with diabetes have a greater abundance of a particular bacterium…

  • Microbiome Stress Project

    We have joined researchers at Duke University, the University of New Hampshire, and Montana State University to conduct a large-scale meta-analysis of how environmental stressors impact microbial communities. Prior surveys, like the Earth and Human Microbiome Projects, have established a baseline for healthy ecosystems across the planet. The Microbiome Stress Project will focus on ecological resistance and resilience of natural microbial communities to disturbances. The meta-analysis encompasses hundreds of studies…

  • 100K Wellness Project

    We are collecting dense, dynamic molecular phenotypes from 100,000 people over the next several years. This large cross-disciplinary endeavor, called the 100K Wellness Project, spans multiple labs at ISB. We will integrate measurements of human physiology, immune function, and diet with measurements of the composition and functional potential of the gut microbiome. By tracking people undergoing disease transitions, we will build a set of hypotheses for how shifts in diet…