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

• Lim, J.J., Diener, C., Wilson, J., Valenzuela, J.J., Baliga, N.S., Gibbons, S.M. 2023. Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes. Nature Communications, https://www.nature.com/articles/s41467-023-41424-1
• Diener, C., Gibbons, S.M. 2023. More is different: metabolic modeling of diverse microbial communities. mSystems, https://doi.org/10.1128/msystems.01270-22
• Diener, C., Gibbons, S.M., Resendis-Antonio, O. 2020. MICOM: metagenome-scale modeling to infer metabolic interactions in the gut microbiota. mSystems, https://msystems.asm.org/content/5/1/e00606-19
• 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, https://doi.org/10.1038/s41587-019-0209-9
• Carr, A., Diener, C., Baliga, N.S., and Gibbons, S.M. 2019. Use and abuse of correlation analyses in microbial ecology. ISME Journal, https://doi.org/10.1038/s41396-019-0459-z
  • ReadCube Link: rdcu.be/bH0Ue
• Gibbons, S.M., Duvallet, C., Alm, E.J. 2018. Correcting for batch effects in case-control microbiome studies. PloS Computational Biology, https://doi.org/10.1371/journal.pcbi.1006102
• 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, http://dx.doi.org/10.1371/journal.pcbi.1005364
• 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