Microbial communities cannot be observed directly. We use molecular techniques to measure DNA, RNA, protein, and lipids, 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 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 can be accessible to the rest of the research community. Please visit the lab github page for more details.
- 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., 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