Professional Education

  • Doctor of Philosophy, Stanford University, MI-PHD (2016)
  • Master of Science, Smith College, Biological Sciences (2007)
  • Bachelor of Arts, Hampshire College, Microbiology & Literature (2005)

Research & Scholarship

Lab Affiliations


All Publications

  • The Landscape Ecology and Microbiota of the Human Nose, Mouth, and Throat CELL HOST & MICROBE Proctor, D. M., Relman, D. A. 2017; 21 (4): 421-432


    Landscape ecology examines the relationships between the spatial arrangement of different landforms and the processes that give rise to spatial and temporal patterns in local community structure. The spatial ecology of the microbial communities that inhabit the human body-in particular, those of the nose, mouth, and throat-deserves greater attention. Important questions include what defines the size of a population (i.e., "patch") in a given body site, what defines the boundaries of distinct patches within a single body site, and where and over what spatial scales within a body site are gradients detected. This Review looks at the landscape ecology of the upper respiratory tract and mouth and seeks greater clarity about the physiological factors-whether immunological, chemical, or physical-that govern microbial community composition and function and the ecological traits that underlie health and disease.

    View details for DOI 10.1016/j.chom.2017.03.011

    View details for Web of Science ID 000398896100004

    View details for PubMedID 28407480

  • REPRODUCIBLE RESEARCH WORKFLOW IN R FOR THE ANALYSIS OF PERSONALIZED HUMAN MICROBIOME DATA. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Callahan, B., Proctor, D., Relman, D., Fukuyama, J., Holmes, S. 2016; 21: 183-194


    This article presents a reproducible research workflow for amplicon-based microbiome studies in personalized medicine created using Bioconductor packages and the knitr markdown interface.We show that sometimes a multiplicity of choices and lack of consistent documentation at each stage of the sequential processing pipeline used for the analysis of microbiome data can lead to spurious results. We propose its replacement with reproducible and documented analysis using R packages dada2, knitr, and phyloseq. This workflow implements both key stages of amplicon analysis: the initial filtering and denoising steps needed to construct taxonomic feature tables from error-containing sequencing reads (dada2), and the exploratory and inferential analysis of those feature tables and associated sample metadata (phyloseq). This workow facilitates reproducible interrogation of the full set of choices required in microbiome studies. We present several examples in which we leverage existing packages for analysis in a way that allows easy sharing and modification by others, and give pointers to articles that depend on this reproducible workflow for the study of longitudinal and spatial series analyses of the vaginal microbiome in pregnancy and the oral microbiome in humans with healthy dentition and intra-oral tissues.

    View details for PubMedID 26776185