My lab applies and develops bioinformatics methods for analyzing molecular datasets (16S, metagenomics, metabolomics..etc.) generated from complex microbial communities. Using a combination of phylogenetic methods, compositional data analysis, statistical analysis and machine learning, we investigate the relationship of bacterial, fungal and viral diveristy to biological and physical conditions. Ongoing areas of research include:


Human environments provide fascinating and complex habitats for microbial diversity. Despite the fact that Westerners spend approximately 90% of their time indoors, we know little about the diversity of microbes in these environments. Our studies of hospitals, daycare centers, therapeutic pools, shower curtains and airplanes have shown human environments contain a rich mixture of environmental (soil, water) and human-associated microbes. Moreover, each of the artificial environments appears to select and enrich for particular groups of microbes depending on physical and chemical conditions. Currently, we are using deep-sequencing, quantitative profiling and longitudinal study designs to investigate the influence of moisture and building material composition on bacterial and fungal community diversity, growth and development.

Representative Publications:

Xu, Y. et al. Quantitative profiling of built environment bacterial and fungal communities reveals dynamic material dependent growth patterns and microbial interactions. Indoor Air ina.12727 (2020).

Prussin, A. J. et al. Seasonal dynamics of DNA and RNA viral bioaerosol communities in a daycare center. Microbiome 7, 53 (2019).

Lax, S. et al. Microbial and metabolic succession on common building materials under high humidity conditions. Nat. Commun. 10, 1767 (2019).

Fouquier, J., Schwartz, T. & Kelley, S. T. Rapid assemblage of diverse environmental fungal communities on public restroom floors. Indoor Air 26, 869–879 (2016).

Kelley, S. T. & Gilbert, J. A. Studying the microbiology of the indoor environment. Genome Biol. 14, 202 (2013).


In collaboration with my colleague Dr. Varykina G. Thackray at UC San Diego, we are studying the relationship and potential role of the gut microbiome in Polycystic Ovary Syndrome (PCOS). PCOS is the most common endocrine disorder in women, with a prevalence of 10-15%. In addition to a reproductive phenotype that results in increased incidences of infertility, miscarriage and pregnancy complications, a majority of women with PCOS have metabolic abnormalities and an increased risk of obesity, type 2 diabetes, gestational diabetes, and hypertension. The main objective of our research is to understand the role of the gut microbiome, and its interactions with host physiology and chemistry, in the pathophysiology of PCOS.

Representative Publications:

Torres, P. J. et al. Exposure to a Healthy Gut Microbiome Protects Against Reproductive and Metabolic Dysregulation in a PCOS Mouse Model. Endocrinology 160, 1193–1204 (2019).

de la Cuesta-Zuluaga, J. et al. Age- and Sex-Dependent Patterns of Gut Microbial Diversity in Human Adults. mSystems 4, e00261-19 (2019).

Torres, P. J. et al. Gut Microbial Diversity in Women With Polycystic Ovary Syndrome Correlates With Hyperandrogenism. J. Clin. Endocrinol. Metab. 103, 1502–1511 (2018).

Kosnicki, K. L. et al. Effects of moderate, voluntary ethanol consumption on the rat and human gut microbiome. Addict. Biol. (2018) doi:10.1111/adb.12626.

Kelley, S. T., Skarra, D. V., Rivera, A. J. & Thackray, V. G. The Gut Microbiome Is Altered in a Letrozole-Induced Mouse Model of Polycystic Ovary Syndrome. PLoS One 11, e0146509 (2016).


My lab also develops and applies bioinformatics and statistical software tools for analyzing high-throughput multiomics data (e.g., 16S, metagenomics and metabolomics) from complex microbial communities.

Representative Publications:

Sisk-Hackworth, L. & Kelley, S. T. An application of compositional data analysis to multiomic time-series data. NAR Genomics Bioinforma. 2, lqaa079 (2020).

McGhee, J. J. et al. Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics. PeerJ 8, e8783 (2020).

Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology vol. 37 852–857 (2019).

Califf, K. J. et al. Multi-omics Analysis of Periodontal Pocket Microbial Communities Pre- and Posttreatment. mSystems 2, e00016-17 (2017).

Knights, D. et al. Bayesian community-wide culture-independent microbial source tracking. Nat. Methods 8, 761–763 (2011).