Browsing by Subject "metagenomics"

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Open Access
A phylogenetic transform enhances analysis of compositional microbiota data.
(Elife, 2017-02-15) Silverman, Justin D; Washburne, Alex D; Mukherjee, Sayan; David, Lawrence A
Surveys of microbial communities (microbiota), typically measured as relative abundance of species, have illustrated the importance of these communities in human health and disease. Yet, statistical artifacts commonly plague the analysis of relative abundance data. Here, we introduce the PhILR transform, which incorporates microbial evolutionary models with the isometric log-ratio transform to allow off-the-shelf statistical tools to be safely applied to microbiota surveys. We demonstrate that analyses of community-level structure can be applied to PhILR transformed data with performance on benchmarks rivaling or surpassing standard tools. Additionally, by decomposing distance in the PhILR transformed space, we identified neighboring clades that may have adapted to distinct human body sites. Decomposing variance revealed that covariation of bacterial clades within human body sites increases with phylogenetic relatedness. Together, these findings illustrate how the PhILR transform combines statistical and phylogenetic models to overcome compositional data challenges and enable evolutionary insights relevant to microbial communities.
Open Access
Current State of and Future Opportunities for Prediction in Microbiome Research: Report from the Mid-Atlantic Microbiome Meet-up in Baltimore on 9 January 2019.
(mSystems, 2019-10) Sakowski, Eric; Uritskiy, Gherman; Cooper, Rachel; Gomes, Maya; McLaren, Michael R; Meisel, Jacquelyn S; Mickol, Rebecca L; Mintz, C David; Mongodin, Emmanuel F; Pop, Mihai; Rahman, Mohammad Arifur; Sanchez, Alvaro; Timp, Winston; Vela, Jeseth Delgado; Wolz, Carly Muletz; Zackular, Joseph P; Chopyk, Jessica; Commichaux, Seth; Davis, Meghan; Dluzen, Douglas; Ganesan, Sukirth M; Haruna, Muyideen; Nasko, Dan; Regan, Mary J; Sarria, Saul; Shah, Nidhi; Stacy, Brook; Taylor, Dylan; DiRuggiero, Jocelyne; Preheim, Sarah P
Accurate predictions across multiple fields of microbiome research have far-reaching benefits to society, but there are few widely accepted quantitative tools to make accurate predictions about microbial communities and their functions. More discussion is needed about the current state of microbiome analysis and the tools required to overcome the hurdles preventing development and implementation of predictive analyses. We summarize the ideas generated by participants of the Mid-Atlantic Microbiome Meet-up in January 2019. While it was clear from the presentations that most fields have advanced beyond simple associative and descriptive analyses, most fields lack essential elements needed for the development and application of accurate microbiome predictions. Participants stressed the need for standardization, reproducibility, and accessibility of quantitative tools as key to advancing predictions in microbiome analysis. We highlight hurdles that participants identified and propose directions for future efforts that will advance the use of prediction in microbiome research.