We are interested in developing and utilizing new experimental and computational methods in order to understand microbial communities. Broadly, we are interested in the following areas of research:
- developing and using experimental and bioinformatics tools that better characterize microbial diversity from high-throughput sequencing data
- developing and using bioinformatics tools for genome assembly and binning of large shotgun metagenomic datasets
- understanding genomic diversity, molecular biology, and evolutionary history of microbes from shotgun metagenomic sequencing
- understanding the genomic determinants of ecosystem-scale processes within microbial communities
We have a particular focus on archaeal diversity and the role of archaea in microbial communities. We work with a variety of world-class collaborators on specific experimental systems of interest. Some of these projects are listed below. Please also read our publications for more information.
Determinants of microbial contributions to methane emissions in freshwater wetlands
Of the three most potent greenhouse gases, methane emissions are the most difficult to predict. Despite being overall carbon sinks, wetlands represent the largest single source of atmospheric methane. Yet, relatively little is known about the microbial taxa and pathways responsible for methane flux in freshwater wetlands. Together with the Wrighton lab at Colorado State University and many other great collaborators, we are generating a high-resolution census of freshwater wetland microbial communities, reconstructing the major metabolisms of microbial communities in freshwater wetlands sediments, and aim to understand and ultimately predict how these communities and metabolisms respond to changing climate, hydrology, and variations in available carbon. This project is producing a large amount of marker gene, metagenomic, and transcriptomic sequencing across all domains of life. We are working on methods to simultaneously integrate disparate data types from Bacteria, Fungi, Viruses, and especially the understudied Archaea, in order to understand and predict ecosystem-scale processes in freshwater wetlands.
Archaeal diversity and evolution
New sequencing and computational approaches have allowed us to begin to better understand the vast biodiversity, ecosystem roles, and evolutionary history of archaea. We are interested in using amplicon and shotgun metagenomic sequencing data to shed light on the still relatively understudied archaea. Check out our recent paper, “Complex evolutionary history of translation Elongation Factor 2 and diphthamide biosynthesis in Archaea and parabasalids,” for an example of the kinds of questions that deep sequencing and comparative genomics can answer.
Collaborators: Thijs Ettema, Uppsala University; Anja Spang, NIOZ, Royal Netherlands Institute for Sea Research; Brett Baker, UT Austin
The Golden Retriever Microbiome
The Golden Retriever Lifetime Study is following more than 3,000 golden retrievers throughout their lifetimes, mining a large amount of health, environmental and behavior data for associations with canine disease, especially cancer. We are partnering with the Morris Animal Foundation to collect data on the canine fecal microbiome, and analyzing this data in the context of the larger study.
Collaborators: Missy Simpson, Morris Animal Foundation
The role of the microbiome in modulating effects of environmental pollutant exposure
Many compounds are discharged from wastewater and agricultural effluents into the environment, with the potential to alter the gut microbiome in fish, other wildlife, companion animals, and humans. The nature of these alterations and the potential effects on host organisms are unknown. Using 16S sequencing and metagenomics, we are attempting to characterize pollutant-mediated disruption in the fish gut microbiome, and to estimate the effects of disruption and microbially-mediated contaminant transformation on fish physiology and health.
EMIRGE: specialized assembly of 16S rDNA and other genes from microbial community sequencing data
We continue to develop EMIRGE (expectation maximization iterative reconstruction of genes from the environment) as a tool for assembly of 16S genes from short-read sequencing data (see Software page). Even with modern read lengths and error rates of Illumina sequencing, assembling 16S genes from shotgun metagenomics or amplicon sequencing data is challenging. The goal of EMIRGE is to assembly near-full-length 16S genes from such data, and estimate gene abundances with high accuracy.