Research overview
The broad goal of our work is to develop new quantitative methods for analyzing high-throughput metagenomic data. Metagenomics is a rapidly changing field in which scientists attempt to learn about the composition and dynamics microbial communities. Researchers look to novel and large-scale technologies to study these environments, and we aim to extract important information that is buried in copious biological data.
Detection of differentially abundant features in metagenomes
Recent studies describing the composition of microbiota in different environments have focused on targeted sequencing of individual genes, specifically the ubiquitous gene for 16S rRNA. This gene provides a relatively easy method to survey bacteria and archaea regardless of the environment. The goal of our research is to develop a statistically rigorous method to compare communities on the basis of 16S rRNA abundance data. We wish to detect differentially abundant taxa in two microbial populations and assess the significance of the observed differences. Our solution to this problem incorporates the false discovery rate (FDR) as well as hypothesis tests designed for sparse frequency data. This methodology is applicable to metagenomic data beyond taxa such as subsystem and pathway observations. We have recently submitted a paper detailing our methods. To ensure updates and simplicity, we have made our software available via this website.