Statistical modeling, inference and methodology for analyzing multi-source omics data and computational molecular evolution

Recent research has shown the enormous influence of the microbiome on our lives. Microbial communities play crucial roles in human health, agriculture and the environment. With the rapid development of high-throughput sequencing technologies, enormous data sets have been collected from multiple sources. These data offer the opportunities for us to understand how microbial communities function and interact with the environment. However effective statistical methods to analyze these integrative multi-source data are lagging behind. My research program will be focused on developing effective statistical methods to study the microbial communities, functions, and dynamics. The long term goal is to develop models that will not only predict the difference in functions between existing communities, but will offer methods to create new communities with desired functions, or to alter the functions of existing communities. My research will develop tools for three aspects of this data analysis: developing methods for the initial examination of the data to identify basic patterns; predicting the functions performed by particular microbes based on their genetic sequences; and studying the changes in microbial communities over time. I will then develop analysis methods that put all of these aspects together to learn more about the behavior of microbial communities. A direct application of the research results is for the prediction of Toxic Cyanobacteria blooms in lakes. Certain Cyanobacteria produce toxins which are fatal to humans and animals, and when these are in high abundance, it can poison drinking water supplies to large areas. This is a massive risk to public health, and the economic costs of finding and mitigating these blooms are expected to grow rapidly in the coming years. It is therefore essential that we develop more efficient methods to predict these blooms, or better still, to avert the blooms altogether. My research can be used to improve predictive models for algal activity in drinking water reservoirs and help on the drinking water quality control process. My collaboration with two groups of scientists in Quebec and Halifax make the knowledge translation to addressing water quality concerns more direct through their industry partnerships. I have also started to extend my research to waste water microbiome analysis to help with waste water treatment plans and to ocean microbiome data to study the effects of pollution and climate change on the ocean microbiome. Many of the statistical methods developed in my research are general, and can be applicable to other types of data, leading to impacts in other fields and in statistical methodology research itself.