Research interests:
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We are a statistical group with major applications on genomics and bioinformatics.
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Our research lies on the interdisciplinary boundaries of many fields (Statistical Sciences, Biological and Medical Sciences, Genomics and Genetics).
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Our group's major research interests include development and application of statistical and computational methods for analysis of high-dimensional genomic/genetic, metagenomic/ metatranscriptomic, and epigenomic data.
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Our vision is to develop rigorous, timely and useful statistical and computational methodologies to help biologists/geneticists to ask, answer, and disseminate biologically interesting information in the quest to understand the ultimate function of DNA and gene network.
Projects:
Taxonomic assignment analysis for metagenomic samples based on next generation sequencing data
Metagenomics is the study of multiple genomes from multiple species in communities obtained by direct sequencing from an environment, without the need for culturing them. Based on the sequence data from a metagenomic sample the basic questions will be addressed include “what species or genomes are there?”, “what are their relative abundance?”, and “how are multiple metagenomes different?” Metagenomic experimental process is shown as below. The question Q1 and Q3 in the picture will be addressed and answered in this project.
Statistical methods for functional metagenomic analysis with applications in biological threat detection
High-throughput next generation sequencing technologies provide a powerful way to detect biological threats from metagenomic samples taken directly from the environment without prior knowledge of sample composition. In the analysis of metatranscriptomic data sets, we can examine and compare the active gene functions and pathways in the environmental or host-associated metagenomic samples with the presence or absence of biological threat agents (organisms or viruses). This is accomplished by identifying which genes are active in a sample and characterizing which functional patterns are associated with the presence of biothreat agents. Moreover, functional analysis of metagenomes can explore how functional diversity of microbial communities correlate with important biological factors of interest including the presence of a particular threat organism and its virulence level. In this research we propose to build rigorous statistical models and rapid computational algorithms to detect biological threats based on metatranscriptomic sequencing data, i.e., we are targeting the Q2 & Q3 in the above graph.
Benchmark risk assessment in food safety
The focus of this project is development and study of new statistical methods for use in food safety/microbial risk assessment. Application is directed to settings where a microbial pathogen is measured on food processing equipment or food contact surfaces. Of interest is calculation of benchmark or other safe, low pathogen levels in order to manage risk of contamination of the food being processed in such environments. The resulting guidelines will improve risk management by commercial food establishments when dealing with potential microbial contamination of processing environments, and provide data for science-based risk assessment of food processing environments by regulatory agencies.
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