Biological trace elements are essential for proper growth, development, and physiology of all organisms. The majority of them are metals, which serve as cofactors and activators of metalloproteins implicated in many key cellular processes. A long-term goal of our research is to develop bioinformatics algorithms that help identify new metalloprotein genes and genes involved in metal metabolic pathways.
We have characterized the sets of metalloproteins (metalloproteomes) in a variety of organisms for multiple metals (such as zinc, copper, molybdenum, nickel, and cobalt), demonstrating the most comprehensive view of the utilization of metals and their evolutionary trends in all three domains of life. We also focus on selenium, an important metalloid that mainly occurs in the form of selenocysteine (the 21st amino acid) in selenoproteins. We have developed several algorithms for the identification of new selenoprotein genes and genes associated with selenium metabolism and homeostasis in genomic sequences, and generated the largest map of selenium utilization in prokaryotes.
We are also interested in metagenomic analysis of trace metal utilization, which provides novel insights into the use of metals/metalloids in a much wider range of organisms and its relationship with various environmental conditions. We have developed several algorithms for the identification of new selenoprotein genes and genes associated with selenium metabolism and homeostasis in genomic sequences, and generated the largest map of selenium utilization in prokaryotes. We are also interested in metagenomic analysis of trace metal utilization, which provides novel insights into the use of metals/metalloids in a much wider range of organisms and its relationship with various environmental conditions.
Complex diseases, such as cancer, diabetes, and heart disease, are thought to be caused by genetic variations (gene–gene interactions) and environmental factors (gene–environment interactions). The underlying molecular mechanisms of these diseases are still poorly understood. Moreover, it is still difficult for early diagnosis and prognosis evaluation of most complex diseases.
We are interested in developing new strategies and network-based methods for the exploration of novel mechanisms of several major types of complex diseases (such as neurodegenerative disease, cancer, and diabetes) based on multidimensional omics data (e.g., genomics, transcriptomics, proteomics, and ionomics).
Additionally, we search for new biomarkers and build intelligent prediction models/platforms for the early detection of these diseases. Recent works in my group focus on the following areas: 1) investigation of the mechanisms of neurodegenerative disease (such as Alzheimer's disease) by single-cell sequencing data integration and analysis; 2) Construction of human disease-related databases and resources; and 3) Development of machine learning and deep learning algorithms for diagnostic prediction and classification of complex diseases.