In BioComs lab, we focus on developing bioinformatics methods to analyze high-throughput and large-scale biological data including genomic sequence data, transcriptomics data, protein-protein interaction data, and many more. Please send us a quick message if you are interested in learning about our research or joining our awesome team!
Our current research projects include:
The goal of this project is to develop data mining methods that can be used to identify genotypic patterns associated with phenotypic transitions. The goal will be achieved using combined networks inferred from multiple biological domains including protein-protein interactions and semantic similarities among phenotype ontologies.
Microbiome refers to the entire population of microorganisms that are found in a particular environment. The entire genetic information surveyed from a microbiome is referred to as metagenomics. In addition to metagenomics, other meta-omics data including metatransciptomics, metaproteomics, meta-metabolomics, and metainteractomics were also generated as the results of the NGS (next-generation sequencing) strategy applied to the study of microbiome at different levels. These multiple meta-omics data have been obtained to investigate a variety of microorganisms such as bacteria, fungi, and viruses. In this project, computational methods for multiple meta-omics data analyses will be developed, especially approaches for downstream analysis to accurately predict the association of microbiome with environmental factors. The methods resulted from this research will be used to investigate the relationship between gut microbiota, human genetic variations and risk of diseases to develop novel screening tools. The methods will also be used to study the plant-microbe interactions to identify plant beneficial microorganisms.
Cancer is such a complex human disease that needs sophisticated analysis. All kinds of omics data obtained in cancer research reflect the heterogeneous characteristics in cancer development. The goal of this project is to apply data mining and machine learning techniques to perform an integrative analysis to uncover drivers of cancer. Most initial study of human cancer is first conducted using mouse models. This project will also evaluate how the results from mouse models agree with disease of human.
Recent studies have revealed that transcripts in a cell not only include mRNAs (messenger RNA), but also sRNAs (small RNAs). It is believed that small RNAs especially miRNAs play crucial roles in plant’s response to abiotic stresses such as drought. This project is designed to measure mRNAs and sRNAs from plant individuals under different controlled abiotic stresses. Through gene network construction and comparison, we hope to find master regulators and their target genes that can be used to enhance plant tolerance to extremely environmental conditions.
We also collaborate with other research teams on several collaborative projects including inferring gene regulatory networks in soybean nodule development, grape genome assembling, investigating metastasis of medulloblastomas, plant genotype and ecotype association study, etc.