Integrating genomics, computational biology, and systems biology approaches to study the composition and dynamics of the human gut microbiome, as well as the interactions between microbes and hosts.
整合基因组学、计算生物学和系统生物学手段，研究肠道菌群的结构组成与变化规律，以及微生物与宿主的相互作用等问题。
Establish statistical models and algorithms for circular RNA recognition, quantification, transcript assembly, alternative splicing identification, and functional annotation, providing methodological tools for unraveling their biogenesis mechanisms and functions.
建立环形RNA识别、定量、转录本组装、可变剪接识别和功能注释等统计学模型和算法，为解析其形成机制和功能提供方法学工具。
Based on microfluidics and deep learning technologies, establish new methods for single cell and spatial transcriptomics / proteomics / metabolomics, uncovering the spatial heterogeneity of cells and functions, and gaining a deeper understanding of the impact of the gut microbiota and its metabolites on the host.
基于微流控及深度学习技术，建立空间转录/蛋白/代谢组学新方法，揭示细胞及功能的空间异质性，深入理解肠道菌群及其代谢产物对宿主的影响。
