Work in our group involves information integration, database building, algorithm development, software designing, and data analysis. In particular, our research interests currently focus on:

1.   Designing online databases and tools for high through-put data analysis

The thousands of sequenced genomes and millions of sequences identified by metagenomics projects have promoted the vast amonunt of various-omics data. To facilitate the users, we are interested in developing special database and handy tools. Recently, we put emphasis on several model crops including tomato, kiwifruit, blueberry and rice about their genome, transcriptome and methylome.

2.   Modeling cellular networks (e.g., protein-protein interaction)

Living cells are the products of gene expression program and the activities of protein particular function. In fact, these macromolecules rarely act alone, they typically interact with other molecules to perform cellular tasks. High-throughput technologies have generated large amounts of data on the interactions of macromolecules. Currently, the aim of the project is to develop and compare various methods for inferring functions of proteins and predicting potential protein interactions by using the existing data. Up to date, we have developed an intra-species PPI network, where the two interacting proteins are dericed from the same species, and an inter-species PPI network, where the two interacting proteins are derived from different species.

3.   Improving approaches to bridge the gap between genotype and phenotype

Plant phenotypes are the comprehensive description of their observable traits, such as anatomical, growth, tolerance, resistance, and yield. The phenotype mainly dues to genetic (e.g., DNA sequence mutation, the spatial and temporal expression of genes) and epigenetic (e.g., altered cytosine methylation, histone modification) change as well as the interactions between the two. We have been actively seeking rules that associate genotypes and phenotype by using different methods, such as deep learning.

4.   Development and evaluation of informatization argicultural support system

The informatization agricultural support system, the so-called "Agri-eye", is developed to optimize the agricultural production system. It is composed of the field environmental monitoring and acquisition, the crop growth diagnosis and quality evaluation, the work history recording, the data analysis and visualization, the communication tools, and so on. Currently more than 50 devices have been already installed into more than 10 real fields mainly distributed in Jiaxing (Zhejiang Province, China) and Guangde city (Anhui Province, China). The validity of the systems has been verified by the feasibility studies up to present.