Our Research Fields

 

MicrobiomeM

Method development for metagenomics research on microbial communities

The initiative on method development, which aims to provide a set of methods, from genome assembly, gene annotation to network analysis, which are urgently needed by researchers worldwide.

Method development for multi-omics data analysis

We aim to develop new methods for combining different types of omics data -- genomics, transcriptomic, proteomics, metabolomics and network-omic -- from high-throughput sequencing facilities, and interpret gene regulation process, gene expression dynamics, etc.

Analysis and Statistical Validation of Proteomics
Development of a set of statistical models and algorithms that will enable robust, accurate, and transparent analysis of large-scale quantitative tandem mass-spectrometry (MS/MS) based proteomic datasets from micribial samples.

Pan-genome and single-cellP

Method development for pan-genome research on microbes

A comprehensive analysis on pan-genome's gene prediction, gene annotation, gene expression and comparative genomic study in micro-algae.

Algorithms for genome evolution and community evolution

Algorithm development for genome evolution and community evolution. The aim is progressive develop methods for analysis of simple problems such as SNP and indel detection, to more biologically complex problems such as SNP and indel detections from metagenomic data, and to more computationally complex problems such as the fitness of the gene modules in the network.

OthersO(Data mining, HPC, syn-bio, etc.)

High-performance methods and collaborative methods development for genomic data analysis

Study on accelerating the genomic data analysis efficiency by high-performance computing. There are generally two sub-areas. The first is the development and utilization of high-performance computing hardware (such as GPGPU) and software to improve efficiency. The second is the development of collaborative systems for efficient process of multiple sets of genomic data, and establish the online website for open service.

Analysis and Statistical Validation of Proteomics
Development of a set of statistical models and algorithms that will enable robust, accurate, and transparent analysis of large-scale quantitative tandem mass-spectrometry (MS/MS) based proteomic datasets from micribial samples.