After the invention of PCR in the 1980s, the Next Generation Sequencing (NGS) became the most significant game-changer in the fields of biology and medicine. In the Laboratory of Evolutionary Bioinformatics, we, a bunch of scientists and engineers, focus on the junction of microbiology and bioinformatics using the large-scale genomic, metagenomic and human microbiome data. Our goal is to understand the evolutionary mechanisms of microbial speciation and diversification, which have implications for antibiotic resistance, the emergence of novel pathogens and human health.
Understanding the mechanism of bacterial evolution.
In 2009, we reported that the short-term evolution of bacterial species largely attributes to lateral gene transfer (LGT). It is, therefore, important to detect the LGT events among major pathogens and their underlying mechanisms. By applying various algorithms and statistical methodologies to large genome data, we are in a better position to understand the whole picture of bacterial evolution and diversity than ever before.
Exploring metagenomic and microbiome data.
In recent years, many studies clearly indicate that human microbiome, particularly gut microbiome, is the key to understand and manage human health. Various types of diseases are known to be associated with the gut bacterial community. We focus on improving how we analyze microbiome data, particularly metagenomic NGS data by applying up-to-date algorithms. These include those for machine learning and deep learning.
Providing software tools and databases to the scientific community.
We believe that one of the key roles of bioinformaticians is to provide adequate software tools and databases for the scientific community. Over the years, we deployed and published a series of software tools and databases that can be used for microbiologists and people working in the field of infectious diseases. The list is given at http://www.ezbiocloud.net/tools. At present, we work on web-based bacterial genome browser and tools for bacterial classification and phylogeny.