LC-MS/MS based quantative proteomics enables researchers to study biological and medical issues in a systematic manner. However, high-throughput quantitative proteomics research, especially data-independent acquisition (DIA), brings complex and large-scale data, which requires sophisticated computational tools. Thus we aim to develop computational methods with high precision, sensitivity, robustness, efficiency and reproducibility.
In the last decade, the development of immune checkpoint inhibitors has led to a dramatic change in immuno-oncology, however, only a small percentage of patients respond to immunotherapy. Therefore, how to select the patients who will benefit prior to the treatment would improve overall patient outcome.
Currently, there is no effective biomarker for predicting patients responding to immunotherapy. Many research point that the key to the immunotherapy response lies in the composition of the tumor immune microenvironment. The identification and analysis of different cell types and states in the tumor microenvironment can allow us to further understand the mechanism of tumor response to immunotherapy, so as to develop more effective immunotherapy biomarker and combination therapy regimens.
Rapid pathogens identification is one of the most important issues for microbial community studies to ensure food supply safety and to minimize the impact of foodborne illnesses on public health. Metagenomic next-generation sequencing (mNGS), defined here as the study of genetic material collected directly from environmental samples, has the power to differentiate between even closely related strains of the same species. Although mWGS has already greatly improved outbreak detection and traceback, current approaches rely on culturing a pathogen before sequencing. Therefore, we focus on developing and using bioinformatics algorithms to rapidly and accurately detect pathogens amongst a mix of genetic material sequenced originating in food, environmental sources, and patients.