Manually Curated Proteins


Predicted Proteins

Welcome to Guomics

Diversity &Inclusion

Multidisciplinary team with diverse backgrounds

R&D Driven

Reliable, efficient, and economical proteomics technologies

Big Data

Discover quantitative biological rules in proteomic big data

AI Empowered

Data and knowledge mining through artificial intelligence

Research Interests

My team focuses on developing cutting edge proteomics technologies to precisely quantify maximum proteins with the tiny biological or clinical sample volumes by high-throughput protocols, enabling proteomic big data researches to tackle complex biomedical questions.

Our kernel method is the PCT-SWATH/DIA (Guo, et al, 2015), which means Pressure Cycling Technology and Sequential Window Acquisition of all THeoretical fragment ion spectra mass spectrometry (protected term from SCIEX) and Data-Independent Acquisition (DIA, more generic description)

In Westlake, we continue to develop new technologies to generate high-throughput, more reproducible, cost-effective proteomic big data (Sun et al, 2020; Gao, et al, 2020; Cai, et al. 2020)

Meanwhile, we are developing computational toolbox for people to easily analyze high volume proteomic datasets (Zhu, et al. 2019; Zhu, et al. 2020)

Our research relies on equipments including barocyclers, mass spectrometers and high performance computing systems.

We developed PCT-SWATH/DIA technology which allows fast, reproducible and cheap proteomic analysis of tissue specimens including fresh tissues and formalin-fixed paraffin-embedded (FFPE) tissues. With no more than 1 mg tissue, the PCT method produces on average 50 µg peptides, while only 0.2-1µg peptides are sufficient for an optimal generation of a SWATH/DIA map using TripleTOF (Shao, et al. 2015) or the latest Orbitraps such as Q-Exactive HF/HFX, Fusion and Lumos within 0.5-2 hrs. We also worked with Pressure Biosciences Inc in developing PCT-MicroPestle for enhanced sample preparation efficiency (Shao, et al. 2016).

The long-term goal of our group is to build up digital biobanks of proteomics of clinical specimens, and develop proteomic big data, cloud computing and AI based new type of diagnostic methods.

The proteomic big data technologies are applied to stratify intermediate prostate cancers.

Prostate cancers are the most common cancers in men. It’s highly prevalent all over the world and largely over-diagnosed and over-treated.

We are also working on developing protein and deep learning based diagnostic tools for thyroid cancer.

Thyroid cancers are the most common cancers in women. It’s highly prevalent all over the world and largely over-diagnosed and over-treated.

We have several openings for postdoc scholars. Please contact the PI with a statement letter of research interest if you are interested.