Experimental Bioinformatics Laboratory

Dr. Youping Deng brings a strong combination of leadership, expertise, and passion to support bioinformatics, genomics, and quantitative health research. He is a Professor and Director of the Bioinformatics Core in the Department of Quantitative Health Sciences at the John A. Burns School of Medicine (JABSOM). He also serves as the Co-Director of the Genomics and Bioinformatics Shared Resource at the University of Hawai?i Cancer Center and Co-Director of the Pacific Center for Genome Research.

With over 22 years of experience in bioinformatics data analysis and management, and more than 25 years of experience in molecular biology and genomics experiments, Dr. Deng has been deeply involved in advancing research through bioinformatics. He holds extensive training in genomics, molecular biology, computer science, and bioinformatics, and has developed more than 20 novel bioinformatics methods over the course of his career.

As a long-term advocate for bioinformatics education, Dr. Deng has taught bioinformatics courses, mentored PhD students, and trained numerous postdoctoral fellows, young faculty members, staff scientists, and lab managers. His work has successfully supported bioinformatics components in multiple NIH-funded projects.

The Bioinformatics Core he oversees is equipped with commercial and customized computational pipelines, as well as high-performance computing resources, designed to support a wide range of research projects.

The long-term goal of the Deng lab is to develop precision medicine for cancer using both bioinformatics and experimental approaches.

My research is mainly centered on 4 areas:

1. New computational method development. My lab has developed a series of innovative methods including novel algorithms for data normalization, clustering, feature selection, classification, differential expression, gene function ontology, gene network modeling and so on. We are currently developing new methods for alternative splicing and DNA somatic mutation for biomarker identification based on sequencing data.
2. Identification of non-invasive biomarkers for early detection of cancer. We are searching for novel accurate circulating biomarkers for early detection of lung cancer and breast cancer. Based on a variety of high throughput “omics” methods including small RNA-seq, metabolomics, DNA-seq, and proteomics plus bioinformatics data mining, we have found and are seeking for circulating metabolite, ncRNA, protein, and CtDNA markers for early diagnosis of cancer.
3. Characterization of biomarkers for predicting clinical outcomes of human diseases including cancer. We are mining public “omics” data such as TCGA data as well as generating our own high-throughput data to identify biomarkers to predict clinical outcomes of human diseases including cancer. For instance, we have found novel DNA mutation and gene expression signatures to predict better response to cancer drugs such as PARP inhibitor. New cellular and animal experiments are being designed to evaluate these biomarkers and understand their mechanisms.
4. Integrative data analysis of “omics” and clinical data. We are integrating different types of “omics” data such as genomics, transcriptomics, metabolomics, epigenomics and proteomics data, as well as clinical factors, using a systems biology approach to understand carcinogenesis, cancer development, and find better biomarkers for precision medicine.

In addition to independent research, my team also provides bioinformatics data science services, which primarily focuses on the analysis and management of high-throughput data such as microarray data, real-time PCR data, proteomics, metabolomics, multiple biomarker data and next-generation sequence data including DNA-seq, RNA-seq, Chip-seq and microbiota (metagenomics) data and so on. The core also supports routine bioinformatics applications, such as phylogenetic, protein function prediction