Computational Biology Department
Introduction:
The Computational Biology Department was established in 2024. It is one of the research focused divisions of China National Center for Bioinformation (CNCB).
Head: Prof. HAN Dali
Deputy Head: Dr. Hao Yajing
Principle Investigators (in alphabetical order): JIANG Lan， KANG Yu, LIU Zhaoqi, TIAN Chenxi, XING Xudong, XU Chenhuan, ZHANG Weiqi
Missions:
Aiming at the global frontier of science and technology and the commanding heights of computational biology research, we develop multi-level life data analysis technologies and computational methods, create artificial intelligence models to decipher the complex regulatory principles of biological systems, deeply reveal the essence of life phenomena, and thereby drive the paradigm shift in scientific research driven by biological big data.
Research Highlights:
Our Computational Biology Department pioneers computational approaches to decode complex biological systems and drive biomedical innovation. We develop cutting-edge tools and algorithms integrating multi-disciplinary methodologies for biological molecule decoding and omics advancement across life cycles and diseases. Leveraging AI and advanced computation, we elucidate genetic material regulation mechanisms, including genome organization, gene expression control, epigenetic information inheritance (like DNA modifications, histone marks, 3D genome organization), and RNA regulation (splicing, polyadenylation, etc.), with impacts on anti-tumor immunity, stem cell function, and epigenetic therapy. We also create innovative single-cell multiomics methodologies and frameworks for analyzing complex biological data. By constructing high-quality human reference genomes, developing analysis platforms, and integrating multi-omics data, we uncover biomarkers and therapeutic targets, and enable single-cell and spatial multi-omics analysis at unprecedented resolution. Our computational approaches are applied in cancer research to identify hallmarks, therapeutic targets, and tumor microenvironment modulation strategies, explore sex differences in cancer, and to develop biological aging clocks for early risk prediction, with the ultimate goal of translating computational biology into health benefits.
