Feature Story Research & Innovation
Bioinformatics is a rapidly growing research strength in SBMS. A few bioinformatics-focused principal investigators (PIs) have joined the School in the last three years (see lab research interest below), and many other laboratories are adopting increasingly sophisticated computational or statistical techniques in their own research area. The increased demand of bioinformatics is in part driven by the increasing volume and complexity of modern biomedical big data. From Sanger sequencing to next (now third!) generation sequencing, the quality and quantity of genomic, epigenomics, transcriptomics, and metagenomic data generated by modern biomedical research are growing rapidly. In addition to sequencing data, we are also experiencing a proliferation of other data types, such as mass spectrometry data, microscopic and medical imaging data, and emerging digital health data. Bioinformatics researchers develop and adopt innovative computational, statistical or mathematical techniques to extract useful information from these big data. The SBMS bioinformatics laboratories are strong in developing innovative general bioinformatics tools and custom software for a variety of biomedical data, especially in the areas of genomics, single cell transcriptomics, and metagenomics. Bioinformatics research @ SBMS
Bioinformatics is a highly collaborative discipline. Bioinformatics laboratories at SBMS collaborate with many colleagues in the SBMS and beyond areas of common interest. The bioinformatics PIs are involved in a variety of collaborative research projects, such as research programmes based at the Centre for PanorOmic Sciences (CPOS), InnoHK centres, and theme-based orcollaborative project grants. To promote data-intensive research at SBMS, the School has invested in computational infrastructure to support the growth of bioinformatics research. Our School has a high-performance computing server in which all members of the School can use. It provides reasonable computing and short to medium-term data storage need. The bioinformatics PIs are passionate in nurturing a new generation of data savvy biomedical scientists. In terms of formal Teaching and Learning activities, we are introducing modern big data concepts and skills into the Precision Medicine stream in the MBBS curriculum, and some new bioinformatics topics in specific BBMS courses. The team is working towards rejuvenating the BSc (Bioinformatics) programme. In addition, we organises specialised training workshops in bioinformatics. A single cell analysis workshop is being planned for June and July this year, with potentially more to follow in the future. We welcome suggestions for additional topics to be covered in the future.
Dr. Joshua Ho Our group develops software to support scalable big data analysis of a variety of sequencing data, including single-cell omic data and metagenomics data. Digital health is another emerging area of research in the lab, in which we develop smartphone apps and AI methods to enable innovative healthcare solutions by harnessing a variety of sensors in wearable and mobile devices. Dr. Jason Wong Our laboratory applies advanced computational methods and genomics to study mutational processes that lead to cancer development. We leverage somatic mutations from hundreds of thousands of cancer samples to identify patterns associated with specific carcinogens and DNA repair defects. We are also starting to explore third-generation long-read sequencing technology to study the expression of transposable elements in cancer cells.
Dr. Asif Javed Our group combines state-of-the-art omics workflows with downstream integrative analytics to study disease impact in primarysamples.The current areas ofourfocus are congenital diseases and blood cancers. In parallel we work on understanding the biases in new data generation technologies to develop computational tools to better counteract them.
Dr.Yuanhua Huang We work on single cell data science, by developing statistical models and machine learning algorithms to decipher cell states from single-cell omics data, including transient differentiation via RNA velocity and clonal evolution via detected somatic mutations.
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