The past decades have witnessed a rapid growth in high-throughput technologies in genomics and medicine. Our research interests are to develop and apply genomic and bioinformatic approaches, aiming to gain more knowledge into human health and diseases.

    The Laboratory of Bioinformatics and Genomics is a research unit of the State Key Laboratory of Ophthalmology of China. Our research is also supported by the Center of Precision Medicine, Sun yat-sen University. The members of the group come from different background including computer science, bioinformatics, molecular biology and medicine. Our state-of-key facilities include powerful high-performance CPU and GPU clusters and high-throughput sequencers.

Our Research

1) Computational genomics of single-molecule long-reads

The long reads from single molecular sequencing (SMS) technologies such as PacBio single molecule real time (SMRT) and Oxford Nanopore have many advantages in genomic studies. However, applying SMS to large-sized genomes has suffered from high computational cost. We developed an ultra-fast Mapping, Error Correction, and de novo Assembly Tool (MECAT) for SMS reads [Nature Methods 2017]. The computing efficiency of MECAT is superior compared to current tools while the results are comparable or improved. Another advantage of SMS sequencing technology is to detect the less studied DNA 6mA and 4mC modifications at single-nucleotide resolution. We developed MethSMRT, the first resource hosting DNA 6mA and 4mC methylomes [Nucleic Acids Research 2017].

2) Translational regulation of gene expression

Ribosome profiling is a technique that enables genome-wide investigation of in vivo translation at sub-codon resolution, to understand the composition, regulation and mechanism of translation. Using ribosome profiling, we can not only understand the translational regulation of protein coding genes [Journal of Virology 2016; BMC Genomics 2017; Protein Cell 2018], but also coding potentials of non-coding genes [Nucleic Acids Research 2017]. A comprehensive collection of ribosome profiling datasets can be found in [Nucleic Acids Research 2015]. In addition, a review of computational resources and tools for ribosome profiling can be found in [Briefings in Bioinformatics, 2017].

3) Multi-omics and systems biology

Recent advances in omics technologies, including genomics, transcriptomics, proteomics and metabolomics, enable us to build an integrative and comprehensive model and to systematically understand molecular changes. (1) By integrating transcriptomic, proteomic and metabolomic datasets, we found that mitochondrial and protein quality control playing an important role in response to hibernation which could potentially protect non-hibernated species from cold stress [Journal of Cellular Physiology 2017; Cell 2018]. (2) Using an integrative analysis of transcriptomics and proteomics, we revealed that oxidative phosphorylation and mitochondrial biogenesis were involved in induced differentiation of glioblastoma cells into astrocytes [Cell Reports 2017]. (3) To systematically understand human immunity, we analyzed immune parameters in depth both at baseline and in response to influenza vaccination. Peripheral blood mononuclear cell transcriptomes, serum titers, cell subpopulation frequencies, and B cell responses were assessed in 63 individuals before and after vaccination. Strikingly, independent of age and preexisting antibody titers, accurate models could be constructed using pre-perturbation cell populations alone, which were validated using independent baseline time points [Cell 2014]. Overall, multi-omic analysis can provide important insights into the complex biological systems.

4) Deep learning in bioinformatics and medicine

Deep learning is a recent and fast-growing field of machine learning. It attempts to model abstraction from large-scale data by employing multi-layered deep neural networks, thus making sense of data such as genomics, texts and images [Genomics, Proteomics and Bioinformatics 2018]. Compared to the conventional analysis strategies, deep learning is powerful to leverage very large and complex data sets. Our lab is currently working on a number of projects in bioinformatics and medical images to solve computational challenges in biology and medicine.

5) Single cell genomics

Recent technological advances have enabled unprecedented insight into genomics at the level of single cells. Single cell transcriptomics enables the measurement of transcriptomic information of thousands of single cells in a single experiment. However, many technical challenges exist in improving sensitivity and specificity of the experiments. In addition, the volume and complexity of data make it a paradigm of big data. Consequently, the field is presented with new technical and, in particular, analytical challenges. Our lab is interested in developing both computational and experimental methods to explore single cell gene expression variation in development and human diseases.

Laboratory of Bioinformatics and Genomics, Sun Yat-sen University