I'm a Ph.D. candidate in Bioinformatics working with Dr. Yi Xing at the UCLA Bioinformatics IDP/Children's Hospital of Philadephia. Currently my research is focused on leveraging big data analytics to elucidate the regulatory basis in the human transcriptomes. My long term goal is to predict and interpret the phenotypic and molecular trait variations by integrating multi-omics data and biological regulatory patterns in health and disease.
I obtained my B.Sc. degree from Zhejiang University. During my undergrad, I worked with Dr. Ming Chen for three years to study the regulatory and metabolic networks of Oryza Sativa using computational and systems biology approaches.
The rapid accumulation of RNA-seq data across diverse cell types and conditions provides an unprecedented resource for characterizing transcriptome complexity. However, the use of these large-scale data in routine RNA-seq studies to detect patterns of expression and thereby discover new regulatory events has been limited.
Conceptually, the DARTS framework transforms existing RNA-seq big data into a splicing knowledge-base by deep learning, and help democratize the information from large consortium projects to help individual investigators better characterize alternative splicing profiles using their specific RNA-seq datasets. Read More
CLIP/RIP-seq allows transcriptome-wide discovery of RNA regulatory sites. As CLIP-seq/RIP-seq reads are short, existing computational tools focus on uniquely mapped reads, while reads mapped to multiple loci are discarded.
CLAM uses a statistical algorithm called "Expectation–Maximization" to assign multi-mapped reads and calls peaks combining uniquely and multi-mapped reads. CLAM provides a useful tool to discover novel protein–RNA interactions and RNA modification sites from CLIP-seq and RIP-seq data, and reveals the significant contribution of repetitive elements to the RNA regulatory landscape of the human transcriptome. Read More