Mulinlab

Mulin Jun Li

Mulin Jun Li received a Ph.D. in Bioinformatics and Functional Genomics from The University of Hong Kong. He had exchange and postdoctoral training at Harvard University and The University of Hong Kong. He is now a Professor with the Department of Bioinformatics at School of Basic Medical Sciences, Tianjin Medical University. Mulin is a bioinformatics researcher with expertise in regulatory genomics. His research interests include the development of bioinformatics tools and resources for interpreting variant effects on different types of molecular traits and human complex diseases, and the dissection of biological mechanism of disease-causal regulatory variants using high-throughput functional genomics methods.

Research interests

Our group focuses on bioinformatics tools development and functional genomics data profiling to interrogate regulatory mechanism of complex diseases. We have developed a number of useful algorithms and tools for disease-causal variant annotation, tissue/cell type-specific regulatory variant prioritization, and genetics-based drug response prediction. Through leveraging CRISPR editing and single-cell omics, we are developing several techniques to study endogenous variant effect on fine-scale molecular phenotypes of gene regulation.

Variant Annotation and Prioritization

We have been developing algorithms (VarNote, regBase, cepip), online/local tools (GWAS4D, vSampler), and databases (CAUSALdb, QTLbase, vannoPortal) to facilitate the analysis of genome-scale sequencing variants and interpret genetic mechanism of traits and diseases. By integrating large-scale context-specific epigenomics and population genetics data, we are designing novel methods to investigate pathogenicity, tissue/cell type specificity as well as clinical actionability of human genome variations.

Regulatory Genomics and Mutagenesis Screen

We are developing the computational methods for the understanding of transcription factor cooperation and chromosome loop (3Dcoop, loopAnchor) in 3D genome. We also optimize CRISPR-based mutagenesis techniques to study the causal effect of regulatory variants on transcription initiation, elongation, promoter usage, enhancer targeting and dynamic epigenetic remodelling.

Genetics-based Translational Medicine

Using GWAS, xQTL and cancer genomics data, we have been investigating the genetic evidence of drug target selection (e.g. Firework), drug side effect probability (e.g. Onco-CardioRisk) and drug repurposing opportunity. We are also developing genetics-led computational methods and drug response screening techniques to facilitate drug development and personalized therapy.