1. We combine wet-lab experiments and computational analysis of multi-omics data to investigate important biological questions, including the onset and progression of complex diseases such as cancers (our major research focus now), the establishment and maintenance of tissue-specific epigenetic landscape and gene expression program, the characteristic feature and fine structure of regulatory elements, etc.
2. We develop computational models and algorithms for large-scale omics data analysis and integration (epigenome, proteome, transcriptome, etc.), which could be generated by a variety of cutting-edge platforms including ChIP-seq, ATAC-seq, RNA-seq, mass spectrum. Here are some examples:
1) MAnorm (Shao et al., Genome Bio 2012) for quantitatively comparing two ChIP-seq samples and detecting differential binding sites between them;
2) MAnorm2 (Tu et al., Genome Res 2020) for quantitatively comparing two or multiple groups of ChIP-seq samples and detecting differential binding events on group level;
3) MAmotif (Sun et al., Cell Discov 2018) for quantitatively comparing two ChIP-seq samples and detecting TFs whose binding is significantly associated with the differential ChIP-seq signals as candidate cell type-specific co-regulators;
4) MAP (Li et al., Cell Discov 2019) for quantitatively comparing quantitative proteomic profiles generated based isotope labeling based mass spectrometry techniques and detect proteins showing significant abundance changes.