With the development of sequencing technology, bioinformatics is playing an increasingly important role in biological research. Here I will introduce two collaborative projects. In the first project, we analysed single cell sequencing data in Drosophila. It was shown by our collaborator that the Drosophila scrib mutant tumors show different growth rates in different stages. To understand this process, we analysed the bulk and single cell RNA-seq data, and found that several biologically relevant signaling pathways have been altered, and interestingly the Gini coefficient of gene expression convergent over time, and we further used single cell topological analysis approach (non-linear, unsupervised method) to characterize and visualize the transition of the cell states longitudin...[ Read more ]

With the development of sequencing technology, bioinformatics is playing an increasingly important role in biological research. Here I will introduce two collaborative projects. In the first project, we analysed single cell sequencing data in Drosophila. It was shown by our collaborator that the Drosophila scrib mutant tumors show different growth rates in different stages. To understand this process, we analysed the bulk and single cell RNA-seq data, and found that several biologically relevant signaling pathways have been altered, and interestingly the Gini coefficient of gene expression convergent over time, and we further used single cell topological analysis approach (non-linear, unsupervised method) to characterize and visualize the transition of the cell states longitudinally. Furthermore, we used a computational framework to predict the Drosophila embryo cell position based on gene patterns. As the spatial gene patterns of Drosophila determine the development of cell types and body structures, it is important to identify minimal number of marker genes for special positioning. Using a greedy algorithm, we defined unsupervised single cell clusters and eliminated redundant genes, and eventually found marker genes for particular locations to characterize spatial gene patterns during embryonic development. In the second project, I improved a Functional Long-noncoding RNA Assembly Workflow (FLORA) and developed its function module in predicting function of lncRNA based on network biology approach. The updated pipeline was then applied to mouse muscle stem cells activation and human gastric cancer studies. We demonstrated that FLORA was able to identify and prioritise novel functional noncoding RNAs in the study of developmental biology and human diseases.