THESIS
2020
xiii, 71 pages : color illustrations ; 30 cm
Abstract
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...[
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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.
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