Computational Microbiome Analysis: Method Development, Integration and Clinical Applications

Metagenomics is the study of microbial genomes from one common environment and in most cases, metagenomic data refer to the whole-genome shotgun sequencing data of the microbiota, which are fragmented DNA sequences from all regions in the microbial genomes. Because the data are generated without laboratory culture, they provide a more unbiased insight to and uniquely enriched information of the microbial community. Currently many researchers are interested in metagenomic data, and a sea of software exist for various purposes at different analyzing stages. Most researchers build their own analyzing pipeline on their expertise, and the pipelines for the same purpose built by two researchers might be disparate, thus affecting the conclusion of experiment. 

My research interests involve: (1) We first developed an assembly graph-based ncRNA searching tools, named DRAGoM, to improve the searching quality in metagenomic data. (2) We proposed an automatic metagenomic data analyzing pipeline generation system to extract, organize and exploit the enormous amount of knowledge available in literature. The system consists of two work procedures: construction and generation. In the construction procedure, the system takes a corpus of raw textual data, and updates the constructed pipeline network, whereas in the genera- tion stage, the system recommends analyzing pipeline based on the user input. (3) We performed a meta-analysis on the taxonomic and functional features of the gut microbiome from non-small cell lung cancer patients treated with immunotherapy, to establish a model to predict if a patient will benefit from immunotherapy. We systematically studied the taxonomical characteristics of the dataset and used both random forest and multilayer perceptron neural network models to predict the patients with progressing-free survival above 6 months versus those below 3 months.