Analysis Of Time-series Gene Expression Data And Research On Gene Regulatory Network Modeling Methods

Since most biological processes are dynamic, experiments based on time-series can help people to understand and model these processes. Some genetic data can be measured over time, the most abundant in the available data is time-series gene expression data. Such data can be used to describe the function of specific genes, the relationship between these genes, their regulation and coordination of information and its clinical significance. Time-series gene expression data has become one of the most basic methods to study biological processes.In this paper, we use a set of time-series RNA-seq data of Ethanoligenens harbinense YUAN-3 bacterium measured at six time points under specific conditions, making full use of the dynamic characteristics of time series data based on the static gene expression data analysis methods. We used methods based on timing difference and LRT to analyze gene expression differences, and identified differentially expressed genes during the entire time series. We used clustering algorithm based on timing trajectory and HMM model to cluster bacterial expression data, which can effectively identify dynamic changes of bacterial gene expression. We use dynamic Bayesian network to model bacterial gene regulatory networks, which can can effectively analyze transcription time lag and establish the correct gene regulation interactions.