Data analysis and database development for plant bioinformatics applications

Abstract

In this dissertation, we analyzed next-generation sequencing (RNA-SEQ) data from soybeans (Glycine max) that were infected by the plant-parasitic nematode (Heterodera glycines), also known as the soybean cyst nematode. A pest that causes billions of dollars in loses to soybean crops worldwide. The goal of these experiments was to examine the role of mitogen-activated protein kinase (MAPKs) genes in the defense mechanism. We also analyzed RNA-SEQ data associated with cucumbers infected with Rhizoctonia solani, a common fungus that infect many plant crops. We developed databases and web applications to manage the huge data sets from both experiments, and to give users the ability to browse, search, and mine the data for useful information. We also compared and contrasted commonly used differential expression analysis tools (mainly DEGseq, Deseq2, EdgeR, and Cuffdiff2). The tools were compared based on their efficiency and sensitivity. We found that Cuffdiff2 and DEGseq were the most sensitive in detection of differentially expressed genes.