Ph.D. Candidate - South Dakota State University
Applications of Bioinformatics to Study Fungal Pathogens, Soil Microbes, and Human Diseases
Key aspects of bioinformatics are the development of algorithms and the analysis of “big data” to study diverse components of biological systems. Additionally, next-generation sequencing (NGS) has become an indispensable technology for life science research. Using small RNA/RNA-seq, degradome-seq, and whole exome sequenced genotyped data, my studies aim to understand diverse topics in biology and bioinformatics. One of the goals of these studies were to
develop a method to quality control NGS data. Identification of sample swaps and DNA contamination are critical steps in the quality control of sequence data. We demonstrated that, using appropriate filtering and population stratification control, that identity by state (IBS) can identify sample contamination in both germline and somatic sequencing without the use of a matching germline sample or SNP genotyping array. We demonstrated that this method can be used with as few as 3,000 SNPs, making it suitable for large targeted sequencing panels. We
further implemented this QC algorithm in SKAT/Burden tests to identify deleterious mutations in cancer. On the other hand, we also explored the applications of agricultural microbial next-generation sequencing. We studied the roles of RNAi genes in host-pathogen interactions. For example, in fungi, RNA silencing has been shown to function in defense against invasive nucleic acids. RNA-silencing-deficient fungi show increased susceptibility to virus infection. With the application of bioinformatics, we were also able to study the effects crop rotation on soil
microbes and overall yield in a site-specific manner. These studies show how bioinformatics and NGS technology help accelerate the understanding of diverse components of biological systems.
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