High-Throughput Polygenic Biomarker Discovery Using Condition-Specific Gene Coexpression Networks

Biomarkers can be described as molecular signatures that are associated with a trait or disease. RNA expression data facilitates discovery of biomarkers underlying complex phenotypes because it can capture dynamic biochemical processes that are regulated in tissue-specific and time-specific manners. Gene Coexpression Network (GCN) analysis is a method that utilizes RNA expression data to identify binary gene relationships across experimental conditions. Using a novel GCN construction algorithm, Knowledge Independent Network Construction (KINC), I provide evidence for novel polygenic biomarkers in both plant and animal use cases. Kidney cancer is comprised of several distinct subtypes that demonstrate unique histological and molecular signatures. Using KINC, I have identified gene correlations that are specific to clear cell renal cell carcinoma (ccRCC), the most common form of kidney cancer. ccRCC is associated with two common mutation profiles that respond differently to targeted therapy. By identifying GCN edges that are specific to patients with each of these two mutation profiles, I discovered unique genes with similar biological function, suggesting a role for T cell exhaustion in the development of ccRCC. Medicago truncatula is a legume that is capable of atmospheric nitrogen fixation through a symbiotic relationship between plant and rhizobium that results in root nodulation. This process is governed by complex gene expression patterns that are dynamically regulated across tissues over the course of rhizobial infection. Using de novo RNA sequencing data generated from the root maturation zone at five distinct time points, I identified hundreds of genes that were differentially expressed between control and inoculated plants at specific time points. To discover genes that were co-regulated during this experiment, I constructed a GCN using the KINC software. By combining GCN clustering analysis with differentially expressed genes, I present evidence for novel root nodulation biomarkers. These biomarkers suggest that temporal regulation of pathogen response related genes is an important process in nodulation. Large-scale GCN analysis requires computational resources and stable data-processing pipelines. Supercomputers such as Clemson University’s Palmetto Cluster provide data storage and processing resources that enable terabyte-scale experiments. However, with the wealth of public sequencing data available for mining, petabyte-scale experiments are required to provide novel insights across the tree of life. I discuss computational challenges that I have discovered with large scale RNA expression data mining, and present two workflows, OSG-GEM and OSG-KINC, that enable researchers to access geographically distributed computing resources to handle petabyte-scale experiments

Performance of the Second International Mungbean Nursery

Cover title

Does a Child\u27s Entrance Age Effect 2nd and 4th Graders CAT Performance?

Second and fourth graders NCE reading, math, and language arts scores from the CAT were analyzed using a multiple regressoin and 3-way ANOVA to determine the effects of a child\u27s entrance age on achievement and whether the interaction effect was significant. The study also controlled for SES by using the children\u27s lunch payment status

B-400 : a new, early variety of winter barley for Missouri

Cover title

Performance of the First International Mungbean Nursery

Cover title.Includes bibliographical references

Performance of the Third International Mungbean Nursery

Includes bibliographical references

Performance of the Fourth International Mung-bean Nursery : summary of the first four International Mung-bean Nurseries

Title from JPEG cover page (University of Missouri Digital Library, viewed Aug. 3, 2009)

Wheat in Missouri

Cover title."This is a revision of Missouri Agricultural Experiment Station Bulletin 398 ..."--P. [3].Includes bibliographical references

Growing good crops of oats in Missouri

Revision of Bulletin 402.Cover title

Growing good crops of oats in Missouri

Revision of Bulletin 439--P. [3].Cover title.Includes bibliographical references