| Summary: | Even under the best management strategies contemporary crops face yield losses from diverse threats such as, pathogens, pests, and environmental stress. Adding to this management challenge is that under current global climate projections these impacts are predicted to become even greater. Natural genetic variation, long used by traditional plant breeders, holds great promise for adapting high performing agronomic lines to these stressors. Yet, efforts to bolster crop plant resilience using wild relatives have been hindered by time consuming efforts to develop genomic tools and/or identify the genetic basis for agronomic traits. Thus, increasing crop plant resilience requires developing and deploying approaches that leverage current high-throughput sequencing technologies to more rapidly and robustly develop genomic tools in these systems. Here we report the integration of bioinformatic and statistical tools to leverage high-throughput sequencing to 1) develop a machine learning approach to determine factors impacting transcriptome assembly and quantitatively evaluate transcriptome completeness, 2) dissect complex physiological pathway interactions in Solanum pimpinellifolium under combined stresses—using comparative transcriptomics, and 3) develop a genome assembly pipeline that can be deployed to rapidly assemble a more contiguous genome, unraveling previously hidden complexity, using Phytopthora capsici as a model. As a result, we have generated strategic guidelines for transcriptome assembly and developed an orthologue and reference free, machine learning based tool "WWMT" to quantitatively score transcriptome completeness from short read data. Secondly, we identified "hub genes" and describe genes involved with "cross-talk" between drought and herbivore stress response pathways. Finally, we demonstrate a protocol for combining long-read sequencing from the Oxford Nanopore Technologies MinION, and short-read data, to rapidly assembly a cost-effective, contiguous and relatively complete genome. Here we uncovered hidden variation in a well-known plant pathogen finding that the genome was 92% bigger than previous estimates with more than 39% of duplicated regions, supporting a hypothesized recent whole genome duplication in this clade. This community resource will support new functional and evolutionary studies in this economically important pathogen. === Doctor of Philosophy === Meeting the food production demands of a burgeoning population in a changing environment, means adapting crop plants to become more resilient to environmental stress. One of the greatest barriers to understanding and predicting crop responses to future environmental change is our poor understanding of the functional and genomic basis of stress resistance traits for contemporary crops. This impediment presents a barrier for rapid crop improvement technologies, such as, gene editing or genomic selection, that is only partially overcome by generating large amounts of sequencing data. Here we need tools that allow us to process and evaluate huge amounts of data generated from next generation sequencing studies to help identify genomic regions associated with agronomic traits. We also need technical approaches that allow us to disentangle the complex genetic interactions that drive plant stress responses. Here we present work that used statistical analysis and recent advances of artificial intelligence to develop a bioinformatic approach to evaluate genomic sequencing data prior to downstream analyses. Secondly, we used a reductionist approach to filter thousands of genes to key genes associated with combined stress responses (herbivory and drought), in the most widely used vegetable in the world, tomato. Finally, we developed a method for generating whole genome sequences that is low-cost and time sensitive and tested it using a well-known plant pathogen genome, wherein we unraveled significant hidden complexity. Overall this work provides community-wide genomic tools and information to promote crop resilience.
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