A Comprehensive Suite of Tools for Allele Discovery and Phenotype Improvement in Crops

Abstract

The rapid advancements in sequencing technologies have significantly expanded genomic research, generating vast amounts of genomics and phenomics data. Whole-genome re-sequencing has become an invaluable resource, enabling precise identification of alleles and functional elements within genomes. This dissertation presents a suite of computational tools and pipelines designed to enhance genomic analysis and facilitate agricultural innovation. These include the SnakyVC variant calling pipeline, the Allele Catalog pipeline, the Allele Catalog Tool, the Genomic Variation Explorer, the Phenotype Distribution pipeline, and the Phenotype Distribution Tool. By integrating data mining methodologies and leveraging these tools, researchers can pinpoint causative alleles, link genomic variations to phenotypic traits, and explore regulatory differences, including variations within promoter regions and copy number variations. The Allele Catalog Tool supports allele discovery and improvement efforts, while the Genomic Variation Explorer provides insights into the genetic and regulatory components influencing specific traits. Additionally, the Phenotype Distribution Tool aids in identifying candidate genes associated with phenotypic traits and analyzing phenotype distribution differences at the allele level. These tools collectively empower researchers and breeders to make informed decisions in selective breeding, translational research, and phenotype improvement strategies. Moreover, the development of a reverse phenotype-to-genotype approach offers a novel method for identifying potential causative genes and alleles. By making these resources publicly available, this work contributes to advancements in crop research and agricultural productivity, paving the way for more efficient and targeted breeding programs to drive innovation in agricultural science.