We develop approaches to facilitate the identification of protein coding genes, transcriptome assembly, functional annotation, and conserved element identification, through machine learning and computational statistics. To aid conservation and breeding efforts for plant species, we develop web-based applications that integrate data across domains so that researchers can analyze, share, and visualize this information.Such integration requires the implementation of semantic technologies and ontologies to connect genotype, phenotype, and environmental data for georeferenced accessions. We use these novel computational approaches and web-based software to address questions related to genome biology and population genomics. We are specifically interested in conservation genomics as it relates to the identification of regions of the genome of adaptive significance in forest trees.