Cyberinfrastructure and resources to enable an integrative approach to studying forest trees

• Main Authors: Jill L. Wegrzyn, Taylor Falk, Emily Grau, Sean Buehler, Risharde Ramnath, Nic Herndon
• Format: Article
• Language: English
• Published: Wiley 2020-01-01
• Series: Evolutionary Applications
• Subjects: cyberinfrastructure; FAIR; phenomics; plant ontologies; population genetics; tree databases
• Online Access: https://doi.org/10.1111/eva.12860

Abstract Sequencing technologies and bioinformatic approaches are now available to resolve the challenges associated with complex and heterozygous genomes. Increased access to less expensive and more effective instrumentation will contribute to a wealth of high‐quality plant genomes in the next few years. In the meantime, more than 370 tree species are associated with public projects in primary repositories that are interrogating expression profiles, identifying variants, or analyzing targeted capture without a high‐quality reference genome. Genomic data from these projects generates sequences that represent intermediate assemblies for transcriptomes and genomes. These data contribute to forest tree biology, but the associated sequence remains trapped in supplemental files that are poorly integrated in plant community databases and comparative genomic platforms. Successful implementation of life science cyberinfrastructure is improving data standards, ontologies, analytic workflows, and integrated database platforms for both model and non‐model plant species. Unique to forest trees with large populations that are long‐lived, outcrossing, and genetically diverse, the phenotypic and environmental metrics associated with georeferenced populations are just as important as the genomic data sampled for each individual. To address questions related to forest health and productivity, cyberinfrastructure must keep pace with the magnitude of genomic and phenomic sampling of larger populations. This review examines the current landscape of cyberinfrastructure, with an emphasis on best practices and resources to align community data with the Findable, Accessible, Interoperable, and Reusable (FAIR) guidelines.