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Genome-wide molecular gene expression studies generally compare expression values for each gene across multiple conditions followed by cluster and gene set enrichment analysis to determine whether differentially expressed genes are enriched in specific biochemical pathways, cellular components, biological processes, and/or molecular functions, etc. This approach to analyzing differences in gene expression enables discovery of gene function, but is not useful to determine whether pre-defined groups of genes share or diverge in their expression patterns in response to treatments nor to assess the correctness of pre-defined gene set groupings. Here we present a simple method that changes the dimension of comparison by treating genes as variable traits to directly assess significance of differences in expression levels among pre-defined gene groups. Because expression distributions are typically skewed (thus unfit for direct assessment using Gaussian statistical methods) our method involves transforming expression data to approximate a normal distribution followed by dividing the genes into groups, then applying Gaussian parametric methods to assess significance of observed differences. This method enables the assessment of differences in gene expression distributions within and across samples, enabling hypothesis-based comparison among groups of genes. We demonstrate this method by assessing the significance of specific gene groups’ differential response to heat stress conditions in maize.AbbreviationsGO– gene ontology HSP – heat shock proteinKEGG– Kyoto Encyclopedia of Genes and GenomesHSF TF– heat shock factor transcription factorHSBP– heat shock binding proteinRNA– ribonucleic acidTE– transposable elementTF– transcription factorTPM– transcripts per kilobase millions

The CRISPR-Cas9 system (clustered regularly interspaced short palindromic repeats with associated Cas9 protein) has been used to generate targeted changes for direct modification of endogenous genes in an increasing number of plant species; but development of plant genome editing has not yet fully considered potential off-target mismatches that may lead to unintended changes within the genome. Assessing the specificity of CRISPR-Cas9 for increasing editing efficiency as well as the potential for unanticipated downstream effects from off-target mutations is an important regulatory consideration for agricultural applications. Increasing genome-editing specificity entails developing improved design methods that better predict the prevalence of off-target mutations as a function of genome composition and design of the engineered ribonucleoprotein (RNP). Early results from CRISPR-Cas9 genome editing in plant systems indicate that the incidence of off-target mutation frequencies is quite low; however, by analyzing CRISPR-edited plant lines and improving both computational tools and reagent design, it may be possible to further decrease unanticipated effects at potential mismatch sites within the genome. This will provide assurance that CRISPR-Cas9 reagents can be designed and targeted with a high degree of specificity. Improved and experimentally validated design tools for discriminating target and potential off-target positions that incorporate consideration of the designed nuclease fidelity and selectivity will help to increase confidence for regulatory decision making for genome-edited plants.

Field-based, high-throughput phenotyping enables the detailed characterization of plant populations under relevant conditions, providing valuable biological insight into the life history of plants. ### Glossary FB-HTP : field-based, high-throughput phenotyping EM : electromagnetic radiation 3D : three-dimensional UAS : unmanned aircraft system QTL : quantitative trait locus GPR : ground-penetrating radar ERT : electrical resistivity tomography EQ : entity quality IPPN : International Plant Phenotyping Network CGM : crop growth model * ### Glossary FB-HTP : field-based, high-throughput phenotyping EM : electromagnetic radiation 3D : three-dimensional UAS : unmanned aircraft system QTL : quantitative trait locus GPR : ground-penetrating radar ERT : electrical resistivity tomography EQ : entity quality IPPN : International Plant Phenotyping Network CGM : crop growth model

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments. Fil: Thessen, Anne E.. Ronin Institute for Independent Scholarship; Estados Unidos Fil: Bunker, Daniel E.. New Jersey Institute of Technology; Estados Unidos Fil: Buttigieg, Pier Luigi. Helmholtz-Zentrum für Polar-und Meeresforschung; Alemania Fil: Cooper, Laurel D.. State University of Oregon; Estados Unidos Fil: Dahdul, Wasila M.. University of South Dakota; Estados Unidos Fil: Domisch, Sami. University of Yale; Estados Unidos Fil: Franz, Nico M.. Arizona State University; Estados Unidos Fil: Jaiswal, Pankaj. State University of Oregon; Estados Unidos Fil: Lawrence-Dill, Carolyn J.. University of Iowa; Estados Unidos Fil: Midford, Peter E.. No especifica; Fil: Mungall, Christopher J.. Lawrence Berkeley National Laboratory; Estados Unidos Fil: Ramirez, Martin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; Argentina Fil: Specht, Chelsea D.. University of California at Berkeley; Estados Unidos Fil: Vogt, Lars. Universitat Bonn; Alemania Fil: Vos, Rutger Aldo. Naturalis Biodiversity Center; Países Bajos Fil: Walls, Ramona L.. Arizona State University; Estados Unidos Fil: White, Jeffrey W.. United States Department of Agriculture; Estados Unidos Fil: Zhang, Guanyang. Arizona State University; Estados Unidos Fil: Deans, Andrew R.. State University of Pennsylvania; Estados Unidos Fil: Huala, Eva. Phoenix Bioinformatics; Estados Unidos Fil: Lewis, Suzanna E.. Lawrence Berkeley National Laboratory; Estados Unidos Fil: Mabee, Paula M.. University of South Dakota; Estados Unidos