In recent years the kinesin superfamily has become so large that several different naming schemes have emerged, leading to confusion and miscommunication. Here, we set forth a standardized kinesin nomenclature based on 14 family designations. The scheme unifies all previous phylogenies and nomenclature proposals, while allowing individual sequence names to remain the same, and for expansion to occur as new sequences are discovered.

Summary: CRISPR/Cas9 and Cas12a (Cpf1) nucleases are two of the most powerful genome editing tools in plants. In this work, we compared their activities by targeting maize glossy2 gene coding region that has overlapping sequences recognized by both nucleases. We introduced constructs carrying SpCas9‐guide RNA (gRNA) and LbCas12a‐CRISPR RNA (crRNA) into maize inbred B104 embryos using Agrobacterium‐mediated transformation. On‐target mutation analysis showed that 90%–100% of the Cas9‐edited T0 plants carried indel mutations and 63%–77% of them were homozygous or biallelic mutants. In contrast, 0%–60% of Cas12a‐edited T0 plants had on‐target mutations. We then conducted CIRCLE‐seq analysis to identify genome‐wide potential off‐target sites for Cas9. A total of 18 and 67 potential off‐targets were identified for the two gRNAs, respectively, with an average of five mismatches compared to the target sites. Sequencing analysis of a selected subset of the off‐target sites revealed no detectable level of mutations in the T1 plants, which constitutively express Cas9 nuclease and gRNAs. In conclusion, our results suggest that the CRISPR/Cas9 system used in this study is highly efficient and specific for genome editing in maize, while CRISPR/Cas12a needs further optimization for improved editing efficiency.

MaizeGDB is a highly curated, community-oriented database and informatics service to researchers focused on the crop plant and model organism Zea mays ssp. mays. Although some form of the maize community database has existed over the last 25 years, there have only been two major releases. In 1991, the original maize genetics database MaizeDB was created. In 2003, the combined contents of MaizeDB and the sequence data from ZmDB were made accessible as a single resource named MaizeGDB. Over the next decade, MaizeGDB became more sequence driven while still maintaining traditional maize genetics datasets. This enabled the project to meet the continued growing and evolving needs of the maize research community, yet the interface and underlying infrastructure remained unchanged. In 2015, the MaizeGDB team completed a multi-year effort to update the MaizeGDB resource by reorganizing existing data, upgrading hardware and infrastructure, creating new tools, incorporating new data types (including diversity data, expression data, gene models, and metabolic pathways), and developing and deploying a modern interface. In addition to coordinating a data resource, the MaizeGDB team coordinates activities and provides technical support to the maize research community. MaizeGDB is accessible online at http://www.maizegdb.org.

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements.

We created a new high‐coverage, robust, and reproducible functional annotation of maize protein‐coding genes based on Gene Ontology (GO) term assignments. Whereas the existing Phytozome and Gramene maize GO annotation sets only cover 41% and 56% of maize protein‐coding genes, respectively, this study provides annotations for 100% of the genes. We also compared the quality of our newly derived annotations with the existing Gramene and Phytozome functional annotation sets by comparing all three to a manually annotated gold standard set of 1,619 genes where annotations were primarily inferred from direct assay or mutant phenotype. Evaluations based on the gold standard indicate that our new annotation set is measurably more accurate than those from Phytozome and Gramene. To derive this new high‐coverage, high‐confidence annotation set, we used sequence similarity and protein domain presence methods as well as mixed‐method pipelines that were developed for the Critical Assessment of Function Annotation (CAFA) challenge. Our project to improve maize annotations is called maize‐GAMER (GO Annotation Method, Evaluation, and Review), and the newly derived annotations are accessible via MaizeGDB (http://download.maizegdb.org/maize-GAMER) and CyVerse (B73 RefGen_v3 5b+ at doi.org/10.7946/P2S62P and B73 RefGen_v4 Zm00001d.2 at doi.org/10.7946/P2M925).

Field-based, high-throughput phenotyping enables the detailed characterization of plant populations under relevant conditions, providing valuable biological ins