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Management and distribution of experimental data from prebreeding projects is important to ensure uptake of germplasm into breeding and research programs. Being able to access and share this data in standard formats is essential. The adoption of a common informatics platform for crops that may have limited resources brings economies of scale, allowing common informatics components to be used across multiple species. The close integration of such a platform with commonly used breeding software, visualization, and analysis tools reduces the barrier for entry to researchers and provides a common framework to facilitate collaborations and data sharing. This work presents significant updates to the Germinate platform and highlights its value in distributing prebreeding data for 14 crops as part of the project 'Adapting Agriculture to Climate Change: Collecting, Protecting and Preparing Crop Wild Relatives' (hereafter Crop Trust Crop Wild Relatives project) led by the Crop Trust (https://www.cwrdiversity.org). The addition of data on these species compliments data already publicly available in Germinate. We present a suite of updated Germinate features using examples from these crop species and their wild relatives. The use of Germinate within the Crop Trust Crop Wild Relatives project demonstrates the usefulness of the system and the benefits a shared informatics platform provides. These data resources provide a foundation on which breeding and research communities can develop additional online resources for their crops, harness new data as it becomes available, and benefit collectively from future developments of the Germinate platform. [ABSTRACT FROM AUTHOR]

CGIAR is a global research partnership of 15 geographically and scientifically diverse Centers dedicated to reducing poverty, enhancing food and nutrition security, and improving natural resource management. The Centers are charged with accelerating innovation to tackle challenges at a variety of scales from the local to the global. This requires data to be findable, accessible, interoperable, and reusable (FAIR) and inter-linked where relevant. CGIAR Centers have made strong progress in implementing publication and data repositories; however, many of these still represent silos whose contents are not generally easily discoverable or inter-linked (e.g., agronomic trial data with socioeconomic or adoption data in the same geographies). In the absence of such interoperability-mediated discovery, “open” is of limited utility. The overall goal is for CGIAR’s trove of research data and associated information to be indexed and interlinked through a demand-driven cyberinfrastructure for agriculture, ensuring that research outputs are discoverable by humans and machines, and reusable via appropriate licensing to enhance innovation, uptake and impact. There are challenges to achieving this goal, not only across CGIAR, but for the agricultural domain in general. Among the foremost hurdles is that “open” tends to remain an unfunded mandate, making it difficult to operationalize effectively. Further, there is still significant concern on the part of scientists about making data open – largely centered around issues of trust, time, and quality – resulting in repositories frequently exposing metadata rather than the data sets themselves. While the ability to find metadata about resources qualifies as improvement, it continues to impose barriers to data access, discoverability, integration, and analysis, without which complex challenges to global agriculture development cannot be effectively addressed. CGIAR is addressing the urgent need to create a data sharing culture and enabling environment for Open Access and Open Data (OA/OD) that includes projects planning for OA/OD and allocating funds to support it, in parallel with the technical infrastructure mentioned above. While the technology necessary to enable FAIR outputs exists, achieving success implies data provider and consumer trust and buy-in, agreement and adherence to interoperability standards and/or mapping across varied approaches, and compliance with guidelines (including those on citation and licensing governing content reuse). [ABSTRACT FROM AUTHOR]

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes. [ABSTRACT FROM AUTHOR]

The volume of Arabidopsis information has increased enormously in recent years as a result of the sequencing of the reference genome and other large-scale functional genomics projects. Much of the data is stored in public databases, where data are organized, analyzed, and made freely accessible to the research community. These databases are resources that researchers can utilize for making predictions and developing testable hypotheses. The methods in this chapter describe ways to access and utilize Arabidopsis data and genomic resources found in databases and stock centers.

Metabolic databases such as The Plant Metabolic Network/MetaCyc and KEGG PATHWAY are publicly accessible resources providing organism-specific information on reactions and metabolites. KEGG PATHWAY depicts metabolic networks as wired, electronic circuit-like maps, whereas the MetaCyc family of databases uses a canonical textbook-like representation. The first MetaCyc-based database for a plant species was AraCyc, which describes metabolism in the model plant Arabidopsis. This database was created over 10 years ago and has since then undergone extensive manual curation to reflect updated information on enzymes and pathways in Arabidopsis. This chapter describes accessing and using AraCyc and its underlying Pathway Tools software. Specifically, methods for (1) navigating Pathway Tools, (2) visualizing omics data and superimposing the data on a metabolic pathway map, and (3) creating pathways and pathway components are discussed.