Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the AgBioData Consortium

Over the last several decades, there has been rapid growth in the number and scope of agricultural genetics, genomics and breeding (GGB) databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as 'databases' throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, conducted a survey to assess the status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data sharing practices by AgBioData databases are in a healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that ontology use has not substantially changed since a similar survey was conducted in 2017. We recommend 1) providing training for database personnel in specific data sharing techniques, as well as in ontology use; 2) further study on what metadata is shared, and how well it is shared among databases; 3) promoting an understanding of data sharing and ontologies in the stakeholder community; 4) improving data sharing and ontologies for specific phenotypic data types and formats; and 5) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means.Comment: 17 pages, 8 figure

Food Industry Views on Pulse Flour-Perceived Intrinsic and Extrinsic Challenges for Product Utilization

Pulses such as beans, chickpeas, peas, and lentils are typically consumed whole, but pulse flours will increase their versatility and drive consumption. Beans are the most produced pulse crop in the United States, although their flour use is limited. To expand commercial applications, knowledge of pulse flour attributes important to the food industry is needed. This research aimed to understand the food industry's needs and barriers for pulse flour utilization. An online survey invitation was sent via direct email to individuals employed in food companies developing wheat flour products. A survey weblink was distributed by pulse commodity boards to their membership. Survey questions asked food manufacturers about intrinsic factors of pulse flours that were satisfactory or challenging, and extrinsic factors for use such as market demand. Of the 75 complete responses, 21 currently or had previously used pulse flours in products, and 54 were non-users of pulse flours. Ten users indicated that there were challenges with pulse flours while five did not. Two of the most selected challenges of end-product qualities were flavor and texture. Over half of the respondents were unfamiliar with bean flour. Increasing awareness of bean flours and their attributes coupled with market demand for pulse flour-based products may be the most important extrinsic factors to increasing use among food manufacturers rather than supply or cost.This article is published as Sadohara R, Winham DM, Cichy KA. Food Industry Views on Pulse Flour-Perceived Intrinsic and Extrinsic Challenges for Product Utilization. Foods. 2022 Jul 20;11(14):2146. doi: 10.3390/foods11142146. Posted with permission

Food Industry Views on Pulse Flour—Perceived Intrinsic and Extrinsic Challenges for Product Utilization

Pulses such as beans, chickpeas, peas, and lentils are typically consumed whole, but pulse flours will increase their versatility and drive consumption. Beans are the most produced pulse crop in the United States, although their flour use is limited. To expand commercial applications, knowledge of pulse flour attributes important to the food industry is needed. This research aimed to understand the food industry’s needs and barriers for pulse flour utilization. An online survey invitation was sent via direct email to individuals employed in food companies developing wheat flour products. A survey weblink was distributed by pulse commodity boards to their membership. Survey questions asked food manufacturers about intrinsic factors of pulse flours that were satisfactory or challenging, and extrinsic factors for use such as market demand. Of the 75 complete responses, 21 currently or had previously used pulse flours in products, and 54 were non-users of pulse flours. Ten users indicated that there were challenges with pulse flours while five did not. Two of the most selected challenges of end-product qualities were flavor and texture. Over half of the respondents were unfamiliar with bean flour. Increasing awareness of bean flours and their attributes coupled with market demand for pulse flour-based products may be the most important extrinsic factors to increasing use among food manufacturers rather than supply or cost

Seed coat color genetics and genotype × environment effects in yellow beans via machine-learning and genome-wide association

Common bean (Phaseolus vulgaris L.) is consumed worldwide, with strong regional preferences for seed appearance characteristics. Colors of the seed coat, hilum ring, and corona are all important, along with susceptibility to postharvest darkening, which decreases seed value. This study aimed to characterize a collection of 295 yellow bean genotypes for seed appearance and postharvest darkening, evaluate genotype × environment (G × E) effects and map those traits via genome-wide association analysis. Yellow bean germplasm were grown for 2 yr in Michigan and Nebraska and seed were evaluated for L*a*b* color values, postharvest darkening, and hilum ring and corona colors. A model to exclude the hilum ring and corona of the seeds, black background, and light reflection was developed by using machine learning, allowing for targeted and efficient L*a*b* value extraction from the seed coat. The G × E effects were significant for the color values, and Michigan-grown seeds were darker than Nebraska-grown seeds. Single-nucleotide polymorphisms (SNPs) were associated with L* and hilum ring color on Pv10 near the J gene involved in mature seed coat color and hilum ring color. A SNP on Pv07 associated with L*, a*, postharvest darkening, and hilum ring and corona colors was near the P gene, the ground factor gene for seed coat color expression. The machine-learning-aided model used to extract color values from the seed coat, the wide variability in seed morphology traits, and the associated SNPs provide tools for future breeding and research efforts to meet consumers’ expectations for bean seed appearance

Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the Agbiodata Consortium

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as 'databases' throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL  https://www.agbiodata.org/databases