Legume Information System/Legume Federation

Apios americana an herbaceous perennial legume with climbing, vinous habit, produces underground stem tubers along stolons A americana was wild harvested by Native Americans throughout eastern North America There is also some evidence of cultivation and transport of landraces Early historical records of Apios in North America describe its important role in the diet of numerous tribes Cooking methods included boiling, frying, drying and grinding into flour They were cooked, variously, with maple syrup, animal fat, roasted, or used in stews Apios remains promising as a crop, particularly for its perenniality its ability to tolerate wet soils, and its adaptation across much of eastern North America, and its nutritional value with high levels of protein and complex starches

Over the last 40 years, several researchers have worked to develop improved cultivars of Apios americana. Notable improvements have been made in yield and tuber size. Research over this period has also led to better understanding of genetic and genomic limitations (e.g. sterile triploid populations) and characteristics of floral biology (an unusual tripping mechanism in the flower, which has made manual pollination difficult). Some genomic resources have also been developed, including transcriptomic assemblies and a genetic map.

Legumes, comprising one of the largest, most diverse, and most economically important plant families, are the subject of vibrant research and development worldwide. Continued improvement of legume crops will benefit from the recent proliferation of genetic (including genomic) resources; but the diversity, scale, and complexity of these resources presents challenges to those managing and using them. A workshop held in March of 2019 addressed questions of data resources and priorities for the legumes. The workshop identified various needs and recommendations: (a) Develop strategies to effectively store, integrate, and relate genetic resources collected in different projects. (b) Leverage information collected across many legume species by standardizing data formats and ontologies, improving the state of metadata about datasets, and increasing use of the FAIR data principles. (c) Advocate for the critical role that curators exercise in integrating complex datasets into databases and adding high value metadata that enable downstream analytics and facilitate practical applications. (d) Implement standardized software and database development practices to best leverage limited developer time and expertise gained from the various legume (and other) species. (e) Develop tools and databases that can manage genetic information for the world's plant genetic resources, enabling efficient incorporation of important traits into breeding programs. (f) Centralize information on databases, tools, and training materials and establish funding streams to support training and outreach.

As sequencing prices drop, genomic data accumulates-seemingly at a steadily increasing pace. Most genomic data potentially have value beyond the initial purpose-but only if shared with the scientific community. This, of course, is often easier said than done. Some of the challenges in sharing genomic data include data volume (raw file sizes and number of files), complexities, formats, nomenclatures, metadata descriptions, and the choice of a repository. In this paper, we describe 10 quick tips for sharing open genomic data.

Quantitative trait loci (QTL) analysis is often the starting point for dissecting underlying genetic mechanisms of complex traits. To make use of the many QTL mapping studies in legumes, methods are needed for integrating QTLs from various studies within a species. We describe the approaches used in public databases for soybean (soybase.org), common bean (legumeinfo.org) and peanut (peanutbase.org). In SoyBase, QTL coordinates have been projected onto a composite reference map by manual interpolation with respect to common markers. In LegumeInfo and PeanutBase, we have projected QTLs from the experiment maps to the consensus maps where possible, using a percentile length calculation method along with linked marker information. Briefly, the lengths of individual linkage groups and the QTL intervals are recorded from a publication linkage map using the ImageJ software and converted into centimorgans using a perl script. The publication linkage groups lengths are adjusted to the corresponding consensus linkage groups, and QTL intervals are placed using closely linked marker and flanking marker information. The associated QTL descriptive information (trait descriptions, R-squared values, additivity, parents, etc.) is collected into standardized spreadsheet forms and then loaded into databases at legumeinfo.org and peanutbase.org. To avoid discrepancies in trait nomenclature between different publications and species, unified trait ontologies are utilized for each trait. Overall, the QTL methods described here can facilitate the use of published QTL information to design breeding experiments, conduct meta-QTL analysis, and facilitate reverse genetic experiments.

LIS (legumeinfo.org) is a resource for trait genetics and comparative genomics for legumes. The site hosts annotated genomes for nine species: common bean, chickpea, pigeonpea, Medicago truncatula, Lotus japonicus, mungbean, soybean (SoyBase.org) and two Arachis species (PeanutBase.org). A major effort at LIS is to leverage data from information-rich species, such as soybean and Medicago, to aid the interpretation of data from other species, using phylogenetic and synteny-based approaches. Genes from all hosted genomes have been placed into ~18,500 gene families - searchable and viewable as gene trees and multiple alignments. These families enable traversal among orthologous and paralogous sequences across the legumes. This complements functional annotations based on protein domains and multi-species microsynteny views using a genome “context viewer” showing genomic regions with similar local gene content and ordering. Chromosome-scale synteny blocks are presented in per-species genome browsers. The other emphasis at LIS is integration of genetic and genomic data. QTLs from many studies (so far focused on common bean and peanut) have been collected and integrated into a common database, and projected onto composite genetic maps (in CMap) when possible. Molecular markers are being mapped on to the genome to make possible traversal from traits to genome, and vice versa. Germplasm data is also being incorporated in readiness for future sequence-based genotyping and phenotype data. LIS, funded by the USDA-ARS and jointly developed with NCGR, is a major component of the NSF-funded Legume Federation project that promotes sharing common resources and standards among its member databases.