Legume Information System
Legumeinfo.org is a USDA-funded collaborative, community resource to facilitate crop improvement by integrating genetic, genomic, and trait data across legume species.
Funded research and projects in development.
Legumeinfo.org is a USDA-funded collaborative, community resource to facilitate crop improvement by integrating genetic, genomic, and trait data across legume species.
The Legume Federation is an NSF project to help foster increased use of data standards, distributed development, and comparative analysis, to support research across the legume family, and to support robust agriculture for a world that is significantly "legume-fed".
Red alder is a hardwood tree of economic importance in the Pacific Northwest, exceeding the annual value of the dominant softwood species Douglas-fir. Red alder is used for quality cabinetry, utensils and handles, and as a biomass/biofuels crop. It grows in inhospitable environments and on unproductive land, meaning that it does not compete with food production. Demand for saplings suitable for planting currently greatly exceeds supply. With better understanding of its genetics, scientists would be able to breed trees that grow faster and in more inhospitable environments—this would expand the area available for growing, and increase the annual yield for timber and biomass fuel. Additionally, red alder does not require nitrogen fertilizers. It achieves this by harnessing Frankia, a microbe, to provide the nitrogen for it. The relationship between the tree and the microbe requires scientific study—some Frankia strains are better than others, and some combinations of tree and microbial strains are well suited to certain environments. This research will help us understand how the red alder genetics, soil type, Frankia strain, and other bacteria in the soil interact to promote tree growth. The result will be the ability to develop new tree strains suited to certain environments, expand planting, and meet growing demand. Our research includes training opportunities for students high school through graduate level) and supports a high school summer innovation academy that targets low-income students. It promotes access to research by native and Hispanic underrepresented groups. The project is a collaboration between NCGR and Washington State University.
In this project we applied the principles and methods pioneered in the study of single neurones to bring the benefits of single cell transcriptomics to plant biology, to increase the precision of transcriptome studies, to reveal previously undetectable differences in molecular composition among different cell types, and to discover the gene expression patterns that underlie phenomena that can only be studied in single cells.
Given the prevalence of complex heritable diseases and the need to better understand their genomic origin in order to improve treatments, investigation of new analysis techniques is important. The tool we are working on here combines two relatively new analysis concepts: pan-genomic graphs, which represent a large cohort of individuals’ genomes with paths through a graph of DNA sequence nodes; and Frequented Regions, a novel way of describing genomic variation within a pan-genomic graph. We combine these two concepts with the growing field of machine learning in order to produce a supervised classification algorithm for human diseases.
The New Mexico INBRE program* (prime NMSU) champions biomedical and community based research excellence in the state of New Mexico through the development of innovative, supportive and sustainable research environments for faculty, students, and the community. The program's goal is to develop lead scientists and educators that grow the state's technical and scientific workforce, while building a biomedical network regionally and nationally. The New Mexico INBRE Sequencing and Bioinformatics Core (SBC) at NCGR supports this mission by providing bioinformatics resources, tools, education, and training to enable researchers and students rapid progress in their genomic studies.
*Funded by the Institutional Development Award (IDeA) of the National Institutes of Health (NIH) & National Institute of General Medical Sciences (NIGMS) Grant # P20GM103451
BRaID will complement existing microbial databases by developing a data representation paradigm that solves the problems posed by the challenge arising from the unique combination of data, and will implement new metrics and algorithms for the formal description and analysis of biofilms data. This project aims to provide the premier resource that offers users the ability to ask complex questions (including those currently impossible to answer), permitting quantitative comparisons among biofilms, supporting remediation efforts, and enabling predictions about outcomes.
Even within a single species, there can be large amounts of variation between individual genomes. In order to better understand how genetic variation drives phenotypic differences within a species, we develop pangenomic software that can model this variation across multiple genome assemblies, forming a pangenome that captures more of the species variation than is present in any one individual. We also research machine learning approaches for connecting regions of the pangenome to phenotypic outcomes.
Extracellular vesicles are submicroscopic particles produced by most plant, animal and bacterial cells that are potent vehicles of cell-to-cell communication by delivering proteins, fats, carbohydrates, and gene information to recipient cells, which influences their behavior and function. Extracellular vesicles garnered interest as therapeutic targets since they are implicated in promoting viral infection, cell damage in neurological diseases, and cancer. Common techniques to study the uptake and distribution of these vesicles in cells and the body are laborious and time consuming. In this project, we will develop a new and efficient method to study vesicle uptake and distribution in mice on a large scale that will provide a tool for future studies on vesicle destination sites with implications in medicine as specific vesicles may be exposed as therapeutic targets.
Diversify and broaden the scientific workforce
Engage elementary and middle student interest using DNA-based case studies
Why so young?
By connecting with younger students, we can engage and sustain student interest and help students to see themselves as scientists. This will help prepare them to follow science career pathways to research and address fundamentally important challenges.
Partnering with Teachers
We partner with New Mexico elementary and middle school teachers through summer experiences. Teachers improve their science knowledge, especially around DNA, receive and get training on a bioinformatics-based kit of interesting, engaging instructional materials, and develop, with support, a DNA-based instructional unit for their classrooms.
The legume Medicago truncatula represents an important model system for symbiosis given its interactions with nitrogen fixing bacteria and mycorrhizal fungi. This project generated a HapMap resource and genome assemblies of the plant and its symbionts, explored population genomics, analyzed gene families, especially those involved in symbiosis, and connected genetic variants to phenotypic differences.
Maternal nutrient restriction can lead to numerous long-term health consequences in fetal and postnatal development. Using the baboon as a model for human pregnancy, the focus of this project is to investigate the role of fetal imprinting in the undernourished environment and identify biochemical pathways that are significantly altered in response.
Genetic and genomic data to enable more rapid crop improvement in peanut.
In collaboration with the USDA, we are developing a genome assembly and annotation of the alfalfa perennial genotype, RegenSY27x, which is important in alfalfa breeding for its transformation and regeneration capacity.
DNA sequence data is becoming ubiquitous in various domains of science, such as medicine and agriculture. However, the volume of these data and the rate at which they are being generated is rapidly outpacing storage and analysis capabilities. This project aims to address both the storage and analysis issues by developing new techniques for compressing DNA sequence data such that analyses can be performed directly on the compressed data. Specifically, the project aims to compress collections of DNA sequence data from the same species, or pangenomes. In addition to reducing data storage costs and transmission times, this will enable the analysis of pangenomes at an unprecedented scale which could aid researchers seeking to understand the genetic basis of complex diseases in medical contexts, or similarly complex traits that are targets of directed breeding efforts in agricultural.
The National Center for Genome Resources is a not-for-profit research institute that innovates, collaborates, and educates in the field of genomic data science. As leaders in DNA sequence analysis, we partner with government, industry, and academia to drive biological discovery in all kingdoms of life. We deliver value through expertise in experimental design, software, computation, data integration and training a skilled workforce.More
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