Carnegie Institution Adds Four New Crop Databases

Soybean field (Agricultural Research Service/USDA)

The Carnegie Institution of Science says its Plant Metabolic Network, based in Stanford, California, has added online databases on the biochemical pathways controlling the metabolism of corn, soybeans, wine grapes, and cassava. The new additions offer a detailed view of the chemical reactions taking place in the cells of these four commercially important crops.

The Plant Metabolic Network compiles research literature and analysis into two types of collections, a multi-species reference database called PlantCyc, with data on more than 800 pathways and their catalytic enzymes and genes, as well as compounds from over 300 plant species. The network offers as well collections on individual species, such as the four new additions. The network already has species-specific collections for Arabidopsis and poplar. The site also hosts species-specific databases developed by collaborators.

The Plant Metabolic Network’s collections help scientists better understand plant biochemical processes, as well as discover previously uncharacterized enzymes important to plant life. The databases cover metabolic reactions that allow plants to convert carbon dioxide and sunlight into chemical energy, import mineral nutrients from the soil into plant roots, and help plants defend against environmental stress.

The corn, soybeans, wine grapes, and cassava data were selected for early release because of their economic and agricultural importance. “Wine grapes are an important crop for the state of California,” says Carnegie staff scientist Sue Rhee who leads the research team, noting that “corn and soybeans are the number one and two crops of the United States, both as a source of food and biofuel.” Rhee adds that cassava, also known as manioc or yucca, “is one of the most-common sources of food worldwide and a tremendously important crop for combating hunger.”

The network’s research team consists of plant scientists, scientific curators, post-doctoral scholars, and student interns, using tools that include molecular sequence analysis, artificial intelligence, statistics, plant molecular biology, and plant biochemistry. The project is funded by a National Science Foundation grant.

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