Location: Corn Insects and Crop Genetics Research
2019 Annual Report
Accomplishments
1. MaizeGDB completed a major expansion of resources to improve data access and facilitate crop improvement. In genetics and genomics research, model organism databases are critical by acting as both a data repository and as a resource for crop breeders and researchers to search, integrate, analyze, and visualize data that are essential for their work. The needs of research communities continually change as the size, scale, and types of available data are growing quickly. The genetics and genomics database for the maize research community is MaizeGDB. ARS researchers in Ames, Iowa have expanded its capabilities to adapt to the needs of the maize research community including the ability to host multiple reference-quality assemblies, support datasets related to gene expression, and provide resources to better understand the relationships between genes and phenotypes. MaizeGDB now hosts reference assemblies for 14 maize genomes including three additions in the past year. MaizeGDB now provides a tool that compares how different genes are expressed in a plant for over 150 different conditions and a curation tool to link images of traits to genes. These new resources will lead to improved crop performance by helping researchers to better understand how the genes in a plant define the potential traits that will be observed in farmers’ fields.
Review Publications
Andorf, C.M., Beavis, W.D., Hufford, M., Lubberstedt, T., Smith, S., Suza, W., Wang, K., Woodhouse, M., Yu, J. 2019. Technological advances in maize breeding: Past, present and future. Journal of Theoretical and Applied Genetics. 132(32):817-849. https://doi.org/10.1007/s00122-019-03306-3.
Schott, D.A., Vinnakota, A.G., Portwood II, J.L., Andorf, C.M., Sen, T.Z. 2018. SNPversity: A web-based tool for visualizing diversity. Database: The Journal of Biological Databases and Curation. https://doi.org/10.1093/database/bay037.
Zhou, N., Siegel, Z.D., Zarecor, S., Lee, N., Campbell, D.A., Andorf, C.M., Nettleton, D., Lawrence-Dill, C.J., Ganapathysubramanian, B., Kelly, J.W., Friedberg, I. 2018. Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning. PLoS Computational Biology. 14(7):e1006337. https://doi.org/10.1371/journal.pcbi.1006337.
Springer, N., Anderson, S., Andorf, C.M., Ahern, K., Bai, F., Barad, O., Barbazuk, W., Bass, H.W., Baruch, K., Gen-Zvi, G., Buckler IV, E.S., Bukowski, R., Campbell, M.S., Cannon, E.K., Chomet, P., Dawe, R., Davenport, R., Dooner, H.K., He Du, L., Du, C., Easterling, K., Gault, C., Guan, J., Jander, G., Hunter III, C.T., Jiao, Y., Koch, K.E., Kol, G., Kudo, T., Li, Q., Lu, F., Mayfield-Jones, D., Mei, W., McCarty, D.R., Noshay, J., Portwood II, J.L., Ronen, G., Settles, M.A., Shem-Tov, D., Shi, J., Soifer, I., Stein, J.C., Suzuki, M., Vera, D.L., Vollbrecht, E., Vrebalov, J.T., Ware, D., Wei, X., Wimalanathan, K., Woodhouse, M.R., Xiong, W., Brutnell, T.P. 2018. The maize W22 genome provides a foundation for functional genomics and transposon biology. Nature Genetics. 50:1282-1288. https://doi.org/10.1038/s41588-018-0158-0.
Harper, E.C., Campbell, J., Cannon, E.K., Jung, S., Main, D., Poelchau, M.F., Walls, R.L., Andorf, C.M., Arnaud, E., Berardini, T.Z., Birkett, C.L., Cannon, S.B., Carson, J., Condon, B., Cooper, L., Dunn, N., Elsik, C., Farmer, A., Ficklin, S., Grant, D.M., Grau, E., Hendon, N., Hu, Z., Humann, J., Jaiswal, P., Jonquet, C., Laporte, M., Larmande, P., Lazo, G.R., McCarthy, F., Menda, N., Mungall, C., Munoz-Torres, M., Naithani, S., Nelson, R., Nesdill, D., Park, C., Reecy, J., Reiser, L., Sanderson, L., Sen, T.Z., Staton, M., Subramaniam, S., Karey, T., Unda, V., Unni, D., Wang, L., Ware, D., Wegrzyn, J., Williams, J., Woodhouse, M. 2018. AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture. Database: The Journal of Biological Databases and Curation. 2018(1):1-32. https://doi.org/10.1093/database/bay088.
