bioRxiv preprint doi: https://doi.org/10.1101/222836; this version posted November 21, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Maize GO Annotation - Methods, Evaluation, and Review (maize-GAMER) Kokulapalan Wimalanathan1,2, Iddo Friedberg1,3, Carson M. Andorf4,5, and Carolyn J. Lawrence-Dill1,2,6,* 1Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50011, USA 2Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA 50011, USA 3Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA 50011, USA 4USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA 5Department of Computer Science, Iowa State University, Ames, IA 50011, USA 6Department of Agronomy, Iowa State University, Ames, IA 50011, USA *Correspondence to triffi
[email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/222836; this version posted November 21, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Summary We created a new high-coverage, robust, and reproducible functional annota- tion of maize protein coding genes based on Gene Ontology (GO) term assign- ments. Whereas the existing Phytozome and Gramene maize GO annotation sets only cover 41% and 56% of maize protein coding genes, respectively, this study provides annotations for 100% of the genes.