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ARS Home » Midwest Area » Ames, Iowa » Corn Insects and Crop Genetics Research » Research » Publications at this Location » Publication #327219
Title: Maize - GO annotation methods, evaluation, and review (Maize-GAMER)

Author
item WIMALANATHAN, KOKULAPALAN - Iowa State University
item FRIEDBERG, IDDO - Iowa State University
item Andorf, Carson
item LAWRENCE-DILL, CAROLYN - Iowa State University

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/16/2016
Publication Date: 3/17/2016
Citation: Wimalanathan, K., Friedberg, I., Andorf, C.M., Lawrence-Dill, C. 2016. Maize - GO annotation methods, evaluation, and review (Maize-GAMER). In: 58th Annual Maize Genetics Conference, March 17-20, 2016, Jacksonville, Florida. p. 87.

Interpretive Summary:

Technical Abstract: Making a genome sequence accessible and useful involves three basic steps: genome assembly, structural annotation, and functional annotation. The quality of data generated at each step influences the accuracy of inferences that can be made, with high-quality analyses produce better datasets resulting in stronger hypotheses for downstream experimentation. Here we report on our efforts to assess existing functional annotations for maize as well as to generate not only a high-quality functional annotation set for B73, but a freely available pipeline that enables others to carry out the same process on other inbred lines and plant genomes. Our pipeline makes use of methods developed for the Critical Assessment of Function Annotation (CAFA) competition (see http://biofunctionprediction.org/ for details) and enables experts to endorse or reject annotations to further improve the quality of available functional annotations. Preliminary results from the pipeline suggest that a combined system based on multiple methods increases both the number of genes that are assigned at least one functional annotation (GO term) and the quality of functional assignments on average (as compared to the existing Gramene/Ensemble pipeline). A downstream component of the pipeline enables review of functional assignment by experts, which promises to improve the confidence-level of evidence codes associated with GO terms assigned by these computational methods.