title: The quality of metabolic pathway resources depends on initial enzymatic function assignments: a case for maize.
abstract: As metabolic pathway resources become more commonly available, researchers have unprecedented access to information about their organism of interest. Despite efforts to ensure consistency between various resources, information content and quality can vary widely. Two maize metabolic pathway resources for the B73 inbred line, CornCyc 4.0 and MaizeCyc 2.2, are based on the same gene model set and were developed using Pathway Tools software. These resources differ in their initial enzymatic function assignments and in the extent of manual curation. We present an in-depth comparison between CornCyc and MaizeCyc to demonstrate the effect of initial computational enzymatic function assignments on the quality and content of metabolic pathway resources.
  
  
  
  These two resources are different in their content. MaizeCyc contains GO annotations for over 21,000 genes that CornCyc is missing. CornCyc contains on average 1.6 transcripts per gene, while MaizeCyc contains almost no alternate splicing. MaizeCyc also does not match CornCyc's breadth in representing the metabolic domain; MaizeCyc has fewer compounds, reactions, and pathways than CornCyc. CornCyc's computational predictions are more accurate than those in MaizeCyc when compared to experimentally determined function assignments, demonstrating the relative strength of the enzymatic function assignment pipeline used to generate CornCyc.
  
  
  
  Our results show that the quality of initial enzymatic function assignments primarily determines the quality of the final metabolic pathway resource. Therefore, biologists should pay close attention to the methods and information sources used to develop a metabolic pathway resource to gauge the utility of using such functional assignments to construct hypotheses for experimental studies.
authors.affiliation: Bioinformatics and Computational Biology Program, Iowa State University, Ames IA, USA.|Electrical and Computer Engineering Department, Iowa State University, Ames IA, USA.|USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames IA, USA.
authors.forename: Jesse R
authors.initials: JR
authors.lastname: Walsh
authors.affiliation: USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, University of Missouri, Columbia MO, USA.
authors.forename: Mary L
authors.initials: ML
authors.lastname: Schaeffer
authors.affiliation: Department of Plant Biology, Carnegie Institution for Science, Stanford CA, USA.
authors.forename: Peifen
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Department of Plant Biology, Carnegie Institution for Science, Stanford CA, USA.
authors.forename: Seung Y
authors.initials: SY
authors.lastname: Rhee
authors.affiliation: Bioinformatics and Computational Biology Program, Iowa State University, Ames IA, USA.|Electrical and Computer Engineering Department, Iowa State University, Ames IA, USA.
authors.forename: Julie A
authors.initials: JA
authors.lastname: Dickerson
authors.affiliation: Bioinformatics and Computational Biology Program, Iowa State University, Ames IA, USA. taner.sen@ars.usda.gov.|USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames IA, USA. taner.sen@ars.usda.gov.|Department of Genetics, Development and Cell Biology, Iowa State University, Ames IA, USA. taner.sen@ars.usda.gov.|USDA-ARS Crop Improvement and Genetics Research Unit, Albany CA, USA. taner.sen@ars.usda.gov.
authors.forename: Taner Z
authors.initials: TZ
authors.lastname: Sen
mesh_terms: D019295:Computational Biology; D058977:Molecular Sequence Annotation; D010940:Plant Proteins / Q000378:metabolism; D003313:Zea mays / Q000201:enzymology / Q000378:metabolism*
chemical_list: D010940:Plant Proteins
keywords: BioCyc; CornCyc; Database comparison; JavaCycO; MaizeCyc; Metabolic pathway databases