title: Automation of gene assignments to metabolic pathways using high-throughput expression data.
abstract: BACKGROUND
  Accurate assignment of genes to pathways is essential in order to understand the functional role of genes and to map the existing pathways in a given genome. Existing algorithms predict pathways by extrapolating experimental data in one organism to other organisms for which this data is not available. However, current systems classify all genes that belong to a specific EC family to all the pathways that contain the corresponding enzymatic reaction, and thus introduce ambiguity.
  
  
  RESULTS
  Here we describe an algorithm for assignment of genes to cellular pathways that addresses this problem by selectively assigning specific genes to pathways. Our algorithm uses the set of experimentally elucidated metabolic pathways from MetaCyc, together with statistical models of enzyme families and expression data to assign genes to enzyme families and pathways by optimizing correlated co-expression, while minimizing conflicts due to shared assignments among pathways. Our algorithm also identifies alternative ("backup") genes and addresses the multi-domain nature of proteins. We apply our model to assign genes to pathways in the Yeast genome and compare the results for genes that were assigned experimentally. Our assignments are consistent with the experimentally verified assignments and reflect characteristic properties of cellular pathways.
  
  
  CONCLUSIONS
  We present an algorithm for automatic assignment of genes to metabolic pathways. The algorithm utilizes expression data and reduces the ambiguity that characterizes assignments that are based only on EC numbers.
authors.affiliation: Department of Computer Science, Cornell University, Ithaca, NY, USA. liviup@cs.cornell.edu
authors.forename: Liviu
authors.initials: L
authors.lastname: Popescu
authors.forename: Golan
authors.initials: G
authors.lastname: Yona
mesh_terms: D000465:Algorithms; D001216:Asparagine / Q000096:biosynthesis; D001330:Electronic Data Processing; D005492:Folic Acid / Q000096:biosynthesis; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism
chemical_list: D001216:Asparagine; D005492:Folic Acid