TY - JOUR T1 - The CanOE Strategy: Integrating Genomic and Metabolic Contexts across Multiple Prokaryote Genomes to Find Candidate Genes for Orphan Enzymes A1 - Smith, Adam Alexander Thil A1 - Belda, Eugeni A1 - Viari, Alain A1 - Medigue, Claudine A1 - Vallenet, David Y1 - 2012/05/31 N2 - Author Summary The discovery of the various metabolic functions catalyzed by enzymes encoded by the genes from the exponentially increasing number of sequenced genomes is one of the main focuses of bioinformatics tools today. However, most of these tools rely on already identified enzyme-coding gene or protein sequence information to predict known enzymatic activities in new genomes. Therefore, they cannot be used to reveal metabolic activities without any corresponding sequenced genes, dubbed “sequence-orphan activities”. In such cases, the best approach is the bioanalysis of target genes by human expert curators, manually integrating so-called “context-based information” (such as gene co-localization on the genome, or the presence of incomplete metabolic pathways) to infer novel functions. Few bioinformatics tools exploit such information and render accessible results in an automated way. Here, we present “CanOE”, a strategy that uses contextual information to propose and rank Candidate genes for Orphan Enzymes in Bacteria and Archaea. Beyond the merit of extending our knowledge and comprehension of prokaryote metabolism, identifying coding genes for sequence-orphan activities opens new opportunities for functional annotation (homology-based transfer made accessible), drug design (new metabolic targets), synthetic biology (new building blocks) and biotechnology applications (new biocatalysts). JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 8 IS - 5 UR - https://doi.org/10.1371/journal.pcbi.1002540 SP - e1002540 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002540 ER -