Predicting metabolic pathways from bacterial operons and regulons

The pathway extraction tool predicts metabolic pathways from sets of functionally related enzyme-coding genes [Faust, et al., Bioinformatics 2010, 26:1211]. In contrast to pathway projection approaches, pathway discovery does not rely on any assumption of pathway conservation. This approach can detect variants, super-pathways, and “cross-map” paths. It can be applied to organisms whose metabolism is unknown, but for which enzyme-coding genes have been identified in the genome, and some information is available about their functional grouping (co- expression, operons, gene fusion, ....). Materials and Methods To extract a pathway, we connect query compounds or reactions in the metabolic network using various algorithms: the random walk based kWalks algorithm, three approaches based on k-shortest path finding and combinations of kWalks with the latter. When predicting pathways from genes, we have to link enzyme-coding genes to reactions. In case of broad-specificity enzymes, this yields a large number of reactions, only a few of which are relevant in the pathway. To address this problem, reactions catalyzed by the same EC number are merged into equivalence groups. Results We evaluated the pathway extraction algorithms on 71 MetaCyc pathways and found that a combination of kWalks with a shortest-paths based approach yields the highest accuracy (77%). The pathway extraction tool has been integrated in the Network Analysis Tools (NeAT, http://rsat.ulb.ac.be/neat/), and can be accessed via a web interface, as stand-alone application or as SOAP/WSDL Web services. The seed nodes for subgraph extraction can be provided as reactions, (partial) compound names as well as EC numbers or genes. Pathways can be extracted from KEGG, MetaCyc or custom networks. Discussion We will present a selection of study cases illustrating the way to combine operon prediction, phylogenetic footprint discovery and pathway extraction in order to infer metabolic pathways from bacterial genomes. In future, this strategy will be systematically applied on bacterial genomes in the framework of the MICROME project (EU FP7)