title: The Path-A metabolic pathway prediction web server.
abstract: Pathway Analyst (Path-A) is a publicly available web server (http://path-a.cs.ualberta.ca) that predicts metabolic pathways. It takes a FASTA format file containing a set of query protein sequences from a single organism (a partial or complete proteome) and identifies those sequences that are likely to participate in any of its supported metabolic pathways (currently 10). Path-A uses a number of machine-learning and sequence analysis techniques (e.g. SVM, BLAST and HMM) to predict pathways. Each machine-learned classifier exploits similarity between sequences in the pathways of its model organisms and sequences in the query set. It predicts the pathways that are present in the query organism and annotates each predicted reaction and catalyst, using the appropriate sequences from the query set. Path-A also provides a browsable and searchable database of the pathways for the model organisms that are used to make its predictions. Path-A's predictor sets (using different classifier technologies) have been evaluated using standard cross-validation techniques on a dataset of 10 metabolic pathways across 13 model organisms--a total of 125 organism-specific pathways. The most accurate classifier technology obtained a mean precision of 78.3% and a mean recall of 92.6% in predicting all catalyst proteins, of all reactions, in all pathways present in the dataset. Although Path-A currently only supports metabolic pathways, the underlying prediction techniques are general enough for other types of pathways. Consequently, it is our intent to extend Path-A to predict other types of pathways, including signalling pathways.
authors.affiliation: Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8.
authors.forename: Luca
authors.initials: L
authors.lastname: Pireddu
authors.forename: Duane
authors.initials: D
authors.lastname: Szafron
authors.forename: Paul
authors.initials: P
authors.lastname: Lu
authors.forename: Russell
authors.initials: R
authors.lastname: Greiner
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D020407:Internet; D008660:Metabolism; D040901:Proteomics; D020539:Sequence Analysis, Protein; D012984:Software; D014584:User-Computer Interface