BACKGROUND: An important step in the reconstruction of a metabolic network is annotation of metabolites. Metabolites are generally annotated with various database or structure based identifiers. Metabolite annotations in metabolic reconstructions may be incorrect or incomplete and thus need to be updated prior to their use. Genome-scale metabolic reconstructions generally include hundreds of metabolites. Manually updating annotations is therefore highly laborious. This prompted us to look for open-source software applications that could facilitate automatic updating of annotations by mapping between available metabolite identifiers. We identified three applications developed for the metabolomics and chemical informatics communities as potential solutions. The applications were MetMask, the Chemical Translation System, and UniChem. The first implements a "metabolite masking" strategy for mapping between identifiers whereas the latter two implement different versions of an InChI based strategy. Here we evaluated the suitability of these applications for the task of mapping between metabolite identifiers in genome-scale metabolic reconstructions. We applied the best suited application to updating identifiers in Recon 2, the latest reconstruction of human metabolism. RESULTS: All three applications enabled partially automatic updating of metabolite identifiers, but significant manual effort was still required to fully update identifiers. We were able to reduce this manual effort by searching for new identifiers using multiple types of information about metabolites. When multiple types of information were combined, the Chemical Translation System enabled us to update over 3,500 metabolite identifiers in Recon 2. All but approximately 200 identifiers were updated automatically. CONCLUSIONS: We found that an InChI based application such as the Chemical Translation System was better suited to the task of mapping between metabolite identifiers in genome-scale metabolic reconstructions. We identified several features, however, that could be added to such an application in order to tailor it to this task.
BACKGROUND: An important step in the reconstruction of a metabolic network is annotation of metabolites. Metabolites are generally annotated with various database or structure based identifiers. Metabolite annotations in metabolic reconstructions may be incorrect or incomplete and thus need to be updated prior to their use. Genome-scale metabolic reconstructions generally include hundreds of metabolites. Manually updating annotations is therefore highly laborious. This prompted us to look for open-source software applications that could facilitate automatic updating of annotations by mapping between available metabolite identifiers. We identified three applications developed for the metabolomics and chemical informatics communities as potential solutions. The applications were MetMask, the Chemical Translation System, and UniChem. The first implements a "metabolite masking" strategy for mapping between identifiers whereas the latter two implement different versions of an InChI based strategy. Here we evaluated the suitability of these applications for the task of mapping between metabolite identifiers in genome-scale metabolic reconstructions. We applied the best suited application to updating identifiers in Recon 2, the latest reconstruction of human metabolism. RESULTS: All three applications enabled partially automatic updating of metabolite identifiers, but significant manual effort was still required to fully update identifiers. We were able to reduce this manual effort by searching for new identifiers using multiple types of information about metabolites. When multiple types of information were combined, the Chemical Translation System enabled us to update over 3,500 metabolite identifiers in Recon 2. All but approximately 200 identifiers were updated automatically. CONCLUSIONS: We found that an InChI based application such as the Chemical Translation System was better suited to the task of mapping between metabolite identifiers in genome-scale metabolic reconstructions. We identified several features, however, that could be added to such an application in order to tailor it to this task.
Authors: Manish Sud; Eoin Fahy; Dawn Cotter; Alex Brown; Edward A Dennis; Christopher K Glass; Alfred H Merrill; Robert C Murphy; Christian R H Raetz; David W Russell; Shankar Subramaniam Journal: Nucleic Acids Res Date: 2006-11-10 Impact factor: 16.971
Authors: David S Wishart; Dan Tzur; Craig Knox; Roman Eisner; An Chi Guo; Nelson Young; Dean Cheng; Kevin Jewell; David Arndt; Summit Sawhney; Chris Fung; Lisa Nikolai; Mike Lewis; Marie-Aude Coutouly; Ian Forsythe; Peter Tang; Savita Shrivastava; Kevin Jeroncic; Paul Stothard; Godwin Amegbey; David Block; David D Hau; James Wagner; Jessica Miniaci; Melisa Clements; Mulu Gebremedhin; Natalie Guo; Ying Zhang; Gavin E Duggan; Glen D Macinnis; Alim M Weljie; Reza Dowlatabadi; Fiona Bamforth; Derrick Clive; Russ Greiner; Liang Li; Tom Marrie; Brian D Sykes; Hans J Vogel; Lori Querengesser Journal: Nucleic Acids Res Date: 2007-01 Impact factor: 16.971
Authors: Jon Chambers; Mark Davies; Anna Gaulton; Anne Hersey; Sameer Velankar; Robert Petryszak; Janna Hastings; Louisa Bellis; Shaun McGlinchey; John P Overington Journal: J Cheminform Date: 2013-01-14 Impact factor: 5.514
Authors: José Manuel Otero; Donatella Cimini; Kiran R Patil; Simon G Poulsen; Lisbeth Olsson; Jens Nielsen Journal: PLoS One Date: 2013-01-21 Impact factor: 3.240
Authors: German A Preciat Gonzalez; Lemmer R P El Assal; Alberto Noronha; Ines Thiele; Hulda S Haraldsdóttir; Ronan M T Fleming Journal: J Cheminform Date: 2017-06-14 Impact factor: 5.514
Authors: Clément Frainay; Emma L Schymanski; Steffen Neumann; Benjamin Merlet; Reza M Salek; Fabien Jourdan; Oscar Yanes Journal: Metabolites Date: 2018-09-15