1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Comparison of the Salmonella enterica subsp. LT2 metabolic model in biochemical databases
Paper ID : 1233-ICB10
Authors:
Sajjad Gharaghani *1, Shiva Beigizadeh2
1عضو هیات علمی دانشگاه علوم تهران بخش بیوانفورماتیک
2عضوهیات علمی دانشگاه علوم پزشکی جهرم
Abstract:
Metabolic models include the description of all biochemical reactions, metabolites, and metabolism genes for a particular organism, biochemical, genetic, and genomic knowledge [1]. We analyzed five biochemical databases (Bigg, KEGG, SEED, PATRIC, and BIOCYC) commonly used for metabolic modeling and performed pairwise comparisons to examine compatibility, incompatibility, and ambiguity of reactions, metabolites, and genes between databases. Salmonella enterica subsp. LT2 that there's a metabolic model for it in the Bigg database. (ID BIGG: STM_v1_0) The SEED model is also the primary source of genomic-scale metabolic models based on microbial or plant annotation genomes. We created the metabolic model of our reference genome using the Model SEED web server and reconstructed it in the PATRIC metabolic database. (PATRIC Database ID: 99287.1) We compared the Bigg and Seed metabolic models for the number of reactions, metabolites, and genes. The Seed model had 1646 reactions, 1726 metabolites, and 958 genes, and the Bigg model had 2545 reactions, 1802 metabolites, and 1271 genes. There were 1935 reactions and 1547 metabolites in the Seed model that were not presented in the Bigg model, and conversely, there were 1073 reactions and 951 metabolites in the Bigg model that were not presented in the Seed model. In the SEED database, our organism had 566 subsystems and in the PATRIC database, it had 344 subsystems. To find functional proteins that are in the metabolic pathway but are not in the PATRIC database, we searched for organisms in the KEGG database.
Conclusion: Each database uses its own naming space; when different tools are used, the same metabolites and reactions may have different naming conventions. It is now recommended to use unique identifiers, independent of the specific databases used or referred to different namespaces. Also, manual verification of maps is a useful solution to eliminate inconsistencies while combining the models.
Keywords:
databases, incompatibility, name ambiguity, chemical nomenclature
Status : Paper Accepted (Poster Presentation)