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Prediction of trehalose-metabolic pathway and comparative analysis of KEGG, MetaCyc, and RAST databases based on complete genome of Variovorax sp. PAMC28711.

Authors :
Shrestha, Prasansah
Kim, Min-Su
Elbasani, Ermal
Kim, Jeong-Dong
Oh, Tae-Jin
Source :
BMC Genomic Data. 1/6/2022, Vol. 23 Issue 1, p1-7. 7p.
Publication Year :
2022

Abstract

Background: Metabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy. Many metabolic pathways are undisclosed and many organism-specific enzymes involved in metabolism are misplaced. When predicting a specific metabolic pathway of a microorganism, the first and foremost steps is to explore available online databases. Among many online databases, KEGG and MetaCyc pathway databases were used to deduce trehalose metabolic network for bacteria Variovorax sp. PAMC28711. Trehalose, a disaccharide, is used by the microorganism as an alternative carbon source. Results: While using KEGG and MetaCyc databases, we found that the KEGG pathway database had one missing enzyme (maltooligosyl-trehalose synthase, EC 5.4.99.15). The MetaCyc pathway database also had some enzymes. However, when we used RAST to annotate the entire genome of Variovorax sp. PAMC28711, we found that all enzymes that were missing in KEGG and MetaCyc databases were involved in the trehalose metabolic pathway. Conclusions: Findings of this study shed light on bioinformatics tools and raise awareness among researchers about the importance of conducting detailed investigation before proceeding with any further work. While such comparison for databases such as KEGG and MetaCyc has been done before, it has never been done with a specific microbial pathway. Such studies are useful for future improvement of bioinformatics tools to reduce limitations. [ABSTRACT FROM AUTHOR]

Details

Language :
English
Volume :
23
Issue :
1
Database :
Academic Search Index
Journal :
BMC Genomic Data
Publication Type :
Academic Journal
Accession number :
154535638
Full Text :
https://doi.org/10.1186/s12863-021-01020-y