The outcomes of pathway database computations depend on pathway ontology

Area of Science:

• Systems biology
• Bioinformatics
• Metabolic pathway analysis

Background:

• Biological pathway databases utilize diverse pathway ontologies.
• Pathway ontologies significantly influence computational analyses.
• Varied ontologies yield different results for computational users.

Purpose of the Study:

• To compare and contrast pathway ontologies used in BioCyc and KEGG databases.
• To evaluate the impact of different pathway conceptualizations on biological interpretation.

Main Methods:

• Comparative analysis of BioCyc and KEGG pathway definitions.
• Utilized genome context methods to assess functional relatedness of gene pairs within pathways.
• Statistical comparison of gene pair relatedness between BioCyc and KEGG pathways.

Main Results:

• BioCyc pathways are defined as conserved, atomic metabolic modules within single organisms.
• KEGG pathways are larger, averaging 4.2 times the size of BioCyc pathways, and integrate multiple biological processes across organisms.
• Gene pairs within BioCyc pathways exhibit higher functional relatedness compared to gene pairs within KEGG pathways.

Conclusions:

• The BioCyc pathway ontology better reflects a single conserved biological process.
• KEGG pathways represent a mosaic of reactions centered on substrates, encompassing broader biological scope.
• The choice of pathway ontology is critical for accurate biological interpretation and computational modeling.