Compounds
Table 1 shows that both DBs contain significant numbers of compounds that are not substrates in any reaction, e.g., 8,249 of the compounds in KEGG do not directly participate in any reaction; 3,100 MetaCyc compounds do not directly participate in any reaction. MetaCyc includes such compounds for a variety of reasons: some such compounds are activators, inhibitors, and cofactors of MetaCyc enzymes; others are analogs of reaction substrates; others are expected to be present in reactions that will be curated in the future; still others are indirect substrates of MetaCyc reactions because they are instances of MetaCyc compound classes that are substrates of MetaCyc generic reactions. Although users might not expect pathway DBs to contain metabolites that are not participants in pathways or reactions, these metabolites may be useful for identification of compounds from metabolomics datasets.
KEGG contains more duplicate compound entries than does MetaCyc, but overall compound duplicates are relatively low in both DBs.
MetaCyc provides a richer set of compound data fields than KEGG does, including SMILES [43] and InChI [31] strings for most compounds (SMILES is also an ASCII system for encoding chemical structures). In addition, MetaCyc compounds are cross-referenced to the enzymes for which they are activators, inhibitors, and cofactors.
KEGG contains 2.0 times more compounds with comments than does MetaCyc, but the KEGG comments are extremely short, averaging 6.5 characters per comment. MetaCyc comments average 47.7 characters in length. Many KEGG comments are single phrases such as “pesticide”.
MetaCyc contains 2.4 names per compound compared to 1.6 for KEGG, which may render MetaCyc more able to recognize chemical names in chemical datasets that use non-standard nomenclature (e.g., metabolomics datasets). On the other hand, KEGG does contain significantly more compounds than MetaCyc.
Reactions
As noted earlier, many metabolites within the two DBs are not substrates of any reaction; similarly, many reactions within the two DBs are not components of any pathway. This situation occurs for a variety of reasons. Biologically, many metabolic reactions have not been assigned to a metabolic pathway. MetaCyc attempts to gather a comprehensive compendium of bioreactions for applications such as flux-balance analysis and design of novel metabolic pathways, that do not depend soley on reactions within defined metabolic pathways. In addition, some reactions in MetaCyc and KEGG will probably be assigned to pathways curated in the future.
Overall, MetaCyc contains 1.2 times as many reactions as does KEGG. Applications such as flux-balance analysis require reactions that are fully balanced (including hydrogen) because unbalanced reactions violate conservation of mass and thus the model can generate non-physical flux values. MetaCyc curators routinely encounter unbalanced reactions in the literature, and although many such unbalanced reactions can be corrected by curators, for some unbalanced reactions it is not clear how to correct them.
We can calculate for each DB the number of “high quality reactions” by subtracting from each total the duplicate reactions, and the unbalanced reactions. The results are MetaCyc: 9,451 and KEGG: 6,900, a ratio of 1.37:1.
MetaCyc also provides a richer set of attributes for reactions than does KEGG, such as identification of spontaneous reactions.
The atom mapping of a reaction describes for each reactant non-hydrogen atom its corresponding atom in a product compound. KEGG has provided atom-mapping data through its RPAIR attribute for several years; the Feb 2012 version of KEGG contains atom-mapping data for 8,292 reactions. MetaCyc began providing atom mapping data in version 16.5 in November 2012, which contains atom-mapping data for 8,281 reactions.
Although both DBs employ generic reactions, some details of the treatment of these reactions differ. Generic reactions are reactions in which one or more substrates denote a set of possible compounds, often by using R-groups. For example, the MetaCyc reaction DEOXYCYTIDINE‐KINASE‐RXN describes the reaction deoxycytidine + a nucleoside triphosphate →dCMP + a nucleoside diphosphate
KEGG contains the same reaction (R02321) with the same equation. However, KEGG represents the compound classes differently. In MetaCyc “a nucleoside triphosphate” is described by a class frame (Nucleoside‐Triphosphates). The MetaCyc ontology links that class frame to several subclasses, and ultimately to eleven specific compounds that are instances of that class, such as ATP. This representation allows software within Pathway Tools to generate instantiations of generic reactions — namely, to generate all possible instance reactions (reactions all of whose substrates are instance compounds, not classes) that are specializations of the generic reaction. MetaCyc contains 2,884 generic reactions, from which many additional reactions can be generated through instantiation. In contrast, although KEGG contains an object representing the generic compound (C00201), that generic compound is not found in the KEGG BRITE ontology, nor does KEGG contain links from the generic compound to instances of that compound. Thus, so far as we know, the KEGG representations do not facilitate programmatic instantiation of generic reactions.
Pathways
Based on Table 7, MetaCyc contains 10.3 times as many base pathways as KEGG contains modules. MetaCyc contains 1.2 times as many superpathways as KEGG contains maps. Because pathway size measured in reactions varies so strongly between the two DBs, comparing the DBs purely based on pathway counts can be misleading — the average MetaCyc base pathway contains 4.37 reactions, whereas the average KEGG map contains 28.84 reactions. Furthermore, 17% of MetaCyc pathways consist of a single reaction step — namely, in those cases where the MetaCyc curation rules on defining pathway boundaries [44, 45] result in single-reaction pathways.
A more meaningful way to compare the pathway complements of the two DBs is to compare the size of the metabolite and reaction spaces covered by these pathways. Table 1 shows that MetaCyc pathways refer to 5,523 distinct metabolites, or 1.16 times as many as KEGG. A small difference exists between the substrates covered by MetaCyc base pathways versus MetaCyc super pathways, most likely because MetaCyc super pathways are ultimately defined in terms of base pathways, plus some additional reactions not present in the base pathways. In contrast, there is a large difference between the substrates covered by KEGG maps versus modules — modules cover a very small set of substrates compared to maps and compared to MetaCyc pathways.
We posit that KEGG has such a small number of modules because modules were introduced to KEGG in the last few years, and their coverage is still limited. For example, KEGG contains one module for proline biosynthesis; MetaCyc contains four such pathways. KEGG lacks modules for biosynthesis of the amino acids valine, glycine, aspartate, alanine, glutamine, and glutamate (most but not all are one reaction pathways). That MetaCyc contains 10.3 times as many base pathways as KEGG contains modules means that studies such as [46] that analyze the pathway content of metagenomic samples may be incomplete because they may miss pathways using the limited repertoire of KEGG modules that could be found using MetaCyc base pathways (note that [46] also included KEGG maps in their analysis).
MetaCyc pathways refer to 6,348 reactions, or 1.03 times as many reactions as referred to in KEGG pathways. Thus, the reaction spaces covered by the two DBs are very similar in size.
MetaCyc provides a more extensive array of pathway attributes than does KEGG. Some of these attributes can be used to increase the accuracy of pathway prediction, e.g., Taxonomic-Range and Key-Reactions. Lacking those attributes, pathway predictions performed using KEGG pathways are likely to be less accurate than for MetaCyc pathways.
In the years before KEGG introduced its modules, KEGG and MetaCyc employed very different conceptualizations of pathways. As discussed in detail in [45], KEGG maps are larger than MetaCyc base pathways because KEGG maps are mosaics that integrate reactions from multiple organisms and multiple biological pathways. For example, KEGG map00270 (“cysteine and methionine metabolism”) integrates reactions from pathways involving the biosynthesis of both L-cysteine and L-methionine, and their conversion to compounds such as L-cystathionine and L-homocysteine, from all domains of life. In contrast, MetaCyc creates separate base pathways — called pathway variants — for each distinct pathway of L-methionine biosynthesis (eight pathways) and L-cysteine biosynthesis (four pathways) that has been experimentally elucidated in a given organism (pathways are considered distinct if they contain different sets of reactions). MetaCyc pathway boundaries are defined [45] based on evolutionary conservation, on the metabolism literature, on regulation, and on stable high-connectivity metabolites. We estimate that KEGG modules are created according to principles similar to those of MetaCyc base pathways.
These differences in pathway conceptualization have different implications, depending on the intended uses of pathway data. (1) MetaCyc pathways (and probably KEGG modules) more accurately portray the exact biological pathways that occur in a specific organism, because for a KEGG map, its mosaic nature means that the user cannot tell which subset of its reactions was experimentally elucidated in a particular organism. (2) KEGG maps (and MetaCyc superpathways) are more effective at portraying the set of possible reactions that can impinge on a given metabolite in a wide range of organisms. (3) KEGG maps are not effective for statistical correlation studies because they encompass so much metabolic ground. For example, if we compare two metagenomic datasets and find that map00270 (“cysteine and methionine metabolism”) is present in one but not the other, is it the biosynthesis of cysteine that is over represented, or that of methionine? Or is it the biosynthesis of other compounds in this map (such as L-cystathionine and L-homocysteine) that are over represented? Abubucker et al. make a similar point [46] about KEGG maps. (4) We argue that for pathway reconstruction in sequenced genomes, MetaCyc pathways are more effective because their smaller size produces more focused predictions. For example, KEGG shows its map00680 (“methane metabolism”) as present in E. coli K-12 MG1655 with 23 reactions (excluding transporters) colored as occurring in this organism. Yet, E. coli K-12 MG1655 does not produce methane. A counter-example of KEGG pathway prediction comes from the photosynthesis map (map00195), for which only annotations based on photosynthetic organisms can be selected on the KEGG website. Thus, it is unclear what rules KEGG uses to call a given map as present or absent in a given organism; the rules used by Pathway Tools are published [47].
When a map is called as present by KEGG, does it predict all reactions in the map as present in that organism? For example, for the methane metabolism pathway in E. coli, are the additional 55 uncolored reactions inferred as present in textitE. coli? Since KEGG pathways are known to be multi-organism mosaics, such an inference will surely contain many false-positive reactions. In contrast, when a MetaCyc pathway is predicted as present, the assertion is that all of its reactions are probably present, permitting a more focused and accurate prediction of the reactome of an organism. This more accurate prediction of the reactome has implications for metabolic modeling using flux-balance analysis, where missing reactions usually yield non-solvable models, whereas extra reactions can yield models that make erroneous predictions. KEGG may resolve these issues once its collection of modules is more extensive, but currently its modules cover too little of metabolism to have broad utility.
Figure 1 and Figure 2 reveal that while MetaCyc base pathways have a distribution range comparable to that of KEGG modules, there is a significant difference in mean and variance for MetaCyc super pathways and KEGG maps. Many pathway analyses, such as enrichment/depletion, may exhibit bias when the sets of pathways have a large range of sizes. By virtue of having a smaller range of sizes, MetaCyc super pathways provide a more consistent basis for performing pathway analyses.
We analyzed the degree of overlap on a pathway-class basis in Tables 11 and 12, revealing the pathway classes that are enriched for reaction links (i.e., there is a significant amount of overlap between the two databases), and the pathway classes that are depleted for reaction links (i.e., the pathway class is relatively unique to its database). The KEGG pathway class depletion in Table 12 shows that the metabolism of MetaCyc is under-represented for counterparts of the KEGG maps for xenobiotics, glycans, and polyketides. For glycans and polyketides, we expect that this is because MetaCyc does not currently have the ability to represent abstracted versions of glycan chemical structures, nor abstracted versions of polyketide pathways, found in KEGG map drawings.
Table 13 shows that MetaCyc contains large numbers of unique pathways, which are primarily found in plant taxa, but are also found in vertebrates, chordata, and metazoa; in fungi; in archaea; and in proteobacteria.
Miscellaneous
MetaCyc contains extensive data on metabolic enzymes. Version 16.0 of MetaCyc contains 7,893 metabolic enzymes. MetaCyc describes enzyme subunit composition, substrate specificity, activators, inhibitors, and cofactor requirements. KEGG does not describe the protein properties of metabolic enzymes, and therefore lacks this type of data; KEGG does associate cofactors with reactions.
MetaCyc and KEGG also differ in their licensing terms. MetaCyc data are freely available to all users via data file download in multiple formats, and may be openly redistributed. KEGG dataset FTP downloads are available for a fee to all users, and may not be openly redistributed. KEGG provides a web service API for requesting entries individually, as does MetaCyc.