Guide to MetaCyc


    1  Introduction

    2  MetaCyc Overview
        2.1  Motivations
        2.2  Database Contents
        2.3  Query and Visualization
        2.4  The MetaCyc Data Universe
        2.5  Linking to MetaCyc
        2.6  Development
        2.7  Underlying Software

    3  MetaCyc Availability

    4  MetaCyc Curation
        4.1  Information Types Captured During the MetaCyc Curation Process
            4.1.1  Pathways
            4.1.2  Reactions
            4.1.3  Enzymes and Enzyme Complexes
            4.1.4  Genes
            4.1.5  Compounds

    5  Taxonomic Designations for Pathways

    6  Release Process, Frequency, and History
        6.1  MetaCyc Release Procedures

    7  BLASTing Against MetaCyc

    8  Database Links

    9  Data Sources

    10  Comparison of MetaCyc to other Pathway Databases
        10.1  KEGG
        10.2  EAWAG Biocatalysis/Biodegradation Database
        10.3  Reactome

    11  The MetaCyc Team
        11.1  Current Contributors
        11.2  Past Contributors

    12  Submitting Pathways for Incorporation into MetaCyc
        12.1  How to Ensure that You and Your Organization Receive the Appropriate Credit
        12.2  How to Send Pathways to MetaCyc

    13  MetaCyc Publications
        13.1  Additional Publications

    14  How to Learn More

1  Introduction

This guide provides additional information on the MetaCyc database (DB) beyond that found in other MetaCyc publications [1234567891011], to help users of the database understand its contents in more depth.

MetaCyc is a member of the BioCyc collection of Pathway/Genome Databases. In contrast to all other members of that collection, which are organism-specific DBs, MetaCyc is a multiorganism DB. While the other BioCyc databases describe the metabolic network and genome of a single organism, and contain either solely computationally predicted pathways or a mix of experimentally determined and computationally predicted pathways (depending whether they are tier 3 or tier 2 DBs), MetaCyc contains only experimentally elucidated pathways.  MetaCyc aims to contain a representative example of as many experimentally-determined metabolic pathways as possible.

MetaCyc does not seek to model the complete metabolism of any particular organism, which is the role of individual BioCyc DBs. Instead, MetaCyc serves as a general reference on metabolic pathways and enzymes. MetaCyc is also used as a high-quality reference DB for predicting metabolic pathways in other organisms. Scientists use MetaCyc for a broad range of tasks, such as finding enzymes for metabolic engineering projects, learning about the possible metabolic fates of specific compounds, identifying metabolites based on mass spectometry data, or finding a comprehensive description for complex pathways. For questions that require information about the complete genome, proteome, or metabolic network of a particular organism, the users are advised to consult the appropriate organism-specific PGDB. For example, MetaCyc contains 21 pathways and 112 enzymes that have been experimentally studied in Staphylococcus aureus. In contrast, the BioCyc Staphylococcus aureus aureus database contains 201 pathways (most of which are computationally predicted), plus the entire genome and proteome of that strain.

2  MetaCyc Overview

MetaCyc is a database of non-redundant, experimentally elucidated metabolic pathways and enzymes. It also contains reactions, chemical compounds, and genes. It stores predominantly qualitative information rather than quantitative data, although it does contain some quantitative data such as enzyme kinetics data. “MetaCyc” is pronounced “met-a-sike”. It sounds like “encyclopedia”.

A unique property of MetaCyc is that it is curated[def] from the scientific experimental literature according to an extensive process  [4], such that:

  • As of May 2022, 3,350​ ​different organisms are referenced in MetaCyc.

  • As of May 2022, 3,006​ metabolic pathways and 17,780  enzymatic reactions have been curated, citing 71,322​ literature references.

  • MetaCyc stores the full complement of reactions that have been assigned EC numbers by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB).

  • MetaCyc stores thousands of additional enzyme-catalyzed reactions that have not yet been assigned an EC number.

2.1  Motivations

The MetaCyc mission is to serve a broad community of researchers from genetics, molecular biology, microbiology, biochemistry, genomics, metabolomics, bioinformatics, metabolic engineering, and systems biology in support of the following tasks:

Support computational metabolic reconstruction and modeling
One of MetaCyc’s primary applications is to serve as a reference database for computationally predicting the metabolic network of an organism from its annotated genome, such as by the PathoLogic component of Pathway Tools [example]. Those reconstructions can be converted to metabolic models using tools such as the MetaFlux component of Pathway Tools.

Provide an encyclopedic reference on pathways and enzymes
MetaCyc is used as a readily accessible source of up-to-date, literature-curated information on metabolic pathways and enzymes for basic research and genome analysis, as well as for educational purposes. [example]

Support metabolic engineering
Metabolic engineers use MetaCyc as an encyclopedia of metabolic pathways and enzymes that may be genetically engineered into an organism to alter its metabolism. [example]

Support metabolomics studies
MetaCyc's rich content of metabolites with full structure and monoisotopic mass data, makes it a useful resource for the identification of metabolites from mass spectromety data.

2.2  Database Contents

MetaCyc stores pathways involved in both [Primary metabolism] and [Secondary metabolism].

MetaCyc's metabolites, enzymes, and genes are not limited to those associated with these pathways, the database contains many enzymes and reactions not associated with pathways.

MetaCyc is extensively linked to other biological databases [8] including protein and nucleic-acid sequence databases and chemical compound databases.

MetaCyc provides extensive bibliographic data, including links to the original publications.

While MetaCyc itself does not contain protein or gene sequence data, protein sequences are easily retrievable for MetaCyc enzymes that have a link to UniProt. To retrieve this information, Click on the "Show Sequence at UniProt" command in the Operations menu.

2.3  Query and Visualization

MetaCyc data can be browsed and queried in multiple ways. For pathways, proteins, reactions and compounds, the MetaCyc site supports:

  • Text-based searching: simple text searches are possible by typing in the quick search box at the top of the page. you can hover the mouse inside the box to see a more detailed description [example].

  • Direct queries: extensive search capabilities are provided via user-friendly search interface accessible via the "Tools → Search X" command (when X is Genes/Proteins/RNAs, Compounds, Reactions, Pathways, or Growth Media) [example].

  • Advanced Search: an advanced search option that lets users build a complex query is availble via the "Tools → Advance Search" command.

  • Browsing using ontologies: this option is useful when one wants to search by proceeding from a general category to a specific instance. It is accessible via the "Tools → Browse X" command, when X is Pathway ontology, Enzyme commission ontology, or Compound ontology [example].

Extending information to other databases: every object in MetaCyc enables quick searching for it in a specific organism or a set of organisms via the Operations menu commands "Show this object in another database" and "Seach for this object in multiple databases". An more powerful search that can compare many aspects of the metaboilic network is available via the "Tools → Cross Organism Search" command.

2.4  The MetaCyc Data Universe

MetaCyc inter-relates information about pathways, reactions, compounds, proteins, and genes. Each object name is typically a hyperlink to the page describing that object. For example, while looking at a pathway page, clicking on a compound, reaction arrow, protein name or gene name will navigate to those object's pages, making it extremely easy for the user to navigate among the different database's objects.

Figure 1:  Click here for examples of enzymes, pathways, compounds, reactions, and genes.

2.5  Linking to MetaCyc

Users are encouraged to link their Web site or application to MetaCyc as described here.

2.6  Development

Since its beginning in 1998, MetaCyc’s data have been gathered from a variety of literature and on-line sources. MetaCyc is currently curated by a single full-time curator at SRI International. Some of the data in MetaCyc have been curated via collaborative projects between SRI International and the Marine Biological Laboratory, the Carnegie’s Department of Plant Biology, and the Boyce Thomson Institute for Plant Research [11].

2.7  Underlying Software

Curation and navigation of MetaCyc is performed using the Pathway Tools platform, which supports all of the BioCyc website. Pathway Tools can run on personal computers as well as servers and can be obtained here.

3  MetaCyc Availability

MetaCyc is completely free and is available in several different forms to facilitate different uses of the data:

  • The MetaCyc website permits online interactive querying and visualization.

  • The MetaCyc data can be downloaded as a set of data files (flat files). These files can be parsed and queried, or they can be loaded into other database systems. Click here for more information about the flat files, or you can download them from here.

  • The Pathway Tools software includes MetaCyc and can easily download and locally install any other BioCyc PGDB. It can be downloaded and installed on computers at your site, with the following advantages:

    • It provides some functionality that is not provided by the MetaCyc website.

    • It runs more quickly, as it does not depend on internet connection.

    • It supports programmatic querying of MetaCyc using APIs in the Java, Perl, and Lisp languages.

    • It supports running an equivalent of the BioCyc website on your intranet.

4  MetaCyc Curation

Curation is the process of manually refining and updating a bioinformatics database. The MetaCyc project uses a literature-based curation approach in which database contents are extracted in a step-wise manner from evidence in the experimental literature, as depicted below.

The curation procedures that MetaCyc curators follow are described in the Curator’s Guide to Pathway/Genome Databases.

MetaCyc data are derived from primary literature, review articless, patent applications, and from external compound databases.

For certain organisms, some of the data within MetaCyc have been directly imported from other databases which we consider to be the authoritative sources of data on those organisms:

4.1  Information Types Captured During the MetaCyc Curation Process

Note that not all objects contain all of the information types listed here; rather, this list describes the potential types of information for each object type.

4.1.1  Pathways

Pathways include a mini-review summary that usually includes:

  • Background information about the metabolite(s) being synthesized or degraded

  • Relationship to other pathways in case multiple variants exist

  • Background information about the research leading to establishing the pathway

  • Whether the pathway occurs natively or is engineered

  • The expected taxonomic distribution of the pathway

  • Citations to the literature from which the information was derived

  • Any additional relevant commentary (e.g. a particularly interesting enzyme that participates in the pathway)

Other collected data include:

  • Synonyms

  • Taxonomic range

  • Key reactions

4.1.2  Reactions

  • EC number(s)

  • Free enregy (mostly calculated from energy of formation of participating metabolites)

  • Directionality

  • Whether this is a hypothetical reaction

  • Whether the reaction occurs spontaneously

  • Whether the reaction is/can be balanced (some reactions, such as reactions describing extension of polymers, cannot be balanced)

  • Atom mapping (for balanced reactions only )

4.1.3  Enzymes and Enzyme Complexes

Most enzymes include a mini-review summary that covers:

  • Background information about the research conducted about the enzyme

  • Whether experimental evidence is available, and of what type

  • Whether the enzyme exists as a monmer or a complex

  • Substrate specificity

  • Tissue type and subcellular location

  • Citations to the literature from which the information was derived

Other collected data:

  • Common name and synonyms of the enzyme

  • Subcellular location

  • Molecular weight (KDa)

  • pI

  • Name(s) of cofactor(s), activator(s) and inhibitor(s)

  • Alternative substrates

  • Km, Vmax, Kcat, specific activity

  • Optimal pH and temperature

  • Directionality of the catalyzed activity

  • A link to an external database that contains sequence information, such as UniProt or the NCBI Protein database

4.1.4  Genes

  • Common name and synonyms of the gene

  • Accession numbers

  • A link to an external database that contains sequence information, such as Entrez Nucleotide or Gene

4.1.5  Compounds

  • Recommended name and synonyms of the compound

  • Compound structure

  • Ontology (when available)

  • Free energy of formation

  • A link to an external database such as ChEBI, KEGG, ChemSpider, PubChem etc.

Compound structures are obtained either from the primary literature or from public compound structure databases such as ChEBI and ChemSpider. The structures are edited using the Marvin software to provide a consistent look and to reflect the most prevalent protonation state at pH 7.3. For more information about protonation, see Reaction Balancing and Protonation State in BioCyc at the Guide to The BioCyc Database Collection.

We would like to express our gratitude to Chemaxon for granting us a free license to their Marvin software.

5  Taxonomic Designations for Pathways

MetaCyc pathways are labeled with the name of one or more taxons in which wet-lab experiments have indicated that the pathway is present. These taxonomic designations are present on the pathway page in a line labeled “Some taxa known to possess this pathway include,” and include species names, species and strain names, and occasionally names of higher taxa such as genus names, e.g., Pseudomonas. When a high-level taxon, such as a genus, is present as a pathway label, the interpretation is that experimental evidence suggests that the pathway is present in all members of that taxon.

The “number of organisms” row in the MetaCyc statistics indicates the total number of different organisms that are referenced in the database. These could be listed in the taxonomic designations of pathways, but could also be references by enzymes that do not participate in pathways, or simply be mentioned in pathway/enzyme summaries. There is wide variation in how many pathways a given taxon contributes to MetaCyc, with some taxa contributing only a single pathway, and other taxa contributing more than 100 pathways. The taxonomic distribution of MetaCyc pathways is summarized here: [Pathway Taxonomic Distribution] .

To query MetaCyc pathways by species:

  • Web version: Select MetaCyc, then Tools → Search → "Search Pathways", then expand "Search/Filter by organism".

  • Desktop version: Select MetaCyc, then Pathway → "Search by Organism".

MetaCyc pathway pages also specify an “Expected taxonomic range,” which lists the taxonomic groups in which this pathway is expected to occur, in contrast to the taxonomic groups in which the pathway has been proven to occur by a wet-lab experiment. This information is useful for pathway prediction.

6  Release Process, Frequency, and History

New versions of MetaCyc are released 3–4 times per year.

  • Two releases are “minor” releases, that is, data releases are made available on the Web site and in downloadable data files.

  • One or two releases are “major” releases, which include the updates available in minor releases, plus updates to the Pathway Tools software.

A detailed history of the enhancements to MetaCyc in each MetaCyc release is available here. This page also contains statistics on the changes in the content of MetaCyc over time.

6.1  MetaCyc Release Procedures

The MetaCyc staff perform the following operations as part of each MetaCyc release:

  • Import data from the latest version of the ExploreEnz DB into MetaCyc to ensure MetaCyc contains the latest Enzyme Commission (EC) information.

  • Import the latest version of the NCBI Taxonomy DB [12] into Pathway Tools.

  • Import the latest version of the GO database into Pathway Tools.

  • Update MetaCyc links to external DBs.

  • Compute InChI strings for MetaCyc metabolites using the InChI software [13].

  • Compute protonation states for MetaCyc metabolites using ChemAxon’s Major Microspecies plugin.

  • Compute Gibbs free energies for MetaCyc metabolite using an SRI software tool.

  • Compute reaction atom mappings using an SRI software tool [14].

  • Run many quality assurance programs including the Pathway Tools Consistency Checker; investigate and repair data glitches uncovered by these tools. Examples:

    • Check that reactions are balanced

    • Check for duplicate chemical compounds

    • Check chemical structures for invalid bonds and elements

    • Validate all GO terms as defined within the latest version of GO

    • Validate all internal database relationships as referring to valid database objects

    • Ensure that slot values meet range and cardinality constraints (e.g., values of the pI slot must be between 0–14).

    • Update DB and software documentation and web pages.

7  BLASTing Against MetaCyc

A common early step in performing pathway analysis of genomes and metagenomes is to associate protein sequences to MetaCyc reactions. The Pathway Tools software infers such associations by using EC numbers, enzyme names, and Gene Ontology terms within protein annotations. Such annotations might be inferred using a variety of sequence-analysis methods.

To aid researchers in associating sequences to MetaCyc reactions, MetaCyc enzymes that have a link to UniProt contain protein sequence information. It is possible to perform BLAST searches against MetaCyc proteins with sequence information using the "BLAST Search" command under the Search menu. In addition, each release of MetaCyc includes a file that associates MetaCyc reaction IDs with the UniProt identifiers of enzymes known to catalyze those reactions. Note that not all MetaCyc reactions have EC numbers (because many enzymes have not yet been assigned EC numbers), therefore EC numbers are not a comprehensive mechanism for associating sequences to reactions. The file is called uniprot‑seq‑ids.dat and is included in the MetaCyc data file distribution.

8  Database Links

MetaCyc contains links to many other bioinformatics DBs. Some MetaCyc links are “unification links”, meaning they are links from an object in MetaCyc to an object in another DB that represents the same biological object. Other links are “relationship links”, meaning they are links from an object in MetaCyc to an object in another DB that represents a related object, such as a link from a MetaCyc reaction to a PIR protein that catalyzes that reaction. Note that not all objects contain links to all of the databases listed here; rather, this list describes the potential links for each object type.

The following types of MetaCyc objects contain links to the following databases.

9  Data Sources

MetaCyc incorporates information that was obtained from the following sources:

The primary literature
Most of the data in MetaCyc is derived from the primary literature. Every pathway and most enzymes are based on articles published in peer-reviewed magazines. Many literature citations are obtained via PubMed.

The EC list
The reactions in MetaCyc that have EC numbers were originally defined by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB), as seen in the ExplorEnz database. Many of the “EC reactions” were imported into MetaCyc via the ENZYME database, an enzyme nomenclature database based on the recommendations of the NC-IUBMB, with the kind permission of the Swiss Institute of Bioinformatics.

Subsequent to their import into MetaCyc, we have modified many of the EC reactions from the original IUBMB format. One main reason for the modifications is the fact that in MetaCyc the compounds that participate in the reactions are actually database objects, rather than strings of text. The ExplorEnz and ENZYME databases suffer from some inconsistency so that the same compound may be referred to by different names in different reactions. In MetaCyc reactions the compound is represented by a reference to an underlying database entry for the compound, and thus will always appear with the same common name. A second reason for modification is our attempt to provide a consistent format for the representation of reactions in the form of “reactants = products”, which is not always used by the IUBMB. For example, reaction EC, ADAM 17 endopeptidase, is defined by the IUBMB by the free text: “Narrow endopeptidase specificity. Cleaves Pro-Leu-Ala-Gln-Ala-|-Val-Arg-Ser-Ser-Ser in the membrane-bound, 26-kDa form of tumor necrosis factor alpha (TNF-alpha). Similarly cleaves other membrane-anchored, cell-surface proteins to ’shed’ the extracellular domains”. In MetaCyc the reaction is presented simply as “a TNF-α precursor + H2O → an active TNF-α + a peptide”, and the rest of the information is provided within the reaction comment. A third reason for the differences results from the fact that all MetaCyc compounds are protonated to a consistent pH (the cytosolic pH of 7.3), and all reactions are mass balanced for hydrogen and charge by addition of protons where applicable (most NC-IUBMB reactions do not consider protons).

UniProt protein database
Most of the polypeptides in MetaCyc are linked to UniProt. UniProt data is often used as the source for the enzyme’s molecular weight (calculated from sequence), for links to DNA sequence databases, and for synonyms of the enzyme and its gene. It is also used to help determine the taxonomic distribution of enzymes.

NCBI Taxonomy, Nucleotide and Gene databases
The NCBI taxonomy database is integrated into MetaCyc, and all internal references to organisms that are included in that taxonomy are based on the NCBI taxonomy IDs. MetaCyc gene entries are most commonly linked to the NCBI Nucleotide and Gene databases.

ChEBI, KEGG Ligand and PubChem chemical structure databases
While most of the chemical structures in MetaCyc were obtained from the original literature, many were obtained from other databases. MetaCyc entries contain DB links to many other compound databases, including ChEBI, KEGG and PubChem.

The BRENDA enzyme database
When predicting the taxonomic range of pathways, MetaCyc curators occasionaly use data from BRENDA to estimate the taxonomical range of individual enzymes.

10  Comparison of MetaCyc to other Pathway Databases

10.1  KEGG

A detailed comparison of KEGG and MetaCyc has been published in [15].

KEGG contains two types of pathways: maps and modules.

  • KEGG maps are quite large and typically integrate reactions and pathways found in multiple species – they are therefore chimeric. KEGG maps are not found in their entirety in any one species. MetaCyc superpathways are similar to KEGG maps in that they comprise multiple sub-pathways, though unlike KEGG maps most MetaCyc superpathways do occur in a single organism.

  • KEGG modules are smaller than maps, and are similar in size to individual MetaCyc pathways (though KEGG’s collection of modules is very incomplete because they are a relatively new development in KEGG).

MetaCyc pathways (and KEGG modules) are closer to true biological pathways than are KEGG maps, because they attempt to model individual biological pathways from individual organisms. KEGG maps are typically 3–4 times larger than are KEGG modules and MetaCyc pathways because of their chimeric nature. For example, KEGG map MAP00270, “cysteine and methionine metabolism”, combines pathways for the biosynthesis of L-methionine, L-cysteine, L-homocysteine, L-homoserine, ethylene, and methanethiol, for degradation of L-serine, L-cysteine, L-methionine, sulfolactate, S-methyl-5’-thioadenosine, and S-methyl-5-thio-alpha-D-ribose 1-phosphate; for homocysteine and cysteine interconversion; and for methionine salvage.

While KEGG maps (and MetaCyc superpathways) are useful in showing how individual pathways connect and in presenting the larger biochemical context in which a pathway operates, they are not suitable for many types of analyses.  For example, using KEGG maps, a program for predicting the metabolic pathways of an organism could not predict methionine biosynthesis independently of ethylene biosynthesis, though many organisms do not produce the latter, because those two separate processes are fused into one KEGG map. Similarly, if a program for enrichment analysis of transcriptomics data detects enrichment of MAP00270, we would not know which actual pathway was in fact enriched. For that matter, MAP00270 could receive a high enrichment score because of differential expression of genes within multiple pathways within that map when in fact no individual pathway was highly enriched. And because maps are so large, a large number of genes must be differentially expressed for a map to obtain a high enrichment score.

The smaller pathways in MetaCyc (and KEGG modules) are advantageous for several reasons - they correspond to a single biological function, the enzymes participating in them are usually regulated as a unit, and they tend to be conserved through evolution.  That said, MetaCyc pathway diagrams have several advantages over KEGG modules, as they include full chemical compound  names and enzyme names (KEGG module diagrams contain only unintelligible identifiers) and they can show the full chemical structures for substrates (the user can select the level of detail shown by choosing to turn on or off the display of structures and/or enzyme names).

MetaCyc records separately the different pathway variants that have been observed in different organisms. For example, MetaCyc contains six different pathway variants for synthesizing L-lysine. KEGG does not identify pathway variants. Within the large maps defined by KEGG, it is impossible for the user to tell which subnetworks correspond to distinct biological units, nor in which species these units have been elucidated experimentally.

MetaCyc curators author extensive mini-review summaries that describe individual pathways and enzymes. KEGG contains short summaries for approximately half of its pathway maps.

MetaCyc pathways are labeled with the name(s) of some of the species in which the presence of those pathways has been experimentally determined, whereas such information is irrelevant for KEGG maps since they are chimeric. Pathways in MetaCyc and in other BioCyc PGDBs contain evidence codes that indicate whether experimental or computational evidence supports the presence of the pathway in that organism; KEGG does not use evidence codes.

MetaCyc proteins contain enzyme properties such as subunit composition, substrate specificity, cofactor requirements, activators, and inhibitors. KEGG has only cofactor data. However, because those data are associated with KEGG reactions rather than with KEGG enzymes, it is difficult to be sure for which proteins from which species the cofactor requirement was experimentally elucidated.

And finally, MetaCyc compounds include calculated Gibbs free energies of formation, SMILES, and InChIs, which are missing from KEGG compounds.

MetaCyc version 26.0 (April 2022) contained 3,006 pathways, compared to the 457 metabolic modules in KEGG version 102.0+ (April 2022). MetaCyc version 26.0 contained 382 superpathways, compared to the 551 maps found in KEGG. MetaCyc version 26.0 contained 17,780 reactions, compared to 11,775 in KEGG version 102.0+. MetaCyc version 26.0 cited 71,322 articles from which its contents were derived and contained 10,944 textbook-equivalent pages of mini-review summaries for enzymes and pathways; KEGG contains small numbers of citations and of mini-reviews.

BioCyc Organism-Specific PGDBs Compared to KEGG Species Views of Pathway Maps

KEGG version 102.0+ contained 8,362 organisms whereas BioCyc version 26.0 contained 20,005 organism databases.

Sixty nine of the BioCyc databases are designated as Tier 1 or Tier 2, meaning they have undergone some amount of manual curation. In some cases, databases received several person-years (e.g., for Arabidopsis thaliana) or even person-decades (e.g., EcoCyc and MetaCyc) of curation. In contrast, KEGG curates only its reference pathway maps and modules; it does not curate organism-specific views of those data, which are generated computationally. Manually curated databases have significant advantages, including higher accuracy and richer information. Curators remove incorrectly predicted pathways and add pathways that should have been predicted. Curators also add additional information from the literature that often results in modifying the original annotattion, and add content such as mini-review summaries, evidence codes, literature citations, and enzyme properties.

Pathway Tools Software Compared to KEGG Software

The Pathway Tools software that underlies MetaCyc and BioCyc is more advanced than the KEGG software in many respects. Pathway Tools can be installed locally at your site, and many of its operations are available via the BioCyc website.

  • Inference tools: Pathway Tools predicts the following information for an annotated genome: the reactome of the organism, the metabolic pathways of the organism, pathway hole fillers (genes predicted to encode enzymes that catalyze pathway reactions with no attached enzymes), and operons. KEGG offers the first two functions, but lacks the latter two.

  • Metabolic flux models: The Pathway Tools MetaFlux component supports creation of quantitative metabolic flux models from Pathway/Genome Databases; KEGG does not offer a similar tool.

  • Query tools: Pathway Tools has an extensive suite of query tools under its Web Search menu; KEGG lacks most of these tools.

  • Omics data analysis: Pathway Tools has many omics data analysis tools including:

    • Painting omics data onto individual pathway diagrams (present in KEGG)

    • Painting omics data onto multi-pathway diagrams called pathway collages (not present in KEGG)

    • Painting omics data onto a metabolic network diagram with animation of multiple time points (present in KEGG without the animation)

    • The Omics Dashboard - an innovative tool for analyzing omics data (not present in KEGG)

  • Comparative analysis tools: Pathway Tools has a number of comparative analysis tools not present in KEGG

  • Metabolic route search: Pathway Tools can search for routes through a metabolic network connecting a specified starting compound to a specified ending compound (not present in KEGG)

10.2  EAWAG Biocatalysis/Biodegradation Database

The Biocatalysis/Biodegradation Database was developed by the University of Minnesota and used to be known as UM-BBD. The database contains information on microbial biocatalytic reactions and biodegradation pathways for chemicals largely considered to be potential environmental pollutants.

The data is now hosted at the Swiss Federal Institute of Aquatic Science and Technology and the database is known as the EAWAG Biocatalysis/Biodegradation Database. The database does not appear to be updated - in April 2022 the database contained 219 pathways and 993 enzymes from 543 microorganisms, the same as it did in 2018. The pathways have been curated from the biomedical literature and some contain significant comments and literature citations.

10.3  Reactome

Reactome is a curated database of biological processes in humans and a few other organisms. It covers biological pathways ranging from the basic processes of metabolism to high-level processes such as hormonal signaling. Reactome information is curated form the literature, and includes significant comments and literature citations. Reactome contains far fewer metabolic pathways than does MetaCyc, and because most Reactome pathways are curated based on human biology, Reactome does not have the taxonomic breadth of MetaCyc, although Reactome pathways have been computationally projected to a number of model organisms.

11  The MetaCyc Team

This section summarizes the many past and present contributors to the MetaCyc project, listed alphabetically.

11.1  Current Contributors

SRI International

Roles: Curation of pathways, software development, Website operations

11.2  Past Contributors

  • Martha Arnaud, Ph.D., formerly at SRI International

  • Joseph M. Dale, formerly at SRI International

  • Kate Dreher, Ph.D., formerly at the Carnegie Institution for Science, Department of Plant Biology 

  • Hartmut Foerster, Ph.D., Boyce Thomson Institute for Plant Research

  • Carol Fulcher, Ph.D., formerly at SRI International

  • Fred Gilham, formerly at SRI International

  • John Ingraham, Ph.D., UC Davis (Emeritus)

  • A. Karthikeyan, Ph.D, formerly at the Carnegie Institution for Science, Department of Plant Biology

  • Cindy Krieger, Ph.D., formerly at SRI International

  • Mario Latendresse, Ph.D. — Computer Scientist, SRI International

  • Lukas Mueller, Ph.D., Boyce Thomson Institute for Plant Research

  • John Pick, formerly at SRI International

  • Liviu Popescu, formerly at SRI International

  • Anuradha Pujar, Ph.D, Boyce Thomson Institute for Plant Research

  • Sunita Reddy, formerly at SRI International

  • Seung Yon (Sue) Rhee, Ph.D., Carnegie Institution for Science, Department of Plant Biology

  • Monica Riley, Ph.D., Marine Biological Laboratory (deceased)

  • Alfred Spormann, Ph.D., Stanford University

  • Christophe Tissier, Ph.D., formerly at the Carnegie Institution for Science, Department of Plant Biology

  • Alfred Wang, formerly at Stanford University

  • Deepika Weerasinghe, Ph.D., formerly at SRI International

  • Peifen Zhang, Ph.D., formerly at the Carnegie Institution for Science, Department of Plant Biology

12  Submitting Pathways for Incorporation into MetaCyc

We are happy to incorporate pathways created by other scientists into the database.

If you are a Pathway Tools user and have created a pathway that fits our criteria, please send it to us. When we include externally submitted pathways in MetaCyc, we credit the contribution in the MetaCyc release notes, and if you wish, your name and your institution will appear on the pathway page.

By submitting pathways to MetaCyc you not only add to MetaCyc, but also increase the power of the PathoLogic metabolic-pathway prediction software. PathoLogic recognizes MetaCyc pathways in genome sequence data, and is now in use by hundreds of groups worldwide.

If you would like to submit a pathway for inclusion in a future release of MetaCyc, please make sure that you curate the pathway following these guidelines:

  • The pathway must be experimentally proven, and must be described in a published journal article

  • Each pathway and enzyme must have summaries and literature citations

  • Each pathway gene should have a link to a sequence database (e.g. Entrez); and a citation, if available

  • Enzymes should have as much information as possible, such as optimal pH and temperature, Km values, inhibitor, activator and cofactor information, etc.

  • Please include evidence codes for enzymes and pathways

  • Please make sure to use the Author Crediting feature (found within the Pathway Info Editor) to add the names of the curators and their institution affiliations . This will insure that the appropriate credit for your pathway is published on the BioCyc web page.

For examples of pathways that have been curated based on these guidelines, please see:

Further information can be found in the Curator’s Guide for Pathway/Genome Databases.

12.1  How to Ensure that You and Your Organization Receive the Appropriate Credit

Pathway Tools includes an author crediting system that can attach author and organization credentials to individual pathways. We recommend that prior to creating new objects in the PGDB you should create an Organization frame for your institute and an Author frame for yourself. This way, items that you create afterwards will be associated with these frames, providing you with the credit that you deserve. This credit information would be retained upon exporting the pathways and importing them into MetaCyc. It is also possible to add credit information to older pathways that were created prior to the creation of your author frame, through the Pathway Info Editor.

Detailed instructions on how to create organization and author frames are found in the Pathway Tools user manual, in the section ’Creating author frames’.

12.2  How to Send Pathways to MetaCyc

Pathways should be exported into a text file, which can be emailed to us at with the subjct "Pathway Submission". The procedure for exporting a pathways is:

  • Display the pathway in Pathway Tools; right click on its name and choose: Edit → Add object to file export list

  • Repeat until you have added all pathways to be submitted

  • From the file menu, choose: Export → Selected Objects to Lisp-Format file

  • Email us the resulting Lisp file

Please indicate if you would like your name and/or affiliation to appear on the pathway and enzyme pages.

13  MetaCyc Publications

If you use MetaCyc in your research, we ask that you cite the following publication:

[MetaCyc20] Caspi, R., Billington, Keseler, I.M., Kothari, A., Krummenacker, M., Midford, P.E., Ong, Q., Paley, S., Subhraveti, P. and Karp, P.D. (2020)
The MetaCyc database of metabolic pathways and enzymes - a 2019 update
Nucleic Acids Research 48(D1):D445-D453.

13.1  Additional Publications

[MetaCyc18] Caspi, R., Billington, Fulcher, C.A., Keseler, I.M., Kothari, A., Krummenacker, M., Latendresse, M., Midford, P.E., Ong, Q., Paley, S., Subhraveti, P. and Karp, P.D. (2018)
The MetaCyc Database of metabolic pathways and enzymes
Nucleic Acids Research 46(1):D633-D639.

[MetaCyc16] Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C.A., Keseler, I.M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L.A., Ong, Q., Paley, S., Subhraveti, P., Weaver, D.S. and Karp, P.D.(2016)
The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases
Nucleic Acids Research 44(1):D471-D480.

[MetaCyc14] Caspi, R., Altman, T., Billington, R., Dreher, K., Foerster, H, Fulcher, C.A., Holland, T.A., Keseler, I.M., Kothari, A., Kubo, A., Krummenacker, M., Latendresse, M., Mueller, L.A., Ong, Q., Paley, S., Subhraveti, P., Weaver, D.S., Weerasinghe, D., Zhang, P., and Karp, P.D.(2014)
The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases
Nucleic Acids Research 42(1):D459-D471.

[MetaCyc13] Altman, T., Travers, M., Kothari, A., Caspi, R. and Karp, P.D. A systematic comparison of the MetaCyc and KEGG pathway databases
BMC Bioinformatics 14:112 2013

[Curation13] Caspi, R., Dreher, K, and Karp, P.D. The challenge of constructing, classifying and representing metabolic pathways
FEMS Microbioly Letters 345:85 2013

[MetaCyc12] Caspi, R., Altman, T., Dreher, K., Fulcher, C.A., Subhraveti, P., Keseler, I.M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L.A., Ong, Q., Paley, S., Pujar, A., Shearer, A.G., Travers, M., Weerasinghe, D., Zhang, P., and Karp, P.D. (2012)
The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases
Nucleic Acids Research 40(1):D742-D753 2012.

[MetaCyc11] Karp, P.D., and Caspi, R., A survey of metabolic databases emphasizing the MetaCyc family
Archives of Toxicology 85:1015–33 2011.

[MetaCyc10] Caspi, R., Altman, T., Dale, J.M., Dreher, K., Fulcher, C.A., Gilham, F., Kaipa, P., Karthikeyan, A.S., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L.A., Paley, S., Popescu, L., Pujar, A., Shearer, A., Zhang, P. and Karp, P.D. (2010)
The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases
Nucleic Acids Research 38(1):D473-D479.

[MetaCyc08] Caspi, R., Foerster, H., Fulcher, C.A., Kaipa, P., Krummenacker, M., Latendresse, M., Paley, S., Rhee, S.Y., Shearer, A., Tissier, C., Walk, T.C., Zhang, P. and Karp, P.D. (2008)
The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases
Nucleic Acids Research 36(1):D623–D631.

[MetaCyc06] Caspi, R., Foerster, H., Fulcher, C.A., Hopkinson, R., Ingraham, J., Kaipa, P., Krummenacker, M., Paley, S., Pick, J., Rhee, S.Y., Tissier, C., Zhang, P. and Karp, P.D. (2006)
MetaCyc: A multiorganism database of metabolic pathways and enzymes
Nucleic Acids Research, 34:D511–D516.

[MetaCyc04] Krieger, C.J., Zhang, P., Mueller, L.A., Wang, A., Paley, S., Arnaud, M., Pick, J., Rhee, S.Y., and Karp, P.D. (2004)
MetaCyc: A Multiorganism Database of Metabolic Pathways and Enzymes,
Nucleic Acids Research 32(1):D438–42.

[MetaCyc03] Karp, P.D. (2003)
The MetaCyc Metabolic Pathway Database
In: Metabolic Engineering, B. Kholodenko and H. Westerhoff eds., Horizon Scientific Press.

[MetaCyc02] Karp, P.D., Riley, M., Paley, S. and Pellegrini-Toole, A. (2002)
The MetaCyc Database
Nucleic Acids Research, 30(1):59–61.

[MetaCyc00] Karp, P.D., Riley, M., Saier, M., Paulsen, I.T., Paley, S., and Pellegrini-Toole, A. (2000)
The EcoCyc and MetaCyc Databases
Nucleic Acids Research, 28(1):56–59.

See also the BioCyc Publications Page.

14  How to Learn More


[1]   R. Caspi, R. Billington, C. A. Fulcher, I. M. Keseler, A. Kothari, M. Krummenacker, M. Latendresse, P. E. Midford, Q. Ong, W. K. Ong, S. Paley, P. Subhraveti, and P. D. Karp. The MetaCyc database of metabolic pathways and enzymes. Nuc Acids Res, 46(D1):D633–9, 2018.

[2]   R. Caspi, R. Billington, L. Ferrer, H. Foerster, C. A. Fulcher, I. M. Keseler, A. Kothari, M. Krummenacker, M. Latendresse, L. A. Mueller, Q. Ong, S. Paley, P. Subhraveti, D. S. Weaver, and P. D. Karp. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nuc Acids Res, 44(D1):D471–80, 2016.

[3]   R. Caspi, T. Altman, R. Billington, K. Dreher, H. Foerster, C.A. Fulcher, T.A. Holland, I.M. Keseler, A. Kothari, A. Kubo, M. Krummenacker, M. Latendresse, L.A. Mueller, Q. Ong, S. Paley, P. Subhraveti, D.S. Weaver, D. Weerasinghe, P. Zhang, and P. D. Karp. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nuc Acids Res, 42:D459–71, 2014.

[4]   R. Caspi, T. Altman, K. Dreher, C.A. Fulcher, P. Subhraveti, I. Keseler, A. Kothari, M. Krummenacker, M. Latendresse, L.A. Mueller, Q. Ong, S. Paley, A. Pujar, A.G. Shearer, M. Travers, D. Weerasinghe, P. Zhang, and P. D. Karp. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nuc Acids Res, 40:D742–53, 2012.

[5]   P. D. Karp and R. Caspi. A survey of metabolic databases emphasizing the MetaCyc family. Arch of Toxicol., 85:1015–33, 2011.

[6]   R. Caspi, T. Altman, J.M. Dale, K. Dreher, C.A. Fulcher, F. Gilham, P. Kaipa, A.S. Karthikeyan, A. Kothari, M. Krummenacker, M. Latendresse, L.A. Mueller, S. Paley, L. Popescu, A. Pujar, A.G. Shearer, P. Zhang, and P. D. Karp. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nuc Acids Res, 38:D473–9, 2010.

[7]   R. Caspi, H. Foerster, C.A. Fulcher, P. Kaipa, M. Krummenacker, M. Latendresse, S. Paley, S. Y. Rhee, A. Shearer, C. Tissier, T.C. Walk, P. Zhang, and P. D. Karp. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nuc Acids Res, 36:D623–31, 2008.\_1/D623.

[8]   R. Caspi, H. Foerster, C.A. Fulcher, R. Hopkinson, J. Ingraham, P. Kaipa, M. Krummenacker, S. Paley, J. Pick, S. Y. Rhee, C. Tissier, P. Zhang, and P. D. Karp. MetaCyc: A multiorganism database of metabolic pathways and enzymes. Nuc Acids Res, 34:D511–6, 2006.\_1/D511.

[9]   C.J. Krieger, P. Zhang, L. A. Mueller, A. Wang, S. Paley, M. Arnaud, J. Pick, S. Y. Rhee, and P. D. Karp. MetaCyc: A multiorganism database of metabolic pathways and enzymes. Nuc Acids Res, 32:D438–42, 2004.

[10]   P. D. Karp. The MetaCyc metabolic pathway database. In Metabolic Engineering. Horizon Scientific Press, 2003.

[11]   P. D. Karp, M. Riley, S. Paley, and A. Pellegrini-Toole. The MetaCyc database. Nuc Acids Res, 30(1):59–61, 2002.

[12]   E. W. Sayers, T. Barrett, D. A. Benson, S. H. Bryant, K. Canese, V. Chetvernin, D. M. Church, M. DiCuccio, R. Edgar, S. Federhen, M. Feolo, L. Y. Geer, W. Helmberg, Y. Kapustin, D. Landsman, D. J. Lipman, T. L. Madden, D. R. Maglott, V. Miller, I. Mizrachi, J. Ostell, K. D. Pruitt, G. D. Schuler, E. Sequeira, S. T. Sherry, M. Shumway, K. Sirotkin, A. Souvorov, G. Starchenko, T. A. Tatusova, L. Wagner, E. Yaschenko, and J. Ye. Database resources of the national center for biotechnology information. Nuc Acids Res, 37(Database issue):D5–15, 2009.

[13]   S. E. Stein, S. R. Heller, and D. Tchekhovskoi. An open standard for chemical structure representation: The IUPAC chemical identifier. In Proc. 2003 International Chemical Information Conference (Nimes), pages 131–43, 2003.

[14]   M. Latendresse, J.P. Malerich, M. Travers, and P. D. Karp. Accurate atom-mapping computation for biochemical reactions. J Chem Inf Model, 2012.

[15]   T. Altman, M. Travers, A. Kothari, R. Caspi, and P. D. Karp. A systematic comparison of the MetaCyc and KEGG pathway databases. BMC Bioinformatics, 14:112, 2013.