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Guide to MetaCyc

Contents

    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. The other BioCyc databases describe the metabolic network and genome of a single organism, and mix experimentally determined pathways with computationally predicted pathways. MetaCyc contains experimentally elucidated pathways only; one goal of the MetaCyc project is for MetaCyc to contain a representative example of every experimentally determined metabolic pathway.

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 broad reference on metabolic pathways and enzymes. For example, MetaCyc is a high-quality reference DB for predicting metabolic pathways in other organisms. Scientists typically use MetaCyc to answer metabolic questions that span multiple domains of life, such as “what are all the pathways for arginine degradation in microbes,” or “what cofactor biosynthesis pathways are known in bacteria?” For questions that require information about the complete genome, proteome, or metabolic network of an organism, instead consult the organism-specific PGDB. For example, MetaCyc contains 14 pathways that have been experimentally studied in Staphylococcus aureus, and 36 enzymes that participate in these pathways. In contrast, the BioCyc Staphylococcus aureus RF122 PGDB contains 189 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:

  • 2,740​ ​ different organisms are represented, with the majority of pathways occuring in microorganisms and plants

  • 2,411​ ​ metabolic pathways are stored, with 13,074​ ​ enzymatic reactions and 47,838​ ​ associated literature citations

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

  • MetaCyc also 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, 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 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 by researchers for basic research and genome analysis, and by students and teachers 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]

2.2  Database Contents

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

MetaCyc also stores metabolites, enzymes, enzyme complexes, and genes associated with these pathways.

MetaCyc is extensively linked to other biological databases [8] containing protein and nucleic-acid sequence data, bibliographic data and protein structures.

Unlike EcoCyc, MetaCyc provides little genomic data. MetaCyc does contain objects for the genes that encode most of the enzymes within the DB, and contain links to external sequence databases. MetaCyc enzymes that have a link to UniProt contain protein sequence information. 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 several different ways. For pathways, proteins, reactions and compounds, the MetaCyc site supports:

Comparison features combine MetaCyc with other BioCyc databases to provide additional ways for viewing data. Examples for Cross-Species comparisons include:

  • Comparing specific pathways between two or more organisms [example]

  • Comparing the genomic maps of two or more organisms [example]

Additionally, a desktop version of the software provides substantially more powerful capabilities. When installed locally with multiple organism-specific databases, the desktop version enables several powerful capabilities, such as:

  • Comparing the overall metabolic networks of different organisms [example]

The desktop version can be downloaded here.

2.4  The MetaCyc Data Universe

MetaCyc inter-relates pathway information (including reactions and their substrates) with genes and their protein products. The diagram below depicts the hyperlinks that are typically available within MetaCyc, allowing the user to navigate among pathways, genes, enzymes, etc.

Overview2.png
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

MetaCyc is a collaborative project between SRI International and the Boyce Thomson Institute for Plant Research. 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 on an ongoing basis using a literature-based strategy [def] [11].

2.7  Underlying Software

MetaCyc is based on the same retrieval and visualization software used by EcoCyc and other BioCyc databases — the Pathway Tools platform — which can be obtained here.

3  MetaCyc Availability

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

  • The MetaCyc data are available through the MetaCyc Web site for interactive querying and visualization.

  • The MetaCyc data can be downloaded as a set of data files. These files can be parsed and queried using languages like Perl, 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.

  • A downloadable program that combines the Pathway Tools software with MetaCyc and the other BioCyc PGDBs can be installed on computers at your site, with the following advantages:

    • It provides some functionality that is not provided by the MetaCyc Web site, such as comparative analysis of entire metabolic pathways

    • It usually runs more quickly than the Web site

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

    • It supports running an equivalent of the BioCyc Web site 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, from reviews, and from external 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:

Literature_based_curationv9.jpg

4.1  Information Types Captured During the MetaCyc Curation Process

4.1.1  Pathways

Pathways include a mini-review summary that includes:

  • Significance of the pathway

  • Relationship with other pathways

  • Organism within which the pathway exists

  • Experimental evidence for the pathway

  • Contradictory evidence

  • Citations

Other collected data include:

  • Synonyms

  • Taxonomic range

  • Key reactions

4.1.2  Reactions

  • EC number of enzyme activities, if applicable

  • Equilibrium constant

  • Whether this is a hypothetical reaction

  • Whether the reaction occurs spontaneously

  • Whether the reaction can be balanced (some published reactions are unbalanced)

4.1.3  Enzymes and Enzyme Complexes

Enzymes include a mini-review summary that covers:

  • Experimental evidence

  • Isoforms

  • Substrate specificity of each isoform

  • Tissue type

  • Citations

Other collected data:

  • Common name of the enzyme and synonym(s)

  • Subcellular location

  • Molecular weight (KDa)

  • pI

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

  • Km, Vmax, Kcat

  • Optimum pH

  • Optimum temperature

4.1.4  Genes

  • Common name of the gene and synonym(s)

  • Citations

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

4.1.5  Compounds

  • Synonym(s) of the compound name

  • Compound structure

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 contains experimentally elucidated metabolic pathways. MetaCyc pathways are labeled with the name of one or more taxonomic groups 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 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 listed in the taxonomic designations of all MetaCyc pathways. 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 Search Menu → 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 are 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 (discussed previously). 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 MetaCyc.org Web site and in downloadable data files.

  • Two releases are “major” releases, which include the updates available in minor releases, plus new versions of the downloadable Pathway Tools software that includes MetaCyc.

A detailed history of the enhancements to MetaCyc in each MetaCyc release is available here. This page also contains statistics on the size 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 new versions of the ExploreEnz DB into MetaCyc to ensure MetaCyc contains the latest EC number information.

  • Import a new version of the NCBI Taxonomy DB [12] into Pathway Tools to ensure MetaCyc has access to up-to-date taxonomy information.

  • 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 (e.g., subpathways listed for a given pathway must be valid database objects)

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

    • Validate literature references, e.g., check that PubMed IDs refer to actual PubMed entries

    • Check syntax of external database links

    • Check syntax of HTML embedded in comments

  • 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 not all enzymes have 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 that 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 that 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 primary literature. Every pathway and every enzyme 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 (IUBMB), as seen in the ExplorEnz database. Most such “EC reactions” were imported into MetaCyc via the ENZYME database, an enzyme nomenclature database based on the recommendations of the 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. Although in the IUBMB and the ENZYME DB the same compound may be referred to by different names in different reactions, in MetaCyc reactions the compound will always be represented by a reference to the same underlying database entry for the compound, and thus will be referred to in MetaCyc reactions by 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 format is not always used by the IUBMB. For example, reaction EC 3.4.24.86, 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. An additional difference 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 by the addition of protons where applicable.

UniProt protein database
Most of the enzymes 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, some 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 often 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 is published in [15].

KEGG contains two types of pathways: maps and modules. A KEGG map is typically derived from multiple literature sources, and typically integrates reactions and pathways found in multiple species (it is therefore chimeric) to create a pathway that is not found in its entirety in any one species. The KEGG web site can display KEGG maps with reactions colored to indicate which reactions within that map are predicted to be catalyzed by a given organism. We call these diagrams “species views of pathway maps.” The organism-specific PGDBs within BioCyc correspond to KEGG species views of its pathway maps. Since KEGG maps are in essence a collection of pathways found in multiple organisms, they are significantly larger than MetaCyc pathways on average. A type of pathway found in MetaCyc, called superpathways, are similar to KEGG maps in that they comprise multiple sub-pathways. However, unlike KEGG maps, most MetaCyc superpathways do occur in a single organism.

KEGG also contains a smaller type of pathway called a module. Modules are similar in size to individual MetaCyc pathways.

We argue that MetaCyc pathways (and KEGG modules) are closer to true biological pathways than are KEGG maps, in part because of the chimeric nature of KEGG maps. KEGG maps are typically 3–4 times larger than are KEGG modules and MetaCyc pathways because MetaCyc pathways attempt to model individual biological pathways from individual organisms. For example, KEGG map MAP00270 called “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.

The smaller pathways in MetaCyc (and KEGG modules) are advantageous for several reasons. First, these smaller pathways correspond more closely to biologically meaningful units — meaningful in the sense that they correspond to a single biological function, they are regulated as a unit, and they tend to be conserved through evolution. For example, a program for predicting the metabolic pathways of an organism could not predict methionine biosynthesis independently of cysteine degradation using KEGG maps (say in an organism that had only one of those pathways), because those two separate processes are fused into one unit in KEGG. Similarly, a program for enrichment analysis of transcriptomics data could not separately detect the enrichment of these two biosynthetic pathways. These issues are discussed in more detail in [16], which discusses how these different pathway ontologies can affect computational analyses of pathway data. KEGG modules are similar in extent to MetaCyc pathways, but KEGG’s collection of modules is very incomplete because they are a relatively new development in KEGG.

Maps and superpathways are useful in showing how individual pathways connect, and in presenting the larger biochemical context in which a pathway operates. MetaCyc pathways can be displayed at multiple detail levels, such as showing chemical structures for substrates. In addition, all MetaCyc pathway diagrams include chemical names and enzyme names; KEGG module diagrams contain unintelligible identifiers only.

MetaCyc version 21.1 (2017) contained 2,572 pathways, compared to the 316 modules in a KEGG version downloaded in January 2017. MetaCyc 21.1 contained 377 superpathways, compared to the 237 maps found in KEGG. MetaCyc contained 14,347 reactions, compared to 10,411 in KEGG. MetaCyc cited 54,000 articles from which its contents were derived and contained 7,900 textbook-equivalent pages of mini-review summaries for enzymes and pathways; KEGG contains small numbers of citations and of mini-reviews.

In addition, 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 KEGG maps do not state the species in which they were experimentally observed. 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 contains data on enzyme properties for specific enzymes from specific species, 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.

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

As of April 2018, KEGG contained 5,702 organisms whereas BioCyc version 22.0 (April 2018) contained 13,075 organism databases.

Forty nine of the BioCyc databases are designated as Tier 1 or Tier 2, meaning that have undergone anywhere from person-months of curation (e.g., the Bacillus subtilis and Saccharomyces cerevisiae databases) to person-years of curation (e.g., for Arabidopsis thaliana) to person-decades of curation (e.g., for EcoCyc and MetaCyc). In contrast, KEGG curates only its reference pathway maps and modules; it does not curate organism-specific views of those data; those views are generated computationally. Manually curated databases have a number of 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 cannot be predicted computationally 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 hypothesized to catalyze unidentified reactions in the organism), and operons. KEGG lacks the latter two predictors.

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

  • 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 (present in KEGG, but no animation of multiple time points)

    • The Omics Dashboard (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 from a starting to an ending compound; KEGG lacks such a tool.

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. As of January 18, the database contained 219​ ​pathways and 993​ ​enzymes from 543​ ​ microorganisms. Pathways are curated from the biomedical literature and contain significant comments and literature citations.

10.3  Reactome

Reactome is a curated database of biological processes in humans and 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.

11.1  Current Contributors

SRI International

Roles: Curation of pathways, software development, Website operations

Boyce Thomson Institute for Plant Research

Role: Curation of plant-related and fungal pathways

11.2  Past Contributors

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

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

  • Kate Dreher, Ph.D., International Maize and Wheat Improvement Center, CIMMYT

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

  • Martha Arnaud, Ph.D., Candida Genome Database, Stanford University

  • Joseph M. Dale, formerly at SRI

  • John Ingraham, Ph.D., UC Davis

  • Cindy Krieger, Ph.D., Saccharomyces Genome Database, Stanford University

  • A. Karthikeyan, Ph.D, formerly at the Carnegie Institute of Washington, Plant Department

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

  • John Pick, formerly at SRI

  • Liviu Popescu, formerly at SRI

  • Sunita Reddy, formerly at SRI

  • Fred Gilham, formerly at SRI

  • Monica Riley, Ph.D., Marine Biological Laboratory

  • 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

12  Submitting Pathways for Incorporation into MetaCyc

We at MetaCyc would like 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.

In addition, by submitting pathways to MetaCyc you 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 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: . 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:

[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.

13.1  Additional Publications

[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

References

[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. Nucleic 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. Nucleic 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.

[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.

[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.

[16]   M.L. Green and P.D. Karp. The outcomes of pathway database computations depend on pathway ontology. Nuc Acids Res, 34:3687–97, 2006.