MetaCyc Database Research Articles

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible database describing metabolic pathways and enzymes from all domains of life. MetaCyc pathways are experimentally determined, mostly small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains >2100 pathways derived from >37 000 publications, and is the largest curated collection of metabolic pathways currently available. BioCyc (BioCyc.org) is a collection of >3000 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems and pathway-hole fillers. Additions to BioCyc over the past 2 years include YeastCyc, a PGDB for Saccharomyces cerevisiae, and 891 new genomes from the Human Microbiome Project. The BioCyc Web site offers a variety of tools for querying and analysis of PGDBs, including Omics Viewers and tools for comparative analysis. New developments include atom mappings in reactions, a new representation of glycan degradation pathways, improved compound structure display, better coverage of enzyme kinetic data, enhancements of the Web Groups functionality, improvements to the Omics viewers, a new representation of the Enzyme Commission system and, for the desktop version of the software, the ability to save display states.

The challenge of constructing, classifying, and representing metabolic pathways.

Scientists, educators, and students benefit from having free and centralized access to the wealth of metabolic information that has been gathered over the decades. Curators of the MetaCyc database work to present this information in an easily understandable pathway-based framework. MetaCyc is used not only as an encyclopedic resource for metabolic information but also as a template for the pathway prediction software that generates pathway/genome databases for thousands of organisms with sequenced genomes (available at www.biocyc.org). Curators need to define pathway boundaries and classify pathways within a broader pathway ontology to maximize the utility of the pathways to both users and the pathway prediction software. These seemingly simple tasks pose several challenges. This review describes these challenges as well as the criteria that need to be considered, and the rules that have been developed by MetaCyc curators as they make decisions regarding the representation and classification of metabolic pathway information in MetaCyc. The functional consequences of these decisions in regard to pathway prediction in new species are also discussed.

CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes

We present a web-based network-construction system, CINPER (CSBL INteractive Pathway BuildER), to assist a user to build a user-specified gene network for a prokaryotic organism in an intuitive manner. CINPER builds a network model based on different types of information provided by the user and stored in the system. CINPER’s prediction process has four steps: (i) collection of template networks based on (partially) known pathways of related organism(s) from the SEED or BioCyc database and the published literature; (ii) construction of an initial network model based on the template networks using the P-Map program; (iii) expansion of the initial model, based on the association information derived from operons, protein-protein interactions, co-expression modules and phylogenetic profiles; and (iv) computational validation of the predicted models based on gene expression data. To facilitate easy applications, CINPER provides an interactive visualization environment for a user to enter, search and edit relevant data and for the system to display (partial) results and prompt for additional data. Evaluation of CINPER on 17 well-studied pathways in the MetaCyc database shows that the program achieves an average recall rate of 76% and an average precision rate of 90% on the initial models; and a higher average recall rate at 87% and an average precision rate at 28% on the final models. The reduced precision rate in the final models versus the initial models reflects the reality that the final models have large numbers of novel genes that have no experimental evidences and hence are not yet collected in the MetaCyc database. To demonstrate the usefulness of this server, we have predicted an iron homeostasis gene network of Synechocystis sp. PCC6803 using the server. The predicted models along with the server can be accessed at http://csbl.bmb.uga.edu/cinper/.

Accurate Atom-Mapping Computation for Biochemical Reactions

The complete atom mapping of a chemical reaction is a bijection of the reactant atoms to the product atoms that specifies the terminus of each reactant atom. Atom mapping of biochemical reactions is useful for many applications of systems biology, in particular for metabolic engineering where synthesizing new biochemical pathways has to take into account for the number of carbon atoms from a source compound that are conserved in the synthesis of a target compound. Rapid, accurate computation of the atom mapping(s) of a biochemical reaction remains elusive despite significant work on this topic. In particular, past researchers did not validate the accuracy of mapping algorithms. We introduce a new method for computing atom mappings called the minimum weighted edit-distance (MWED) metric. The metric is based on bond propensity to react and computes biochemically valid atom mappings for a large percentage of biochemical reactions. MWED models can be formulated efficiently as Mixed-Integer Linear Programs (MILPs). We have demonstrated this approach on 7501 reactions of the MetaCyc database for which 87% of the models could be solved in less than 10 s. For 2.1% of the reactions, we found multiple optimal atom mappings. We show that the error rate is 0.9% (22 reactions) by comparing these atom mappings to 2446 atom mappings of the manually curated Kyoto Encyclopedia of Genes and Genomes (KEGG) RPAIR database. To our knowledge, our computational atom-mapping approach is the most accurate and among the fastest published to date. The atom-mapping data will be available in the MetaCyc database later in 2012; the atom-mapping software will be available within the Pathway Tools software later in 2012.

The Pathway Tools Pathway Prediction Algorithm

The PathoLogic component of the Pathway Tools software performs prediction of metabolic pathways in sequenced and annotated genomes. This article provides a detailed presentation of the PathoLogic algorithm. The algorithm consists of two phases. The reactome inference phase infers the reactions catalyzed by the organism from the set of enzymes present in the annotated genome. The pathway inference phase infers the metabolic pathways present in the organism from the reactions catalyzed by the organism. Both phases draw on the MetaCyc database of metabolic reactions and pathways. MetaCyc contains two data fields to support pathway inference: the expected taxonomic range of each pathway, and a list of key reactions for pathways. These fields have significantly increased the predictive accuracy of PathoLogic.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (http://metacyc.org/) provides a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains more than 1800 pathways derived from more than 30 000 publications, and is the largest curated collection of metabolic pathways currently available. Most reactions in MetaCyc pathways are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes and literature citations. BioCyc (http://biocyc.org/) is a collection of more than 1700 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference database, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs contain additional features, including predicted operons, transport systems and pathway-hole fillers. The BioCyc website and Pathway Tools software offer many tools for querying and analysis of PGDBs, including Omics Viewers and comparative analysis. New developments include a zoomable web interface for diagrams; flux-balance analysis model generation from PGDBs; web services; and a new tool called Web Groups.

Improved genome annotation through untargeted detection of pathway-specific metabolites

BackgroundMass spectrometry-based metabolomics analyses have the potential to complement sequence-based methods of genome annotation, but only if raw mass spectral data can be linked to specific metabolic pathways. In untargeted metabolomics, the measured mass of a detected compound is used to define the location of the compound in chemical space, but uncertainties in mass measurements lead to "degeneracies" in chemical space since multiple chemical formulae correspond to the same measured mass. We compare two methods to eliminate these degeneracies. One method relies on natural isotopic abundances, and the other relies on the use of stable-isotope labeling (SIL) to directly determine C and N atom counts. Both depend on combinatorial explorations of the "chemical space" comprised of all possible chemical formulae comprised of biologically relevant chemical elements.ResultsOf 1532 metabolic pathways curated in the MetaCyc database, 412 contain a metabolite having a chemical formula unique to that metabolic pathway. Thus, chemical formulae alone can suffice to infer the presence of some metabolic pathways. Of 248,928 unique chemical formulae selected from the PubChem database, more than 95% had at least one degeneracy on the basis of accurate mass information alone. Consideration of natural isotopic abundance reduced degeneracy to 64%, but mainly for formulae less than 500 Da in molecular weight, and only if the error in the relative isotopic peak intensity was less than 10%. Knowledge of exact C and N atom counts as determined by SIL enabled reduced degeneracy, allowing for determination of unique chemical formula for 55% of the PubChem formulae.ConclusionsTo facilitate the assignment of chemical formulae to unknown mass-spectral features, profiling can be performed on cultures uniformly labeled with stable isotopes of nitrogen (15N) or carbon (13C). This makes it possible to accurately count the number of carbon and nitrogen atoms in each molecule, providing a robust means for reducing the degeneracy of chemical space and thus obtaining unique chemical formulae for features measured in untargeted metabolomics having a mass greater than 500 Da, with relative errors in measured isotopic peak intensity greater than 10%, and without the use of a chemical formula generator dependent on heuristic filtering. These chemical formulae can serve as indicators for the presence of particular metabolic pathways.

A metaproteomic analysis of the human salivary microbiota by three-dimensional peptide fractionation and tandem mass spectrometry.

Metagenomics uses gene expression patterns to understand the taxonomy and metabolic activities of microbial communities. Metaproteomics applies the same approach to community proteomes. Previously, we used a novel three-dimensional peptide separation method to identify over 2000 salivary proteins. This study used those data to carry out the first metaproteomic analysis of the human salivary microbiota. The metagenomic software MEGAN generated a phylogenetic tree, which was checked against the Human Oral Microbiome Database (HOMD). Pathway analyses were performed with the Clusters of Orthologous Groups and MetaCyc databases. Thirty-seven per cent of the peptides were identifiable only at the level of cellular organisms or bacteria. The rest were distributed among five bacterial phyla (61%), archea (0.5%), and viruses (0.8%); 29% were assignable at the genus level, and most belonged to Streptococcus (17%). Eleven per cent of all peptides could be assigned to species. Most taxa were represented in HOMD and they included well-known species such as periodontal pathogens. However, there also were 'exotic' species including aphid endosymbionts; plant, water, and soil bacteria; extremophiles; and archea. The pathway analysis indicated that peptides were linked to translation (37%), followed by glycolysis (19%), amino acid metabolism (8%), and energy production (8%). The taxonomic structure of the salivary metaproteome is very diverse but is dominated by streptococci. 'Exotic' species may actually represent close relatives that have not yet been sequenced. Salivary microbes appear to be actively engaged in protein synthesis, and the pathway analysis is consistent with the metabolism of salivary glycoproteins.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism.

Plant Metabolic Pathways in MetaCyc and SolCyc

AbstractMetaCyc is a metabolic encyclopedia of experimentally validated biochemical pathways curated from scientific literature, that spans all organisms, with an emphasis on plants and microbes. The Pathway tools is a complex curation software suite that enables curation of reactions, construction of pathways and annotation with one or more representative enzymes, that include information such as substrate specificity, kinetic properties, activators, inhibitors, cofactor requirements, genes if cloned and links to external databases. In addition curators are able to provide concise, review-level summaries and extensive literature citations. The present database release includes more than 1200 pathways from more than 1549 organisms, 7312 reactions, 5127 enzymes, 4748 genes, 7234 chemical compounds, curated from 17916 citations. The MetaCyc database is the reference database on which the pathways are predicted from annotated genomes by PathoLogic called Pathway/genome Databases (PGDB's). The Biocyc Database ("biocyc.org":http://biocyc.org) has a collection over 300 PGDB's. Each BioCyc Database describes the genome and predicted metabolic pathways of a single organism, which are then taken up by interested groups for curation. SolCyc is one such PGDB, developed for the clade oriented Solanceae Genomics Network (SGN) database. It has predicted metabolic pathway databases of significant species belonging to Solanaceae and includes Lycocyc(tomato), Solacyc (eggplant), Nicotianacyc (tobacco),Petuniacyc (Petunia), Capcyc (Capsicum) , Potatocyc (potato). An interactive webinterface has been developed for the seamless flow of information from the SGN phenotype and locus database with SolCyc. This facilitates researchers with the capacity to search for underlying metabolic pathway information of genes and phenotypes that has been curated into the SolCyc database.

Reconstruction of metabolic pathways for the cattle genome

BackgroundMetabolic reconstruction of microbial, plant and animal genomes is a necessary step toward understanding the evolutionary origins of metabolism and species-specific adaptive traits. The aims of this study were to reconstruct conserved metabolic pathways in the cattle genome and to identify metabolic pathways with missing genes and proteins. The MetaCyc database and PathwayTools software suite were chosen for this work because they are widely used and easy to implement.ResultsAn amalgamated cattle genome database was created using the NCBI and Ensembl cattle genome databases (based on build 3.1) as data sources. PathwayTools was used to create a cattle-specific pathway genome database, which was followed by comprehensive manual curation for the reconstruction of metabolic pathways. The curated database, CattleCyc 1.0, consists of 217 metabolic pathways. A total of 64 mammalian-specific metabolic pathways were modified from the reference pathways in MetaCyc, and two pathways previously identified but missing from MetaCyc were added. Comparative analysis of metabolic pathways revealed the absence of mammalian genes for 22 metabolic enzymes whose activity was reported in the literature. We also identified six human metabolic protein-coding genes for which the cattle ortholog is missing from the sequence assembly.ConclusionCattleCyc is a powerful tool for understanding the biology of ruminants and other cetartiodactyl species. In addition, the approach used to develop CattleCyc provides a framework for the metabolic reconstruction of other newly sequenced mammalian genomes. It is clear that metabolic pathway analysis strongly reflects the quality of the underlying genome annotations. Thus, having well-annotated genomes from many mammalian species hosted in BioCyc will facilitate the comparative analysis of metabolic pathways among different species and a systems approach to comparative physiology.

The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

MetaCyc (MetaCyc.org) is a universal database of metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are curated from the primary scientific literature, and are experimentally determined small-molecule metabolic pathways. Each reaction in a MetaCyc pathway is annotated with one or more well-characterized enzymes. Because MetaCyc contains only experimentally elucidated knowledge, it provides a uniquely high-quality resource for metabolic pathways and enzymes. BioCyc (BioCyc.org) is a collection of more than 350 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the predicted metabolic network of one organism, including metabolic pathways, enzymes, metabolites and reactions predicted by the Pathway Tools software using MetaCyc as a reference database. BioCyc PGDBs also contain predicted operons and predicted pathway hole fillers—predictions of which enzymes may catalyze pathway reactions that have not been assigned to an enzyme. The BioCyc website offers many tools for computational analysis of PGDBs, including comparative analysis and analysis of omics data in a pathway context. The BioCyc PGDBs generated by SRI are offered for adoption by any interested party for the ongoing integration of metabolic and genome-related information about an organism.

Using the MetaCyc pathway database and the BioCyc database collection.

The MetaCyc database (http://metacyc.org) is a collection of more than a thousand metabolic pathways from a wide variety of organisms, collected from the literature by manual curation. BioCyc contains more than 370 Pathway/Genome Databases, each of which describes the genome and predicted metabolic pathways of a single organism, as computationally predicted from the annotated genomes, using MetaCyc data as a reference. The protocols in this unit introduce the user to the extensive data content available in MetaCyc and BioCyc, and to mechanisms for querying, visualizing, and analyzing these data using the Pathway Tools software.

MedicCyc: a biochemical pathway database forMedicago truncatula

There is an imperative need to integrate functional genomics data to obtain a more comprehensive systems-biology view of the results. We believe that this is best achieved through the visualization of data within the biological context of metabolic pathways. Accordingly, metabolic pathway reconstruction was used to predict the metabolic composition for Medicago truncatula and these pathways were engineered to enable the correlated visualization of integrated functional genomics data. Metabolic pathway reconstruction was used to generate a pathway database for M. truncatula (MedicCyc), which currently features more than 250 pathways with related genes, enzymes and metabolites. MedicCyc was assembled from more than 225,000 M. truncatula ESTs (MtGI Release 8.0) and available genomic sequences using the Pathway Tools software and the MetaCyc database. The predicted pathways in MedicCyc were verified through comparison with other plant databases such as AraCyc and RiceCyc. The comparison with other plant databases provided crucial information concerning enzymes still missing from the ongoing, but currently incomplete M. truncatula genome sequencing project. MedicCyc was further manually curated to remove non-plant pathways, and Medicago-specific pathways including isoflavonoid, lignin and triterpene saponin biosynthesis were modified or added based upon available literature and in-house expertise. Additional metabolites identified in metabolic profiling experiments were also used for pathway predictions. Once the metabolic reconstruction was completed, MedicCyc was engineered to visualize M. truncatula functional genomics datasets within the biological context of metabolic pathways. freely accessible at http://www.noble.org/MedicCyc/

A Method for Species Comparison of Metabolic Networks Using Reaction Profile

Comparative analyses of the metabolic networks among different species provide important information regarding the evolution of organisms as well as pharmacological targets. In this paper, a method is proposed for comparing metabolic networks based on enzymatic reactions within different species. Specifically, metabolic networks are handled as sets of enzymatic reactions. Based on the presence or absence of metabolic reactions, the metabolic network of an organism is represented by a bit string comprised of the digits “1” and “0, ” called the “reaction profile.” Then, the degree of similarity between bit strings is defined, followed by clustering of metabolic networks by different species. By applying our method to the metabolic networks of 33 representative organisms selected from bacteria, archaea, and eukaryotes in the MetaCyc database, a phylogenetic tree was reconstructed that represents the similarity of metabolic network based on metabolic phenotypes.

In Silico Reconstruction of the Metabolic Pathways of Lactobacillus plantarum : Comparing Predictions of Nutrient Requirements with Those from Growth Experiments

On the basis of the annotated genome we reconstructed the metabolic pathways of the lactic acid bacterium Lactobacillus plantarum WCFS1. After automatic reconstruction by the Pathologic tool of Pathway Tools (http://bioinformatics.ai.sri.com/ptools/), the resulting pathway-genome database, LacplantCyc, was manually curated extensively. The current database contains refinements to existing routes and new gram-positive bacterium-specific reactions that were not present in the MetaCyc database. These reactions include, for example, reactions related to cell wall biosynthesis, molybdopterin biosynthesis, and transport. At present, LacplantCyc includes 129 pathways and 704 predicted reactions involving some 670 chemical species and 710 enzymes. We tested vitamin and amino acid requirements of L. plantarum experimentally and compared the results with the pathways present in LacplantCyc. In the majority of cases (32 of 37 cases) the experimental results agreed with the final reconstruction. LacplantCyc is the most extensively curated pathway-genome database for gram-positive bacteria and is open to the microbiology community via the World Wide Web (www.lacplantcyc.nl). It can be used as a reference pathway-genome database for gram-positive microbes in general and lactic acid bacteria in particular.

Expansion of the BioCyc collection of pathway/genome databases to 160 genomes

The BioCyc database collection is a set of 160 pathway/genome databases (PGDBs) for most eukaryotic and prokaryotic species whose genomes have been completely sequenced to date. Each PGDB in the BioCyc collection describes the genome and predicted metabolic network of a single organism, inferred from the MetaCyc database, which is a reference source on metabolic pathways from multiple organisms. In addition, each bacterial PGDB includes predicted operons for the corresponding species. The BioCyc collection provides a unique resource for computational systems biology, namely global and comparative analyses of genomes and metabolic networks, and a supplement to the BioCyc resource of curated PGDBs. The Omics viewer available through the BioCyc website allows scientists to visualize combinations of gene expression, proteomics and metabolomics data on the metabolic maps of these organisms. This paper discusses the computational methodology by which the BioCyc collection has been expanded, and presents an aggregate analysis of the collection that includes the range of number of pathways present in these organisms, and the most frequently observed pathways. We seek scientists to adopt and curate individual PGDBs within the BioCyc collection. Only by harnessing the expertise of many scientists we can hope to produce biological databases, which accurately reflect the depth and breadth of knowledge that the biomedical research community is producing.

Negative mode sheathless capillary electrophoresis electrospray ionization-mass spectrometry for metabolite analysis of prokaryotes

Capillary electrophoresis (CE) was coupled to negative mode electrospray ionisation-mass spectrometry (MS) for separation and detection of phosphorylated and acidic metabolites in extracts of prokaryotes. Unlike previous CE–MS systems for metabolite analysis, a sheathless interface was used to improve sensitivity. To accomplish this, the separation capillary was modified by creating a porous junction near the outlet where the electrospray voltage and cathodic voltage for CE were applied. The outlet of the capillary was pulled to a 5 μm inner diameter to form an electrospray emitter and had a frit fabricated near the exit to prevent clogging. During analysis pressure was applied at the inlet of the separation column to create sufficient flow towards the detector. Limits of detection for 19 metabolites in full scan mode ranged from 20 nM for ADP ribose to 2.5 μM for α-ketoglutarate for 40 nL injections. Extracts of Escherichia coli, strain DH5-α, were analyzed using this system. In full scan mode, 118 different metabolites were detected. Tandem mass spectrometry was also employed to attempt identification. Reproducible fragmentation of 19 parent peaks was found and 10 of these produced spectra that were consistent with identification obtained from matching to compounds in the MetaCyc database. These results demonstrate the utility of a sensitive CE–MS system for large scale metabolite detection in biological samples.

Metabolomic Analysis of Eukaryotic Tissue and Prokaryotes Using Negative Mode MALDI Time-of-Flight Mass Spectrometry

Metabolites in islets of Langerhans and Escherichia coli strain DH5-alpha were analyzed using negative-mode, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). For analysis of anionic metabolites by MALDI, 9-aminoacridine as the matrix yielded a far superior signal in comparison to alpha-cyano-4-hydroxycinnamic acid, 2,5-dihydrobenzoic acid, 2,4,6,-trihydroxyacetophenone, and 3-hydroxypicolinic acid. Limits of detection for metabolite standards were as low as 15 nM for GDP, GTP, ADP, and ATP and as high as 1 muM for succinate in 1-muL samples. Analysis of islet extracts allowed detection of 44 metabolites, 29 of which were tentatively identified by matching molecular weight to compounds in METLIN and KEGG databases. Relative quantification was demonstrated by comparing the ratio of selected di- and triphosphorylated nucleotides for islets incubated with different concentrations of glucose. For islets at 3 mM glucose, concentration ratios of ATP/ADP, GTP/GDP, and UTP/UDP were 1.9 +/- 1.39, 1.12 +/- 0.50, and 0.79 +/- 0.35 respectively, and at 20 mM glucose stimulation, the ratios increased to 4.13 +/- 1.89, 5.62 +/-4.48, and 4.30 +/- 4.07 (n = 3). Analysis was also performed by placing individual, intact islets on a MALDI target plate with matrix and impinging the laser directly on the dried islet. Direct analysis of single islets allowed detection of 43 metabolites, 28 of which were database identifiable. A total of 43% of detected metabolites from direct islet analysis were different from those detected in islet extracts. The method was extended to prokaryotic cells by analysis of extracts from E. coli. Sixty metabolites were detected, 39 of which matched compounds in the MetaCyc database. A total of 27% of the metabolites detected from prokaryotes overlapped those found in islets. These results show that MALDI can be used for detection of metabolites in complex biological samples.

MetaCyc: a multiorganism database of metabolic pathways and enzymes.

The MetaCyc database (see URL http://MetaCyc.org) is a collection of metabolic pathways and enzymes from a wide variety of organisms, primarily microorganisms and plants. The goal of MetaCyc is to contain a representative sample of each experimentally elucidated pathway, and thereby to catalog the universe of metabolism. MetaCyc also describes reactions, chemical compounds and genes. Many of the pathways and enzymes in MetaCyc contain extensive information, including comments and literature citations. SRI's Pathway Tools software supports querying, visualization and curation of MetaCyc. With its wide breadth and depth of metabolic information, MetaCyc is a valuable resource for a variety of applications. MetaCyc is the reference database of pathways and enzymes that is used in conjunction with SRI's metabolic pathway prediction program to create Pathway/Genome Databases that can be augmented with curation from the scientific literature and published on the world wide web. MetaCyc also serves as a readily accessible comprehensive resource on microbial and plant pathways for genome analysis, basic research, education, metabolic engineering and systems biology. In the past 2 years the data content and the Pathway Tools software used to query, visualize and edit MetaCyc have been expanded significantly. These enhancements are described in this paper.