A significant problem in the study of mechanisms of an organism's development is the elucidation of interrelated factors which are making an impact on the different levels of the organism, such as genes, biological molecules, cells, and cell systems. Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study. Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems.Here we report on a new version of BiologicalNetworks, a research environment for the integral visualization and analysis of heterogeneous biological data. BiologicalNetworks can be queried for properties of thousands of different types of biological entities  and their relations . The system includes the build-pathways infrastructure for molecular interactions/relations and module discovery in high-throughput experiments. Also implemented in BiologicalNetworks are the Integrated Genome Viewer and Comparative Genomics Browser applications, which allow for the search and analysis of gene regulatory regions and their conservation in multiple species in conjunction with molecular pathways/networks, experimental data and functional annotations.http://www.biologicalnetworks.org.The new release of BiologicalNetworks together with its back-end database introduces extensive functionality for a more efficient integrated multi-level analysis of microarray, sequence, regulatory, and other data. BiologicalNetworks is freely available at  As substantial amounts of data concerning expression, interactions/pathways, sequences, and other types of information for a variety of tissues, developmental stages, stimuli and organisms are generated, it becomes difficult for researchers with no background in bioinformatics and statistics to extract the information they seek. Successful data integration is hampered by the orthogonal nature of the integrated data and by the multitude of controversies and name/ID conflicts in public databases. Examples of conflicts that cannot be automatically resolved include the situations when genes with the same name point to different chromosome locations or a gene/protein in different modification states has different names; for example, p53, p53361-393), p53(modified:Thr:212), or pCMX-mutant-p53. Among the name/ID conflicts that can be resolved is, for example, the conflict between different genes/proteins having the same synonym or the conflict between two databases naming the same gene differently - these and similar name/ID conflicts can be automatically resolved if there are other databases that recognize the conflicting names. To analyze and visually integrate publicly available data on the systems level, several web-based tools have been developed: Genomatix -393, p53, CytoscaIn this work, the application BiologicalNetworks 2.0 for integration of functional genomics data with biological networks is presented. In comparison with other tools . IntegromeDB data is stored in a Postgres database under the MetaGraph schema and is updated monthly, being synchronized with the latest changes in most current databases. Detailed information on the statistics, integrated databases catalog, and organism list can be found at http://www.integromedb.org.The backend database of BiologicalNetworks, called IntegromeDB , is a sehttp://www.w3.org/RDF/ and the Web Ontology Language (OWL) http://www.w3.org/TR/owl-ref/. IntegromeDB also enables researchers to integrate their own data into the database as described in the section 'Integration of User's Data.'The procedure of data integration and mapping to the internal database is fully automated and is based on the Semantic Web technologies, such as the Resource Description Framework (RDF) http://www.bioontology.org. The mapping among the OBO ontologies, which is provided by OBO, allows for the automatic integration of 98 ontologies in BioNets Ontology. The basic.owl file with Basic Ontology and mappings from it to other ontologies can be downloaded at http://www.integromedb.org/bionetsonto.php.The IntegromeDB schema is based on BioNets Ontology, the core of which is Basic Ontology that was manually developed by the authors. Basic Ontology describes classes from different domains, such as, protein, gene, pathway, interaction, disease, cell, tissue, drug, chromosome, COG functional group, gene set . Basic Ontology is manually mapped onto 25 OBO ontologies, including Sequence Ontology, GeneOntology, Human Disease, CheBI, BRENDA Tissues, that were selected from the best curated and regularly updated ontologies provided by the OBO consortium Due to the generic schema of the database and ontology-driven mapping, during integration, new objects and their properties are automatically added in the database. For example, if the database stored information about the interaction between the two objects, proteins × and Y, new information about this interaction will be integrated as a new property(s), e.g., a property 'p-value from the experiment A', and the 'experiment A' will be added in the database as a new object. If a clear evidence of, or reference to, a class from the BioNets ontology is absent (missed), an automatic procedure to statistically evaluate the content of the integrated table and assign a term from the ontology is applied. The procedure takes each word and word combination in the table, search for them in the BioNets ontology, calculates the statistical significance of the occurrence, and assigns the most significant term to the table.http://www.integromedb.org. If the user searches the http://integromedb.org page for an object for which inconsistencies were calculated and found, they can be seen on the result search page by clicking the 'red button'.Upon the integration of a new data source, the database automatically identifies conflicts in names, name synonyms and IDs of all objects in the database  among various databases and identification systems. All names/IDs are weighted and sorted by the number of integrated data sources, supporting the name/ID. When the user searches the database, names/IDs appear in the search result in the descending order by weight; the potential conflicts will appear in the bottom of the list. Thus, if one database, for example, names the specific gene as × and another database, as Y, both names, × and Y, will be equally searchable; while in the search result, the name that is the most common among all integrated databases will appear first. However, no conflict data are removed or become invisible to the user, since the data sources are not weighted or judged. Inconsistencies among the names/IDs of all objects in the database that were found for human, mouse and rat are provided at http://www.integromedb.org; however, in comparison with the application BiologicalNetworks, described in this work, the web site provides only general quick search capabilities and no extensive data analysis, dynamic integration, and visualization capabilities.The IntegromeDB data can be searched at The typical user of BiologicalNetworks starts with loading the file or searching the integrated database for a list of genes , networks (.sif-file), curated pathways (.SBML), microarrays, proteomics data (tab-delimited file), or sequence data . BiologicalNetworks provides an Integrative View of the found data Figure , openingMost current web-based tools are focused on retrieving expression and/or co-expression patterns for individual genes from particular microarray experiment. Multi-experiment/multi-gene co-expression analysis is a labor-intensive and computationally challenging task, involving collecting suitable datasets, data downloads, preprocessing, normalization, and gene annotation management, the integration of different datasets, merging cross-platform data, and handling ambiguous mappings between genes and probe sets. Microarray databases Gene Expression Omnibus (GEO)  and Arrae.g. a set of transcription factors regulating a set of co-expressed genes), since several studies exploit the fact that co-expressed and/or functionally related genes may be transcriptionally coordinated [The user can upload the microarray data files and analyze them in conjunction with the integrated compendium of publicly available microarray data using the Microarray Analysis window. When searching in the Microarray Analysis window, the user can select the 'Default', 'Co-expression pairs' or 'Co-expression Triples' search modes. In the first case the search will return all available microarray experiments in which genes from the input gene list are over- or under-expressed. In the second case, it will return experiments and gene pairs where genes are co-expressed; and in the third case, it will return gene triples and experiments where these gene triples are co-expressed. This last option is especially important for discovering gene regulatory modules . Our gene list functional enrichment analysis currently uses as many as 8 annotation categories including 3 categories of GO terms, curated (e.g. KEGG) pathways, diseases, cell types, tissue gene expression, phenotypes and human anatomy  in our internal database, we visualize them as tables ('Themes' Tables) for the sake of usability and easier navigation Figure . Since Bhttp://www.integromedb.org/bionetsonto.php nodes  can be introduced and modified with minimal impact on the rest of the system. This is implemented through 'ontology mapping' [Gene, 'SO:12345') a new class that maintains mapping to source ontology is generated (i.e. class 'mappingSO:012345') which is connected to a Gene in our BioNets ontology through 'same_as' relation. This is done to not modify BioNets ontology classes every time new ontology is ingested, thus while 'unifying' different biological data types to keep specificity of every member schema of our integrated database. Examples of operations on RI-trees that will apply on all substructures , called SUB_X, are represented below:Sequence data together with annotation data, including binding sites, promoters, and other regulatory regions, that have been integrated in the BiologicalNetworks database represent the collection of interval trees; a single interval tree is created per chromosome instead of per annotated DNA sequence regions. Nodes of the interval (RI)-trees, or sequence intervals. are connected to the BioNets Ontology s Figure  through mapping' : for eveifOverlap function: SUB-X * SUB_X - > {0, 1}, returns true if the two interval substructures overlap.Next function: SUB_X ->SUB_X is applicable on data types for which there is a strict ordering on the domain; it returns the sub-structure encountered next in the ordering input substructure. The semantics of 'next' depends upon the data types .Intersect function: SUB_X * SUB_X ->SUB_X, returns the intersection of two SUB-X. This operation is valid for convex data types such as sequences and rectangles.Navigation    of segment elements and different annotations (properties) integrated from many data sources for one gene or gene upstream region. Genomic Sequences are integrated with the meta-graph schema of Biological-Networks database through an ElementId-ObjectID connection table, where elements are sequence elements, for example, a core promoter, TATA box, or binding site, that are attributed to a particular gene by means of known localization in the gene, according to the GenBank global position. Internal enumerations in the integrated databases-TRANSFAC, for example, provides localization of regulatory regions in respect to the transcription start-are recalculated accordingly. The connection table assigns sequence elements to meta-graph objects, so that sequence elements, represented as a RI-tree structure, become graph objects within the meta-graph database. All heterogeneous data, for example gene properties in the Property Panel  Figure  and Anno) Figure . Figure In the Sequence Analysis Browser, the user can upload  large (GBs) volumes of sequence data and analyze them together with the integrated data on gene sequences, annotations, orthologs and cross-references to the major biological databases displayed in the Sequence Annotation Browser Figure . The broBiologicalNetworks provides the ability to investigate transcriptional cascades by integrating and visualizing transcription factor gene regulation networks, relevant transcription factor binding sites and target genes with multiple sequence annotations, thus facilitating validation experiments (e.g. primer design applications) Figure . A fundaThe Comparative Genomics Browser can be explored together with other modules/windows of BiologicalNetworks. For example, Figure The BiologicalNetworks interface contains multiple search and build pathways/networks capabilities Figure , allowinThe seven querying options listed below allow for the specification and search for any logical combination of entities, processes/relations and their properties. The first four options are available from Quick Search Box at the top right corner of the program Figure  and alloSimple Gene/Protein search (default search). For example, the search for the three genes/proteins 'p53, egfr, esr1' returns these three genes in all specified species and gene properties. The list of genes/proteins can be uploaded from the file.1. Keyword search. For example, the keyword search 'p53, egfr, esr1' returns all database objects, including experiments, publications, pathways, and all properties of all objects that contain either p53, egfr, or esr1.2. Wild card search. For example, the search 'neurodeg*' returns all objects related to neurodegenerative, neurodegeneration, and all words in the databases beginning with 'neurodeg'.3. Multi-word search. For example, the search 'obesity diabetes'  returns the results for 'obesity AND diabetes' and 'obesity OR diabetes'.4. Build Pathway Wizard: Path Queries. Build Pathway Wizard contains dozens types of pathway and network searches (in the opened networks/pathways or integrated database) in protein-protein interactions, transcription factor-DNA networks, relational  networks and curated pathways (e.g. KEGG)  Figure . The thr) Figure  allow usa) algorithm type for pathway building;b) select the directionality of relations;c) types of objects and property values  to be included in the pathway; andd) relations  to be included in the new pathway.Specialized search  or GEO experiments (Series and Datasets). The system recognizes most of the currently available gene/protein IDs and synonyms for thousands of organisms integrated from over 100 data sources. To perform a search in the microarray, pathways/networks, sequence annotations and PubMed repositories, the user can specify any combination of keywords, including authors names, tissue types, diseases, gene/protein names. The Search Box contains different configuration options and filters and enables limiting searches on specific species, opened network/pathway and sequences. The organism drop-down menus in each search window include 21 model organisms, which are mostly represented in the database, and the following options to narrow down the search and subsequent data analysis: All Organisms, Eukaryotes, Prokaryotes and Viruses. Eukaryotes are subdivided into Plants, Fungi, Protists, Archea, and Metazoa/Animals, which in turn are subdivided into Vertebrate, Invertebrate and Mammals.Search Boxes accept lists of gene names (that can be loaded from files), accession numbers from public databases , PSI-MI, Tab-delimited network file, SBML and BiGG model format;• microarray data: tab-delimited file format, Illumina tab-delimited and Affymetrix file formats;• sequences: GenBank  and FASTA formats.The results of analysis and visualization in BiologicalNetworks can be saved at any moment as the BioNets XML Project file and then opened at other computers; the user's settings, data files, results of search, built networks, clustering, colorings and all other visualizations will appear exactly how they were at the moment of the file saving. The project can be also saved/exported to the SIF and SVG formats.http://integromedb.org.BiologicalNetworks allows the user to work with his/her own data, and to integrate them into the system database (IntegromeDB). The integration procedure is different from the procedure of loading/opening data files described above in that the loaded data are available to the user and also to whoever obtains the BioNets XML Project file that includes the user's data. The integration procedure allows the data to be made public; they become integrated in IntegromeDB as any other database and become searchable in the BiologicalNetworks application and at the web-page http://integromedb.org under "User's Data Integration" menu  problems are minor problems that can be quickly fixed by the developers, if the Bug and Problem Report Tools are used.To address the critical need for user support, we developed Bug and Problem Report Tools. This tool allows the user to report problems or bugs, while working in BiologicalNetworks. During the installation of BiologicalNetworks and the initial run of the program, the user is asked for the agreement for permission to send from his computer any future bug reports. If the user agrees, Bug Reports will be automatically generated and sent to our support server; it will include the environment settings and the last user's steps before the program gave an error. To report the problem, the user needs to use Problem Report Tool that is located in the 'Tools' menu of the program. The Streptococcus pneumoniae -human/mouse/rat interactions [Giardia lamblia [Thermatoga maritime [The BiologicalNetworks analytical and querying functionality were applied to and tested in a number of different biological systems/projects, both eukaryotic and prokaryotic: host-pathogen interactions, specifically, the influenza and ractions , 'meta-gractions , yeast mractions , whole-gractions , parasit lamblia , and micmaritime . All desRattus norvegicus [In this section, we demonstrate a case study of the search for potential therapeutic targets for hypertension. Specifically, it is shown how, using BiologicalNetworks and starting with a single microarray experiment in the model organism rvegicus , one canFirst, among about 1000 genes significantly perturbed in hypertension in the microarray experiment  Figure , we foun-3 and examined visually for consistency. In the result, we obtained 103 TFs that might potentially regulate transcription of 18 hypertension-specific genes . To construct the final network, we searched for all reported interactions among identified 103 TFs and 20 genes  which can be replicated by simply running the respective project.http://www.biologicalnetworks.org.BiologicalNetworks, along with the user Manual and Video tutorials and Quick Start Guide, is available at Project name: BiologicalNetworksProject home page: http://www.biologicalnetworks.orgOperating systems: Windows 2000/XP/Vista/7, Linux/Ubuntu/Redhat, MacOSXProgramming language: JavaLicense: Free for academic purposesOther requirements: 2GB RAMMB, AG and MS contributed to system concept. SK, YD, MS and MB implemented the system and performed major programming work. MB and JP coordinated this work, contributed to data analysis and wrote the manuscript. All authors read and approved the final manuscript.Methods. Detailed description of the methods and data types used in the BiologicalNetworks system.Click here for file


Cells coordinate their metabolism, proliferation, and cellular communication according to environmental cues through signal transduction. Because signal transduction has a primary role in cellular processes, many experimental techniques and approaches have emerged to discover the molecular components and dynamics that are dependent on cellular contexts. However, omics approaches based on genome-wide expression analysis data comparing one differing condition  did not investigate the dynamics and inter-pathway cross-communication that are dependent on cellular contexts. Therefore, we introduce a new computational omics approach for discovering signal transduction pathways regulated by transcription and transcriptional regulations between pathways in signaling networks that are dependent on cellular contexts, especially focusing on a transcription-mediated mechanism of inter-pathway cross-communication.Applied to dendritic cells treated with lipopolysaccharide, our analysis well depicted how dendritic cells respond to the treatment through transcriptional regulations between signal transduction pathways in dendritic cell maturation and T cell activation.Our new approach helps to understand the underlying biological phenomenon of expression data (e.g. complex diseases such as cancer) by providing a graphical network which shows transcriptional regulations between signal transduction pathways. The software programs are available upon request. Signal transduction is the primary process by which cells coordinate their metabolism, proliferation, and cellular communication according to environmental signals such as hormones, nutrients, and other chemical stimuli. Cells sense environmental signals by receptor proteins which convert the signals into various responses through signal transduction that are dependent on cellular contexts such as signals, receptor proteins that cells possess, and intracellular machinery by which cells integrate and interpret the signals . For exaAs well as for various responses stimulated by signal transduction pathways or signaling pathways, recent articles have presented abundant evidence for inter-pathway cross-communication according to cellular contexts -4. CytokAmong the two mechanisms of cross-communication between signaling pathways according to cellular contexts, we focus on the transcription-mediated mechanism that can be inferred by integrating omics data as well as genome-wide expression data. Various methods analyzing expression data by integrating omics data have been employed to infer sub-networks perturbed at cellular context with protein-protein interaction (PPI) data-10. IdekTherefore, we propose a new computational omics approach for discovering signaling pathways regulated by transcription, Transcription-Regulating Signaling Pathways (TRS Pathways) and transcriptional regulations between pathways in Transcription-Regulating Signaling Networks (TRS Networks) that are dependent on cellular contexts. In this approach, cellular contexts are restricted to the experimental condition of expression data. TRS Pathways are signaling pathways of which some proteins are regulated by transcription according to the context of expression data. Signaling pathways are chains of proteins relaying a signal from ligands or transmembrane proteins to transcription factors, or some proteins whose roles are clearly known such as caspase3 ,12. ProtOur system comprises three major steps: (1) constructing a human omics network from PPIs and Protein-DNA (PD) interactions; (2) identifying TRS Pathways by two strategies: three constraints to reduce the search space for TRS Pathways and designing a scoring function for TRS Pathways; (3) identifying the TRS Networks by a search algorithm. After explaining these three major steps of the system, we briefly explain the scoring function for TRS Networks, designed to compare with sub-networks inferred by previous methods, expression data analyses and kinase reaction annotations.A human omics network is a directed graph comprising Protein-Protein Interactions (PPIs) and Protein-DNA (PD) interactions. The interactions were collected from three types of data: PPI, PD, and KEGG database. Firstly, PPI data were from four public databases; BioGRID version 2.0.26 , IntAct Because finding the highest-scoring connected sub-networks like TRS Pathways in the entire interaction network is a NP-hard problem , we adopThe first strategy for identifying TRS Pathways is to reduce the search space of the entire network for TRS Pathways by three constraints. The three constraints are based on three previous approaches mining candidate signaling pathways from PPI data, given a pair of starting and ending proteins ,22,23. FSecondly, because signals are transmitted from an extracellular region into a nucleus, we defined the right orders of pathways as the following: from an extracellular region to a plasma membrane, from a plasma membrane to cytoplasm and from cytoplasm to a nucleus. When we search for TRS Pathways, we remove edges whose directions are opposite to the right orders such as from a plasma membrane to an extracellular region. Removing the opposite edges can lead to the loss of some feedback regulation processes between PPIs, but is an indispensible step to make the complex PPI data into a simple signaling pathway model to deal with. Moreover, since we can identify the transcriptional feedback regulation processes from the PD interaction data, the processes can compensate the loss defect. The sub-cellular localization data were from Locate database . Lastly,N and E represent a set of nodes and edges of a TRS Pathway respectively. The scoring function P is the sum of the two functions T(N) and R(E);The other strategy is to find the pathways only with the top scores by defining the scoring function for a TRS Pathway. Let N) measures how many nodes in a TRS Pathway are regulated by transcription. This measure is based on the method of Ideker et al. [Tstart and Zend represent the Z score values of the start and end nodes of a TRS Pathway. Z score Zi = Φ-1(1-pi) is converted from the p value pi of a gene i to a significance level, where Φ-1is the inverse normal cumulative density function. The pi value is acquired from the results of the expression data analysis.Zi over all |N| genes in a TRS Pathway to produce an aggregate Z score (ZN) for a TRS Pathway. Then, in order to properly capture the connection between expression and network topology, we investigate whether the score ZN of a TRS Pathway in Eq. (3) is higher than expected relative to a random set of genes. We randomly take 100,000 samples from all gene sets of size k using a Monte Carlo approach and calculate their scores ZN. The mean μk and standard deviation (SD) σk for each k are estimated and the noise in the estimates is reduced using a sliding window average. Using these estimates, the corrected score S(N) is calculated. The corrected score of random TRS Pathways is guaranteed to have a mean of μ = 0 and SD σ = 1.We sum the ZE) estimates how reliable PPIs in a TRS Pathway are. Estimating the PPI reliability R(E) of a TRS Pathway is absolutely necessary to indentify the signaling pathways consisting of PPIs [Rfficient . When in0,..., β4) to maximize the likelihood of the data. To optimize the parameters, we use the lrm function of the Design R package. We randomly select 5,000 PPIs from 12,363 determined by coimmunoprecipitation as our positive training data set [E) of a TRS Pathway, we take the same procedure of estimating the corrected expression score S(N) from the ZN scores of a TRS Pathway. For an edge of TRS Pathways, we did not use PPIs which reliability scores were less than 0.6 (about 85 quantile) .Given positive and negative training data sets, one can optimize the parameters  Initialize G(2) Add the end node of the initial TRS Pathway into Queue;(3) WHILE end nodes exist in Queue(4)  POLL an end node e,(5)   FOR i = 1 to n of outgoing edges of e;i of ei is a DEG  or exists in Gp, add ti and ei into Gp;(6)    IF a target node ti is a start node, a DEG, and positively regulated, add the top ranked TRS Pathways of ti into Gp and add the end node of the top ranked TRS Pathways into Queue;(7)    IF added ti is an end and DEG, add ti into Queue;(8)    IF added tGp and its score.(9) Output The above search algorithm represents the following; by the top ranked TRS Pathway, the signal is transmitted into the end nodes. The activated end nodes carry out their own roles in cells; transcription feedback regulation, enzyme activity or inducing the expression of new start proteins. The newly activated start proteins can send signals to other end nodes, thus we added the TRS Pathways activated by start proteins of a positively regulated DEG into the TRS Network. Since there are so many TRS Pathways at context, we have to select only the highly significant TRS Pathways for adding a TRS Network. The newly added end nodes also carry out their own roles in cells. Therefore, we repeat this process until there is no protein in the Queue.The scoring function for a TRS Network SFhere i is the score value (See Eq. (1)) of the ith TRS Pathway in a TRS Network. Ni and Ei also represent nodes and edges of ith TRS Pathway. N0 and E0 represent a set of nodes and a set of edges which do not belong to any TRS Pathway, respectively. They have a role to inter-connect TRS Pathways. To assess the statistical significance of TRS Networks, we take the same procedure in estimating the TRS Pathways by randomly permutating the expression of individual genes and performing the same search for TRS Networks 1,000 times.In order to calculate SWe investigated dendritic cells treated with lipopolysaccharide (LPS), one of the TLR agonists. TLRs are very important innate receptors that sense microbial products and trigger dendritic cell maturation and cytokine production, effectively bridging innate and adaptive immunity . We downSince phosphorylation is one of the most important and critical processes in the signal transduction pathway, we annotate TRS Networks with the relationships between kinases and their substrates ,34. We bN) (Eq. (3)) and the path score is our own path scoring function (Eq. (1)). Among all in the expression score, the path from TNF to MAPK12 (p38) was the highest-scoring and among all in the path score, the path from TNF to NFKB1 (p50) was the highest-scoring.To find which signaling paths are regulated by transcription in our known knowledge, we searched the KEGG pathways  for TRS TNF to NFKB1 found by our path scoring function is more helpful, though it is difficult to determine which is more significant than the other. Let us suppose that intermediate signaling proteins exist to some extent in a cell to quickly respond to an environmental signal. Then, when the mRNA expression level of the start node of a path increases, we expect that the signal starting from the start node would be transmitted to the end protein of the path. Likewise if the mRNA expression level of the end node of a path increases, we also expect that the biological process of the end node would occur. In the path from TNF to NFKB, both the mRNA expression levels of the start node TNF and the end node NFKB1 were increased. The signal starting from TNF would be transmitted to NFKB1 and NFKB1 would activate the mRNA expression levels of its target genes as a transcription factor. Therefore, we can identify more explicable paths by using our path scoring function, adding the expression scores of the start and end node of a path (Zstart and Zend of Eq. (2)) into the aggregated expression score, than by using just the aggregated score.From the viewpoint of understanding the underlying biological phenomena of cellular contexts, the path from TNF to NFKB1 was the highest-scoring when we searched the omics network with our path scoring function (See Eq. (1) and Methods). We compared it with the highest-scoring TRS Pathway of KEGG, since both pathways have the same start and end nodes  was unfortunately hardly regulated by transcription. Not only was the path score low, but so was its expression score that we could not identify this pathway as a TRS Pathway. It was the correct result because the first pathway was not a TRS Pathway but a relevant signaling pathway in this context. These results shows that our TRS Pathway analysis can search highly reliable TRS Pathway candidates regulated by transcription according to cellular contexts well, even though it cannot guarantee to find all the relevant signaling pathways to the contexts. In our analysis, we fortunately knew that We found that the TLR signaling pathway and Apoptosis are the most relevant and known pathways among all the KEGG pathways 2h after the LPS treatment, by Impact analysis . Impact TLR4 TRS Network and TNF TRS Network) were superior to those of other two networks: the D2D network and the jActiveModule network . To estimate the performance of our TRS Network analysis without knowing the start node, we searched for the two TRS Networks and compared their overlaps. In this context, TLR4 senses LPS as the start protein. If, however, we did not know the start protein, TNF, the start protein of the highest-scoring TRS Pathway would be selected as the start node of the search step. The TLR4 TRS Network was found by the actual start node and the TNF TRS Network was found by the inferred start node. The overall propensity of the overlaps of two networks looked very similar, though the performance by the actual start node was slightly better than by the inferred start node ), we ordered 70 KEGG signaling pathways and four sub-networks: the rk Table . We compTLR4 TRS Network . NFKB in the nucleus induces the transcription of genes that promote immune and inflammatory responses [TNF, one of the target genes increased by NFKB, has the possibility to activate NFKB as a positive feedback by sending a signal to NFKB through a TNF-NFKB pathway. A positive feedback is possible because the TLR4-NFKB pathway and the TNF-NFKB pathway have a common downstream activating NFKB.Firstly, NFKB has gotten a positive feedback by inducing B Figure . When thesponses . TNF, onTNF-NFKB pathway induces inflammatory responses by increasing the mRNA expression levels of inflammatory cytokines (e.g. IL6 and IL12B) . CD40 and CD80 are co-stimulatory molecules that bind to complementary receptors on the T cell surface, in activating a T cell [ICAM1 enables a T cell to remain bound to an antigen-presenting cell long enough for the T cell to become activated by binding lfa-1 on the T cell surface [CD83 and high levels of CD40, CD80, and CD86 [CXCL10, CXCL11, and CCL5 are chemoattractants that guide the migrations of leukocytes such as T cells to induce a suitable immune response [Thirdly, NFKB elevated the mRNA expression levels of necessary genes for T cell stimulation:  surface . Moreoveand CD86 . CXCL10,response -45.BIRC2 and BIRC3 suppressing caspases  [IL15, NFKB1) were also increased. This result shows that apoptosis would be repressed in dendritic cells and it agrees with this context of the dendritic cell maturation. If apoptosis occurred in matured dendritic cells, they could not perform their roles initiating adaptive immune responses as an antigen-presenting cell.Lastly, NFKB increases the mRNA expression levels of genes related to apoptosis, especially inhibitors of apoptosis (IAPs) such as gure S5) . At 2h aWe propose a new computational omics approach to discover signaling pathways regulated by transcription (TRS Pathways) and transcriptional regulations between them in TRS Networks dependent on cellular contexts to investigate the transcription-mediated mechanism of inter-pathway cross-communication of signaling pathways. Our new approach has three advantages. Firstly, highly reliable TRS Pathway candidates and transcriptional regulations between pathways can be discovered. Secondly, the approach can discover more known knowledge than the previous approaches. Lastly, it helps to understand the underlying biological phenomena of cellular contexts by providing a graphical network. We demonstrated that our analysis performed well in the context of dendritic cells treated with LPS, since dendritic cells commanding the human immune system are very important to disease research. Likewise, our approach determines how some signaling pathways are transcriptionally regulated by other pathways in patients by analyzing expression data comprising patients and normal samples. Therefore, it would be very helpful to understand the underlying biological phenomena of complex diseases such as cancer.SH, HS and DL conceived and designed the experiments. SH and SW analyzed the data. SH wrote the manuscript. DL directed this project and helped to draft the manuscript. All authors read and approved the final manuscript.Supplementary materials. It comprises 5 figures , 3 tables .Click here for file


Since then, a variety of new data sources and improvements in functionality have contributed to the growth and utility of the resource. In particular, more comprehensive tracking of compounds from research stages through clinical development to market is provided through the inclusion of data from United States Adopted Name applications; a new richer data model for representing drug targets has been developed; and a number of methods have been put in place to allow users to more easily identify reliable data. Finally, access to ChEMBL is now available via a new Resource Description Framework format, in addition to the web-based interface, data downloads and web services.ChEMBL is an open large-scale bioactivity database ( Details of the data extraction process, curation and data model have been published previously , the biological or physicochemical assays performed on these and the targets of these assays are recorded in a structured form, allowing users to address a broad range of drug discovery questions. Applications of the data include the identification of suitable chemical tools for a target; investigation of the selectivity and off-targets effects of drugs; large-scale data mining, such as the construction of predictive models for targets and identification of bioisostere replacements or activity cliffs ; and as eviously ; therefohttp://www.dndi.org), World Health Organization TDR programme (WHO-TDR) , Harvard University (https://ntp.niehs.nih.gov/drugmatrix/index.html), TP-search . In total, there are now >1.3 million distinct compound structures and 12 million bioactivity data points. The data are mapped to >9000 targets, of which 2827 are human protein targets. Data sets added over the past 2 years include the following: neglected disease screening results from projects funded by Medicines for Malaria Venture , Drugs fiversity  and Glaxiversity ; kinase iversity , and seviversity ; supplemiversity ; and infP-search  and OpenP-search .http://www.ama-assn.org/ama/pub/physician-resources/medical-science/united-states-adopted-names-council/adopted-names.page) and the USP dictionary of USAN and International Drug Names , we have now added structures and annotation for >10 000 compounds and biotherapeutics for which United States Adopted Name (USAN) or International Nonproprietary Name (INN) applications have been filed. This information has been obtained from the public list of adopted names provided by the USANs Council  code, where available. The ATC classification is assigned by the WHO Collaborating Center for Drug Statistics Methodology  and can The annotation of the compounds in preclinical research and development serves as a platform for the annotation of the FDA-approved drugs. It is likely that, when a novel drug is finally marketed, much the information regarding the active molecule can already be found in ChEMBL. For each launched drug, the ingredient-derived data are then complemented with information regarding its marketed product. This includes information regarding its trade names, dosage information, approval dates, administration routes, whether there are ‘black box’ safety warnings associated with the product, whether it has a therapeutic application  and finally whether the product is available on prescription, over-the-counter or, if eventually, it has been discontinued. This information allows users of the bioactivity data to assess whether a compound of interest is an approved drug and is, therefore, likely to have an advantageous safety/pharmacokinetic profile or be orally bioavailable, for example.in vivo efficacy (though these can obviously be queried from the bioactivity data in ChEMBL), or those that may be relevant to other indications for which the drug is not currently approved.Finally, FDA-approved and WHO anti-malarial drugs have now been annotated with mechanism of action and efficacy target information. This information has been manually assigned, using primary sources such as published literature and manufacturer’s prescribing information. Targets have only been included for a drug if (i) the drug is believed to interact directly with the target; and (ii) there is evidence that this interaction contributes toward the efficacy of that drug in the indication(s) for which it is approved. We do not currently list as drug targets proteins that are responsible for the pharmacokinetics or adverse effects of a drug, those for which there is pharmacology data but no known link to The appropriate representation of drug targets in bioactivity databases is a non-trivial issue. In certain circumstances it may be sufficient to consider a ‘target’ and a ‘protein’ as synonymous. However, there are numerous cases where this oversimplification breaks down. A large number of marketed drugs, for example, bind to protein complexes or bind non-selectively to all members of a protein family. Similarly, for published assay data, if a measurement has been performed in a cell-based assay it is often not clear which of several related proteins are responsible for the effect observed. Formerly assays in ChEMBL that described the interaction of a compound with multiple possible proteins were mapped to several targets. However, this representation was suboptimal for a number of reasons. Firstly, users might have retrieved multiple rows of data for a compound of interest and erroneously believed that it has been tested in multiple different assays against each of the individual targets. In addition, a user querying the database with a non–compound-binding subunit of a protein complex would still retrieve activity data and, again, might incorrectly infer that compounds identified bind directly to that subunit. A new target data model has, therefore, been developed within ChEMBL to draw a clear distinction between targets (the entities with which a compound interacts to exert its effects) and the molecular components  that make up those targets.α, β and γ subunits . In a tissue-based format, the exact subunit combinations present are generally not known. In such cases, the target type of ‘PROTEIN COMPLEX GROUP’ is used. Other new target types have also been created for approved drugs whose molecular targets are not proteins . A key step in the development of the new data model was the definition of new target types. The original ‘PROTEIN’ target type has now been subdivided into a number of categories. In the simple case where a compound is believed to interact specifically with a monomeric protein, the target type ‘SINGLE PROTEIN’ is now used. In cases where either a compound is known to act non-specifically with all members of a protein family, or the assay conditions are such that it is not possible to determine which member(s) of a protein family the compound is acting on (e.g. a cell-based or tissue-based assay), a target type of ‘PROTEIN FAMILY’ is used. The target represents, and is linked to, the group of all proteins (components) with which the compound may interact. Where the molecular entity with which the compound interacts is known to be a protein complex, and can be precisely defined, the target type ‘PROTEIN COMPLEX’ is used. Again, the target represents the complex itself, but is linked to components representing each of the protein subunits. However, it is not always possible to define protein complex targets precisely. For example, compounds are often measured for activity at gamma-aminobutyric acid (GABA-A) receptors in tissue-based assays. Although GABA-A receptors are known to be pentameric ligand-gated ion channels, they can consist of various combinations of Supplementary Figure S1.The new data model also allows the annotation of binding site information on protein targets. Binding sites can be defined at varying levels of granularity , according to the information available. This facility has been used to annotate bioactivity results with the predicted Pfam domain to which the compound is most likely to bind  and to aAs the volume of data in ChEMBL grows, it becomes increasingly important to empower users with the ability to evaluate the quality and appropriateness of these data for their particular use cases. For example, while a researcher investigating a single target or compound series may prefer to retrieve as much data as possible, and validate this information by referring back to the original publications, for other applications, such as the training of computational models, it may be vital to exclude any data that could potentially be erroneous. Therefore, a number of different enhancements have been made to the database to allow users to more easily assess the drug-likeness of compounds, compare bioactivity values from different assays and highlight possible errors or duplications in the data.To help users assess the drug-likeness of compounds, a set of physicochemical properties is provided for the ChEMBL compounds. The calculations are made on the parent form of the molecule, after any salts have been removed, and where the molecular weight of the compound is <1000 and the structure is comprised only of standard common atoms . The exception is the full molecular weight (FULL_MWT), which, where applicable, is the molecular weight of the salt plus any hydrates present. The properties are calculated either using algorithms provided in Pipeline Pilot  or using the ACDlabs Physchem software . Some further descriptors have been derived from these properties such as the well-used Lipinski Rule-of-Five ; the RulLigand efficiencies are also increasingly used to identify not just compounds that have high affinity for a target but those that give maximum binding for their size, number of atoms or lipophilicity/polar atoms. There are a number of different measures now described in the literature and four of the more common have now been made available in the database: Ligand Efficiency (LE) , Binding−4 mol/l and so forth have been standardized to nM. Additionally a number of activities are reported in articles as the -log values , we have anti-logged these values so that all of the IC50 values  are seen as IC50, and hence all similar activity types can be readily identified and their values more easily compared.When identifying compounds that bind to a particular protein target for structure–activity relationship or lead identification studies, it is important to be using comparable data. We have, therefore, standardized many of the activity types and their corresponding units. For example, IC50, IC50_mean, IC50_µM and mean IC50 have all been given the standard activity type of IC50, and units of µM, mM, nmol/l and 1010 .In addition to the conversion of published activity types/values/units to standard activity types/values/units, an additional field called pCHEMBL_VALUE has been added to the activities table. This value allows a number of roughly comparable measures of half-maximal response concentration/potency/affinity to be compared on a negative logarithmic scale . The pChEMBL value is currently defined as follows: −log9 nM) and ‘Non standard unit for type’ where the unit is inappropriate for the activity type (e.g. an IC50 with units of % or µg). A table is available in the database (ACTIVITY_STDS_LOOKUP) that contains details of the activity types that have been standardized, their permitted standardized units and their acceptable value ranges.Having standardized the activity types, units and values, it is also then possible to identify data that are potentially erroneous and require further checking with reference to the original article. These data are flagged in the DATA_VALIDITY_COMMENT column of the activities table, the values of which should be self-explanatory. The key ones are ‘Outside typical range’ where the value is what would normally be considered too high or low a value for the activity type . For users wishing to search for compounds by structure, rather than name, the ‘Ligand Search’ tab provides a simple sketcher, allowing users to draw a structure or substructure of interest (or import a molfile) and retrieve related compounds  or document clustering based on a term vector approach .A new feature of the Document Report Card is a section that lists other publications in ChEMBL that are deemed similar to the one featured in the Report Card. Several methods may be used to assess pairwise document similarity, e.g. overlap of Medical Subject Headings (MeSH) terms  or retrieve data programmatically via web services.ftp://ftp.ebi.ac.uk/pub/databases/chembl/). The RDF model follows fairly closely the relational model and uses a basic internal ontology known as the ChEMBL Core Ontology to describe the core concepts and relationships between them. In addition, multiple external ontologies are used including BioAssay Ontology (http://qudt.org) and Chemical Information Ontology (CHEMINF) (http://www.ebi.ac.uk/fgpt/sw/lodestar/) are also provided, allowing users to query and navigate the data: https://www.ebi.ac.uk/rdf/services/chembl/sparql.The semantic web is becoming an increasingly popular platform for large-scale data integration, with many now choosing to use triple stores and storing data in Resource Description Framework (RDF) format, in preference to building traditional data warehouses. In response to demand from our users, an RDF version of ChEMBL has now been developed and is available for download from our FTP site  . A SPARQhttp://creativecommons.org/licenses/by-sa/3.0). Each release of ChEMBL is also freely available from our FTP site in a variety of other formats including Oracle, MySQL, PostGRES, a structure-data file (SDF) of compound structures and a FASTA format file of the target sequences, under a Creative Commons Attribution-ShareAlike 3.0 Unported license (https://www.ebi.ac.uk/chembl/ws for more details). Finally, a set of Representational State Transfer (REST) based web services is also provided , to allow programmatic retrieval of ChEMBL data in extensible mark-up language or JavaScript Object Notation (JSON) formats (see Supplementary Data are available at NAR online.Strategic Award for Chemogenomics from the Wellcome Trust [WT086151/Z/08/Z]; European Molecular Biology Laboratory; the Innovative Medicines Initiative Joint Undertaking ; Medicines for Malaria Venture. Funding for open access charge: European Molecular Biology Laboratory.Conflict of interest statement. None declared.


Pathways provide topical descriptions of cellular circuitry. Comparing analogous pathways reveals intricate insights into individual functional differences among species. While previous works in the field performed genomic comparisons and evolutionary studies that were based on specific genes or proteins, whole genomic sequence, or even single pathways, none of them described a genomic system level comparative analysis of metabolic pathways. In order to properly implement such an analysis one should overcome two specific challenges: how to combine the effect of many pathways under a unified framework and how to appropriately analyze co-evolution of pathways.Here we present a computational approach for solving these two challenges. First, we describe a comprehensive, scalable, information theory based computational pipeline that calculates pathway alignment information and then compiles it in a novel manner that allows further analysis. This approach can be used for building phylogenies and for pointing out specific differences that can then be analyzed in depth. Second, we describe a new approach for comparing the evolution of metabolic pathways. This approach can be used for detecting co-evolutionary relationships between metabolic pathways.We demonstrate the advantages of our approach by applying our pipeline to data from the MetaCyc repository . Our analysis revealed several surprising biological observations. For example, we show that the different habitats in which Archaea organisms reside are reflected by a pathway based phylogeny. In addition, we discover two striking clusters of metabolic pathways, each cluster includes pathways that have very similar evolution.We demonstrate that distance measures that are based on the topology and the content of metabolic networks are useful for studying evolution and co-evolution. The score can be used as yet another type of data with which to build dissimilarity-based phylogenies, but - in addition - the alignment details allow in-depth analysis of the evolutionary changes that various pathways underwent. Thus, our method allows a systematic examination of evolutionary relationships among species using their functional traits, based on a description of their entire metabolic processes rather than an individual element as in conventional analysis. In addition, such studies allow us both to further improve pathway alignment techniques by finding more faithful similarity scores, as well as to study co-evolution of metabolic pathways.The increasing availability of pathway information enables the comparative analyses of organisms at the functional level. As the basis for such studies we propose to use Specifically, there is currently a significant amount of metabolic pathways data residing in public and proprietary databases such as KEGG , MetaCyci.e. the similarity score that is obtained when comparing this pathway in each pair of organisms is regarded as the similarity between them), and then build a phylogenetic tree that reflects the evolutionary history based on this pathway alone. One pathway, however, does not tell the whole story. The first algorithm that we propose here looks at all the known pathways in a set of organisms and combines their similarity scores in a systematic fashion to produce a more comprehensive picture; an entropy-like weight determines the information contents of each individual pathway when computing the combined similarity score between organisms.A single analogous pathway can be used as the basis for generating a similarity matrix between a set of species  array, A, where each page is indexed by the corresponding pathway , the sizes of the pages might not be commensurate with each other; to accommodate for this phenomenon and to obtain a regular n × n × m 3D array we stretch the pages to be the same size, denoting missing entries appropriately (see more on this below).Each such 2 dimensional array is called a e Figure , and in y Figure ; now Aijm entries in each column into one scalar entry; this matrix would serve us as a distance matrix in order to build a phylogenetic tree using known algorithms. The first step in the process of combining a column's entries is setting a threshold that describes scores as relevant: all similarity scores below this threshold  will not contribute to the values in the similarity matrix, and all similarity scores above it will contribute to them equally. Using this method - on one hand - causes some loss of information, but - on the other hand - enables combining the data in an effective manner while also handling the issue of missing entries that will be discussed in more detail later in this section. For each page we create a Threshold Graph (TG) in the following manner: define a node v ∈ V for each organism; two such nodes are connected by a directed edge e ∈ E if their similarity score is higher than the given threshold. Using (1) we calculate w, an assessed weight for each TG:Analyzing these data in order to obtain insights about the evolutionary history of the organisms was performed as follows: We collapse the 3D array into a 2-dimensional (dis)similarity matrix by combining the w = 0 if the degrees of all nodes in the graph are 0, since the This weight, inspired by  Shannon's entropy , reflectAijk by 0 if nodes i and j are not joined by an edge in the TG corresponding to pathway k, and by the calculated weight w if an edge exists between organisms i and j in the TG. In fact, each page is now the adjacency matrix of the weighted TG.Alternatively, w = 0 if all node degrees are |V|, in which case the b: If b is true then each such two organisms will be joined by an edge, otherwise such edges will not be formed. Intuitively, if the confidence in the data is high then b should be assigned true, since in this case the absence of a pathway in both organisms tells us that they are similar in their lacking. If our confidence in the data is low, b should be assigned false since in this case the pathway could be missing due to lack of data, and this might not indicate a real (dis)similarity.This technique also solves the problem of missing entries, as follows: for the cases where a pathway exists in one of the organisms but does not in the other, we decided never to connect the two organisms with an edge in the TG, and as a result the corresponding entry in the array will always be 0. To accommodate the cases where the pathway is missing in both organisms, we added a new global, Boolean parameter Naturally, using a threshold in forming the TG resulted in loss of information. However, we found that the advantage of being able to include missing entries is far more significant to our results than the information lost by not using the actual alignment score. Furthermore, by adjusting the threshold value, we can make sure that our results are as precise as possible. After dealing with this issue we can safely add all the similarity scores in each vector along the pathway axis and thus create a 2-dimensional similarity matrix.e.g. the neighbor-joining algorithm [e.g. the Phylip [The resulting 2D matrix is turned into a dissimilarity matrix so that it can be used as input to an algorithm for building phylogenetic trees, lgorithm , as emboe Phylip  package,Finally, it is possible to identify the contribution of specific pathways to the resulting phylogeny: One can trace differences among phylogenetic trees back to the pathways that cause them in order to understand their biological reason; furthermore, the alignments produced by the MPH algorithm can be used to further elucidate these differences. Moreover, we can study the pathways whose TGs got the highest weights to see if they induce an interesting classification of the organisms into groups.The pseudo-code for the computational pipeline is given here:Input:   n: number of organismsm: number of pathwaysij is the instance of pathway j in organism iP: matrix n*m. Entry Pd: deletion score for MPHt: thresholdb = true) or under  the thresholdb: Boolean parameter to determine whether a pathway missing in two organisms is considered over ;         else Aijk := MPH;         elsif not exists Pik and not exists Pjk then Aijk := both missing;      else Aijk := one missing;         build graph TG s. t.      V := {1..n};ijk ≥ t}; //only numeric values      E := { | Aif b then E := E ∪ { | Aijk = both missing};         for i, j := 1..n do   if  ∈ E then Aijk := w);      else Aijk := 0;      for i, j := 1..n doij :=    SimMat11*1 - SimMat;DisSimMat := SimMattree := NeighbourJoining(DisSimMat);O(mn2), which is dominated by the time needed to read the entries of the input array, assuming that m >>n.The time complexity of this algorithm is i.e., it contains one variation of each pathway for all the organisms for which it is known. Initially we used over 660 metabolic pathways for the 205 organisms that were retrieved from this database . This presented a significant computational challenge, namely making over 26 million individual pathway alignments. Fortunately, this problem is "embarrassingly parallel", i.e. each alignment can be run separately, and thus we were able to solve it using a computational Grid facility.Our algorithm was applied to data that was obtained from the MetaCyc repository . We chosi.e. a page, see Figure H. This can be further explained as follows: Let d1 denote the value of an entry in the distance matrix corresponding to a case where a pathway does not appear in the two organisms; let d2 denote the value of an entry in the distance matrix corresponding to a case where a pathway appears in the two organisms; let d3 denote the value of an entry in the distance matrix corresponding to a case where a pathway appears in the first organism but not in the second one; and let d4 denote the value of an entry in the distance matrix corresponding to a case where a pathway appears in the second organism but not in the first one. Let D denote the contribution to the generalized Hamming distance due to value xin a certain entry in the page of one pathway and y in the same entry in the page of the second pathway. We used ∀xD = 0;For comparing the evolution of different metabolic pathways the following steps were performed. First, a distance matrix  = D = 2, D = D = D = D = 1, and D = D = D = D = 1.The final score was normalized by dividing it by the number of entries in a page.L, considers only entries where both pathways appear in both organisms and it is the L1 distance between the vectors that are composed of these entries in the two organisms. The final distance is a weighted average of the two distances, computed as in (2). As the first component of the score reflected a rougher distance measure we used Wp = 100; however, the result was robust to small changes in Wp.The second component, Based on this measure we generated a distance matrix between pathways that can be further analyzed as described in the next section .i.e. to perform more than 26 million individual pathway alignments in just a few days.The version of MetaCyc that was used in this work includes 660 pathways and 205 organisms. Using a computational grid comprising 250 nodes we were able to analyze the entire dataset, Evolutionary trees based on the entire dataset were very noisy. The main reason for this result is the fact that the data in most of the pathways/organisms are very partial. For example, the mean size (number of enzymes) of 200 of the pathways over the organisms that appear in the dataset is less than 3. On the other hand, the size of the pathway with the top mean size is 53. Thus, as specific biological examples (as described in the next subsections) we chose less biased subsets of organisms and pathways.As a specific biological example we performed a deep biological analysis of all the Archaea that appear in MetaCyc based on their metabolic pathways. We chose this dataset for two main reasons. First, this dataset is less biased since the size of the metabolic network of all Archaea in MetaCyc is relatively similar . Second, as the Archaea live in extreme and diverse environments it is interesting to compare the changes in their metabolic networks to their phylogeny.i.e. the resulting tree is well separated into Methanogens , Halophiles, and Thermophiles and Archaeoglobus fulgidus is farthest from the rest of the organisms.A phylogenetic tree that was produced by running the algorithm is displayed in Figure A. fulgidus also falls close to the Methanogens. This can be explained by the fact that A. fulgidus represents an evolutionary transitional type of organism among the Archaea: it is a hyperthermophile and yet is known to be related to Methanogens [A. fulgidus contains acetyl-CoA decarbonylase/synthase (ACDS), a multienzyme complex catalyzing the reversible cleavage and synthesis of acetyl-CoA in methanogens [A. fulgidus is an example of an organism that contains a functional ACDS complex and is not a methanogen. Furthermore, Thermophilic Archaea are clustered together (with representatives from both Thermophilic Crenarchaea and Thermophilic Euryarchaea). This implies the existence of unique pathways that are found only in thermophiles. Anaerobic respiration, TCA cycle, and tryptophan biosynthesis pathways in MetaCyc were found to include specific variations unique to thermophiles while chorismate biosynthesis, formaldehyde assimilation, threonine biosynthesis, and valine biosynthesis pathways were missing from thermophiles in the MetaCyc dataset. For example, in the chorismate pathway it is known that many Archaea do have a distinct shikimate kinase [Archaea [The differences between these two trees are interesting and usually can be explained: The Hyperthermophilic hanogens . For exahanogens . A. fulge kinase . In addi[Archaea .Methanosarcina mazei with Halobacteriaceae in the tree that is based on metabolism. This result can be explained by the fact that Methanosarcina mazei is a freshwater organism that can adapt to grow at elevated salinities [Halobacteriaceae that are found in water saturated or nearly saturated with salt.Another interesting discrepancy between the two trees is the clustering of linities . Thus, iThermophilic Crenarchaea and the Thermophilic Euryarchaea in the thermophilic cluster. Specifically, why are the Thermophilic Crenarchaea divided in the tree shown in Figure Pyrococcus horikoshii linked to them whereas the crenarchaeote Sofolobus solfataricus is grouped with the euryarchaeotes Pyrococcus furiosus, Thermoplasma acidophilum, and Thermoplasma volcanium? This may be either a result of a curation error in the MetaCyc database or it may suggest that the metabolism of Thermophilic Crenarchaea and the Thermophilic Euryarchaea is not significantly different. We encourage an additional future research on this issue.Still, there are some unresolved discrepancies between the two trees. One unresolved issue is the intermix of the We examined our approach for detecting co-evolution of pathways on a subset of the available pathways that are known to be shared by many organisms such as amino acids biosynthesis and degradation, glycolysis, TCA cycle, and more , different trees would be created. These trees could then be compared to reference trees using e.g. the Robinson-Foulds measure, and that way to determine the best values for the parameters by picking those yielding the best match. Furthermore, such a process could enable comparative studies among the pathway databases themselves; currently, they are incomparable in several ways , making such a study premature.Naturally, the study presented in this work can be generalized in various ways. First, when the data in the various pathway databases will be more reliable and robust, we shall be able to use our tool to construct a complete phylogenetic tree based on all the pathways and organisms in such a database .Finally, we described here a new method for studying co-evolution of metabolic pathways. Our approach considers both the set of enzymes in a pathway and the topology of the pathway. In this work, we demonstrated this method by analyzing the evolution of 77 reliable metabolic pathways over 205 organisms. When reliable data about additional metabolic pathways will be available it will be interesting to use our approach for analyzing a larger set of metabolic pathways. It will be interesting to use such an analysis for comparing the evolution of entire pathways to the evolution of single genes (enzymes) that are related to the pathways. Additionally, a similar approach can be used for analyzing other cellular biological sub-networks (The authors declare that they have no competing interests.AM, RYP, OB and TT participated in the design of the study. AM and TT analyzed the data. AM, RYP, OB and TT wrote the paper. All the authors approved the final manuscript.


H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial.The metabolic network of 2Cards) to provide concise overviews of what the databases do or do not agree on. Each card is centered at a single gene, EC number or reaction. These three complementary perspectives make it possible to distinguish disagreements on the underlying biology of a metabolic process from differences that can be explained by different decisions on how and in what detail to represent knowledge. As a proof-of-concept, we implemented C2CardsHuman, as a web application http://www.molgenis.org/c2cards, covering five human pathway databases.We introduce the concept of Consensus and Conflict Cards (C2Cards can contribute to ongoing reconciliation efforts by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute. Several case studies illustrate the potential of the C2Cards in identifying disagreements on the underlying biology of a metabolic process. The overviews may also point out controversial biological knowledge that should be subject of further research. Finally, the examples provided emphasize the importance of manual curation and the need for a broad community involvement.C In the past decade the number of pathway databases has grown markedly, providing extensive descriptions of the metabolic network for an increasing number of organisms [S. cerevisiae and H. sapiens, are even described in multiple databases. A comparison of two yeast networks showed, however, that the two agreed on only 36% of their reactions [et al.[Metabolic pathway databases have proven very valuable for a wide range of applications, varying from the analysis of high-throughput data to rganisms ,2. The meactions . Given teactions . The coms [et al. also revThe advantage of having several descriptions of the metabolic network for the same organism is that they offer different views on the same biological system and thus can reveal controversial biological knowledge. In addition, the databases each have a particular focus and its curators have specific fields of expertise. Therefore, each database may provide complementary pieces of the puzzle of the complete metabolic network. These observations have motivated, still ongoing, efforts to consolidate the different networks for the same organism and to build consensus metabolic networks using a largely manual approach ,6,7.i.e., per pathway. Fourthly, the larger the number of pathway databases considered, the more difficult it is to identify the consensus and the conflicts. Recently, algorithms have been proposed to semi-automatically merge two descriptions of the metabolic network of the same organism [Combining all the knowledge on the metabolic network contained in the various pathway databases and identifying conflicting information is, however, far from trivial. Retrieving all required information from multiple databases is in itself already a cumbersome task. One reason that makes it challenging to identify instances where pathway databases do not agree on the underlying biology of a metabolic process are the different decisions made by each of the databases on how to represent knowledge ,8. For eorganism ,10. Thesorganism . The merAltogether, the issues described above make the construction of a single, more accurate, and more complete network based on the pathway databases available a laborious and largely manual process . Moreove2Cards). C2Cards combine the knowledge from multiple pathway databases for a specific target organism. A C2Card can be centered at a single gene, Enzyme Commission (EC) number or reaction of interest and gives a concise overview of what the databases do or do not agree on with respect to the entity the C2Card is centered at. These three perspectives offer complementary views on the knowledge contained in the pathway databases. Importantly, using these perspectives disagreements caused by a different decision on how and in how much detail to represent knowledge can be identified. C2Cards can be used to assist reconciliation efforts and make users of pathway databases more aware of the exact differences that currently exist between databases.To more easily visualize the opinion of multiple pathway databases, we introduce the concept of Consensus and Conflict Cards (C2CardsHuman (http://www.molgenis.org/c2cards), which combines the knowledge of the following five frequently used human pathway databases: the Biochemically, Genetically and Genomically structured (BiGG) knowledgebase [H. sapiens Recon 1 [2Cards, the combined strength of the three perspectives, and how C2Cards can aid in the curation of gene and metabolite identifiers. Next, we describe several case studies illustrating the potential of the C2Cards in identifying conflicts between pathway databases. Finally, we discuss the next steps to be taken in curating metabolic networks.As a proof-of-concept, we implemented Cedgebase  (H. sapi Recon 1 ), the Ed Recon 1 , HumanCy Recon 1 , and the Recon 1  and Reac Recon 1 . Below, 2Card provides an overview of the knowledge of multiple pathway databases from the perspective of a specific gene, EC number or reaction of interest. A C2Card answers the basic question of which databases contain the entity of interest. Importantly, each card provides a concise overview of what the databases do and do not agree on with respect to the entity of interest. The core component of a C2Card is a table in which each row contains the following basic elements: a reaction and the EC number(s), gene(s) and pathway linked to it in one of the pathway databases  on the knowledge contained in the pathway databases. Each perspective can answer various types of questions, accommodating the different interests one may have. Importantly, the three perspectives can be used to identify and complement information missing in one (or more) of the pathway databases using the knowledge from the other pathway databases.CThe ’gene perspective’ shows for each of the pathway databases, which metabolic functions the product of a gene has, as indicated by the reaction(s) and EC number(s) linked to it. This perspective may also answer the question whether other genes, either encoding isozymes or components of the same complex, are linked to the same reaction.2Card centered at the EC number 1.1.1.35 (3-hydroxyacyl-CoA dehydrogenase) provides an example of this scenario agree for a specific type of conversion. It may also reveal possible alternative substrates, which is one of the sources of conflict between metabolic pathway databases . The C2CThe ‘reaction perspective’ provides a compact overview of which gene(s) and EC number(s) are linked to a reaction of interest in each pathway database. This perspective can assist in resolving a commonly occurring gap in reconstructions of the metabolic network, namely cases in which the gene product catalyzing a known metabolic reaction is missing . The rea2Card one can easily switch perspective. Furthermore, each C2Card is opened in a new window to enable a simultaneous view of the C2Cards of a linked triple of a reaction, EC number, and gene from different viewpoints. Using all three perspectives is essential to get a complete picture of what the databases do or do not agree on. The EC number perspective can, for example, neither fully replace the gene perspective nor the reaction perspective, as illustrated by the example in Figure 2Cards application one can also cast a wider net when querying for an EC number by allowing a mismatch on the fourth number of an EC number. In contrast to the first three numbers, the last number does not indicate a specific subclass of enzymes and only serves to distinguish enzymes with different substrate specificities.By clicking on any of the entities shown in a Ce.g., α-D-glucose or β-D-glucose, while in other databases the more general form is used, D-glucose in this case. A possible motivation for database curators to choose the general version is that in an experiment the distinction between two isomers may be difficult to make. A second example is that one database may choose to describe a biochemical conversion in a single reaction using generic metabolites, like ‘a long chain alcohol’, versus multiple reactions with more specific examples of metabolites, i.e., ‘hexadecanol’ and ‘octadecanol’ instead of ‘a long chain alcohol’, in another database. The gene or EC number perspective can be used to uncover such a difference. A third example is that the number of steps used to describe a biochemical process may differ, which will prevent a perfect match on reaction level as well. Note, however, that this difference in level of detail may not always be a conceptual difference, but could also be due to a disagreement on the underlying biology. This commonly occurring difference in the number of intermediate steps can be revealed via the gene or EC number perspective as well  assigned to the genes and metabolites by the pathway databases. Identifiers are essential for the unambiguous identification of genes and metabolites across multiple resources and enable linking experimental data to the metabolic network. For each gene and metabolite a C2Card provides the identifiers assigned to them by the pathway databases  and Recon 1 that has chosen ammonium. The gene and EC number perspectives offer a possible way to uncover such differences. The C2Cards application provides an additional means to uncover reactions that are similar, but not an exact match, by allowing the user to specify that one or more mismatches are allowed when querying for a reaction. An example of the results of a query in which one mismatch was allowed is given in Table The ability to correctly match metabolites when comparing reactions is influenced by the different decisions the curators of the pathway databases have taken. For example, in Recon 1 and HumanCyc the protonation state of a metabolite is determined at a pH level of 7.2 and 7.3, respectively. The other three databases always use the neutral form of a metabolite. As illustrated in the C2Cards can be accessed using common JavaScript-enabled browsers on all major platforms including Windows, Linux, and Apple. A C2Card centered at a gene or EC number of interest can be retrieved in a single step. For the reaction perspective two routes are offered, either of which requires three steps. A reaction can be found by entering one or more metabolites or by selecting the pathway it is part of in one of the pathway databases. More detail on how to retrieve a C2Card is described on the C2Cards website (http://www.molgenis.org/c2cards). Once retrieved, a C2Card can also be downloaded for off-line use. In addition, for each database the C2Cards for all its genes, EC numbers, and reactions, respectively, can be downloaded in tab-delimited format in a single ZIP file.C2Cards. One possible application would be to perform computational analyses on each of the pathway databases. A typical example is an enrichment test to prioritize pathways most likely to be affected in a given high-throughput experiment. The differences between pathway databases can be quite large both with respect to content and conceptual differences [Next to the web interface, programming interfaces to R, SOAP (Simple Object Access Protocol), and REST  are provided to enable programmatic querying of the collection of Cferences . For exaferences . It wouli.e., it is either only produced or consumed, are explicitly indicated. The list of reactions provided allows the user to find candidate reactions to resolve these dead-ends in the network of a particular database using information from other databases. All reactions in this list are linked to their corresponding C2Card.Another additional feature offered is the possibility to look up the fate of a metabolite, contained in any of the five databases, by retrieving the list of reactions in which the metabolite of interest participates. Furthermore, databases in which the metabolite is a ‘dead-end’, 2CardsHuman of consensus and conflicts between the five human pathway databases below. The examples have all been chosen from primary metabolic processes, highlighting that conflicts still occur even in well-studied parts of the metabolic network. Moreover, we focused on examples of differences between databases that are not easily resolved and could point either to conflicting information or to complementary information. The case studies illustrate why manual curation remains crucial to resolve contradicting information and to determine in which cases further biochemical experiments are even required to verify what is correct and what is not.For each of the three perspectives we provide a concrete example derived from C2Card focused on the CTPS gene .Reactome and HumanCyc only link the gene to the glutamine dependent reaction and Recon 1 only to the ammonium dependent reaction. The CCTPS and CTPS2 gene contain a glutamine amidotransferase domain and have high sequence similarity. This and the fact that both gene products have the same EC number suggests that they have similar catalytic activity. For L. lactis it is known that both ammonium derived from the hydrolysis of glutamine by the CTP synthase enzymes themselves and ammonium from other external sources of amine donors can be utilized for CTP synthesis [L. lactis. This is supported by the fact that under room temperature glutamine is unstable and will dissociate into an ammonium ion and oxo-proline. One could, therefore, hypothesize that CTPS and CTSP2 should be linked to both reactions for H. sapiens as well. The glutamine and ammonium dependent activity of CTPS2 have indeed recently been shown in human embryonic kidney cells [CTPS and the ammonium dependent reaction to CTPS2. In Reactome and HumanCyc the ammonium dependent reaction then needs to be added to both genes. In this case study a possible source of confusion for database curators might have been the description given by NC-IUBMB for the EC number (EC:6.3.4.2) assigned to the two gene products. The reaction linked to the EC number isThe products of both the ynthesis . The humey cells . This me3”. This might explain the inconsistencies at the reaction level to some extent.and in the comments field it is stated that “Glutamine can replace NHi.e., IDP/ITP instead of GDP/GTP. Although IDP is a substrate for this enzyme in vitro, it is extremely unlikely to play a role in vivo. The concentrations of IDP and ITP are very low as compared to other nucleotides due to the activity of ITPase. Even if there is a mutation in the ITPA gene, the residual activity of ITPase is still considerable and the IDP/ITP concentrations remain undetectable [H. sapiens.The EC number 6.2.1.4 (succinate-CoA ligase (GDP-forming)) is found in all five databases. They all agree on one reaction and two genes linked to it  from differences that may be explained by, for example, different decisions taken on how to represent knowledge  of the databases and cases where databases do not agree due to lack of consensus in the scientific literature. Moreover, for any given organism metabolic pathway databases are still being refined, expanded, and corrected. This makes it challenging to distinguish complementary information from cases in which the database curators purposely excluded, for example, a reaction or gene. Even the parts the pathway databases agree on may need to be reviewed as the databases share information sources and may copy data from each other, thereby possibly propagating incorrect information. Manual curation is also needed to unambiguously assign identifiers to genes and metabolites.Ultimately, to reconcile differences and to integrate the networks manual curation is required. While a C2Cards offer an elegant solution to bring cases that deserve further inspection to the attention of pathway database curators. The overviews may also point out controversial biological knowledge that should be subject of further research.In summary, Ce.g., increase our knowledge about pathways perturbed by a disease, find new drug targets, and interpret the deluge of high-throughput data. A crucial step towards a more complete description is to combine the knowledge captured by each of the available pathway databases for a specific organism. Much time and effort has already been put into pathway databases and we should profit from this to the fullest extent. However, it requires the commitment and the support of a broad community to construct an initial consensus network and to extend it with new knowledge from domain experts, the scientific literature, and as captured by the various pathway databases. C2Cards can contribute to such an endeavor in several ways. As illustrated by the three case studies the C2Cards are a perfect starting point for further manual curation of the human metabolic network in future reconstruction jamborees [2CardsHuman with additional pathway databases. Importantly, the C2Cards application can be set up for other organisms as well (see http://www.molgenis.org/c2cards for a description). Extending each of the three perspectives offered by the C2CardsHuman to multiple organisms could enable using knowledge about metabolism in model organisms to resolve conflicts between the human pathway databases. Note that this does require the use of an ortholog mapping such as InParanoid [A biologically accurate and complete description of the metabolic network for human and other organisms is of utmost importance to, amborees . Our appParanoid .2Card. One could start by curating the reactions contained in all or the majority of the databases. In fact, for more than half of the reactions found in all five human metabolic pathway databases, there is no agreement on the EC numbers and genes linked to a reaction [2Cards can also be of use if a consensus network for a given organism has already been established. We envision that the C2Cards application could serve as a central platform in which the consensus network can be further refined and extended with knowledge available in pathway databases not used for its construction. We are planning to expand C2CardsHuman with the community-driven consensus human metabolic network Recon 2 [2CardsHuman, for example, could be a source of candidates for expanding Recon 2. Bringing the differences between the consensus network and other descriptions to the attention of experts would enable further refinement of Recon 2. As a first step towards such a platform, users can already add comments to a C2Card, preferably substantiated by references to the literature. They can subscribe to C2Cards of their interest and receive an e-mail when new comments are added. Different or even contradictory views possibly held by contributors can be clearly exposed in this forum set-up. Based on these contributions a team of curators could then decide to incorporate the necessary changes in the consensus network, if enough evidence supports this claim. In the future we could extend the forum by allowing people to rank the contributions to bring to the foreground the forum entries deemed most important and thereby aiding the curators. Notably, as illustrated by case study III, it may lead to the conclusion that further biochemical characterization experiments are required. Since pathway databases are continuously being refined and new information is being added, we could also include the possibility to automatically alert the curators by mailing them updated or additional C2Cards.As a guide for integrating pathway databases, we provide overviews of which genes, EC numbers, and reactions can be found in which database. The entries in these overviews are linked to the corresponding Creaction  and addi Recon 2 , which w2Cards was designed to be easy to use and suitable for users with different backgrounds. The application can be accessed via smartphones and tablets as well, allowing C2Cards to be viewed and discussed nearly anywhere. Furthermore, a C2Card can be downloaded for off-line use. The curation of a C2Card is done at the level of a single reaction or the metabolic functions of a single gene product. This may lower the threshold for experts to contribute as well and also allows (very) detailed knowledge of just a single step in the metabolic network to be added. One way to stimulate expert contributions would be to make the contribution traceable and citable in the form of ‘nanopublications’ [statement, e.g., protein X (subject) catalyzes (predicate) reaction Y (object), conditions under which the statement holds, e.g., a specific compartment, and provenance of the statement, e.g., author and literature. Besides that this provides an incentive for experts to share their knowledge, it is also a way to ensure that contributions of curators are substantiated by references to the literature. We also plan to include in C2CardsHuman the human metabolic pathways of WikiPathways [2Cards can contribute to this endeavor by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute.It is important to actively involve domain experts in this continuous curation process, even though they may only indirectly benefit from contributing to such an effort. To make the barrier to contribute as low as possible, the web interface of the Ccations’ . A nanopPathways , an open2CardsHuman was built upon the same dataset we used previously [i.e., EHMN, H. sapiens Recon 1, HumanCyc, and the human metabolic subsets of KEGG and Reactome (Table http://ftp://ftp.ncbi.nih.gov/pubchem/Compound/Extras/CID-SID.gz) to convert PubChem Substance IDs to PubChem Compound IDs.Ceviously  for a come Table . For eacme Table . To compAlthough not used for comparing metabolites, we also retrieved the InChI and SMILES of metabolites, when provided by the pathway database, as additional information. For the genes we retrieved the Entrez Gene and Ensembl Gene ID, if available. For display and comparison purposes we mapped the Entrez Gene and Ensembl Gene IDs to their corresponding HGNC symbol as provided by the Entrez Gene and Ensembl database, respectively. Both the Entrez Gene ID and the Ensembl Gene ID were not available for 396 genes in HumanCyc. For 106 of these genes the UniProt ID was used to retrieve the Entrez Gene ID and/or Ensembl Gene ID. All out-of-date identifiers and EC numbers were transferred to the current ID/EC number  or on name. In the latter case we also required the chemical formula to match. A difference in the number of H atoms when comparing chemical formulae was ignored. Furthermore, matching on names was case-insensitive and spaces and punctuation were ignored. Also for the metabolite matching we computed the transitive closure (see above).Reactions For reactions we determined the percentage of metabolites they agreed upon, respecting the two sides of a reaction, but ignoring the direction of a reaction. We did not consider e-, H+, H2O in matching reactions as with respect to these particular metabolites reactions are not always balanced. In addition, due to the different pH levels under which the reactions are stated in the databases, the e- and H+ metabolites may or may not be included in a reaction. Furthermore, we did not take into account the compartmentalization of reactions. The similarity of two reactions was measured by the percentage of overlap:R1 and R2 denote the two reactions being compared. Furthermore, we computed the transitive closure for the reaction matches as well (see above).where 2Cards database which IDs can best be used for comparing genes and metabolites. Only a few changes to the code and the original C2Cards database scheme are required to use other IDs for matching. A more detailed description of the changes to make is available on our website (http://www.molgenis.org/c2cards).It depends on the organism and the specific pathway databases included in the C2CardsHuman was built using the Molecular Genetics Information Systems (MOLGENIS) toolkit [http://www.freemarker.org), and programming interfaces in Java, R, SOAP and REST to the underlying MySQL database. Building on these generated software we used MOLGENIS ‘plug-in’ framework to program in Java and JavaScript extra features that are specific for C2CardsHuman, such as the various search options. The result is installed on a standard Tomcat web server, but can also run ‘standalone’ using the MOLGENIS embedded web server. A local installation of C2CardsHuman is also available upon request. All code and the database scheme is open source and can be used as a basis for building a C2Cards application for other organisms. A manual on how to do this is available on our website (http://www.molgenis.org/c2cards). The code for the C2Cards application is available at http://www.molgenis.org/svn/c2cards/trunk/. A copy of the core MOLGENIS project is also required, which is available at http://www.molgenis.org/svn/molgenis/branches/molgenis_c2cards.C toolkit . This so2Card contains a reaction, the EC number(s), gene(s), and the pathway linked to the reaction, and the name of the source database. If a reaction was assigned to multiple pathways, a separate row is used for each pathway. The metabolites of a reaction are represented by their primary name as indicated by the pathway database. Although not taken into account when matching reactions, the direction of a reaction and the compartment(s) as indicated by the source database are shown in a C2Card. If the direction was not provided this is indicated with ‘|==|’. Multiple EC numbers are connected by a comma. Following the convention used in Recon 1, genes of which the products are isozymes are connected by the Boolean operator ‘or’. If the gene products form a complex ‘and’ is used. EHMN and KEGG, however, do not have a syntactic mechanism for describing isozymes nor complexes. Therefore, if multiple genes were linked to a reaction by EHMN and KEGG, they are connected by a comma. Genes are represented by the HGNC symbol retrieved from Entrez Gene. The Entrez Gene ID was, however, not always available for every gene, and the HGNC symbol could not always be retrieved when the Entrez Gene ID was available. In these cases we used, when available, the Ensembl Gene ID to retrieve the HGNC symbol. For 358 genes the HGNC symbol was not available via either gene identifier type. In this case the gene is represented by its Entrez Gene or Ensembl Gene ID, depending on which of these two was available. For 274 genes in HumanCyc these two gene identifiers were also not available and for these cases the internal gene identifier of HumanCyc is used for representation. If multiple HGNC symbols were linked to a gene they are separated by two underscores. Note also that HumanCyc and Reactome may link multiple Entrez Gene IDs to a single gene, which in most cases will also result in multiple HGNC symbols. Similarly, KEGG and Reactome contain genes linked to multiple Ensembl Gene IDs.Each row in a CBiGG: Biochemically, Genetically, and Genomically structured; C2Cards: Consensus and Conflict Cards; EC: Enzyme Commission; EHMN: Edinburgh Human Metabolic Network; HGNC: HUGO Gene Nomenclature Committee; ID: Identifier; KEGG: Kyoto Encyclopedia of Genes and Genomes; KGML: KEGG Markup Language; MOLGENIS: Molecular Genetics Information Systems; NC-IUBMB: Nomenclature Committee of the International Union of Biochemistry and Molecular Biology; REST: Representational State Transfer; SBML: Systems Biology Markup Language; SOAP: Simple Object Access Protocol; TCA: Tricarboxylic Acid.The authors declare that they have no competing interests.2Cards and developed the web application, C2CardsHuman. TR and MAS extended the MOLGENIS toolkit with dedicated plug-ins for C2CardsHuman. PDM contributed to the design of the web application. IT contributed to interpretation of the case studies. MDS and PDM wrote the manuscript; IT, MAS, and AHCvK helped draft the manuscript; AHCvK and PDM supervised the project. All authors read and approved the final manuscript.MDS designed the CExample of a C2Card. A C2Card centered at an EC number may reveal possible alternative substrates, which is one of the sources of conflict between metabolic pathway databases . The C2Card centered at the EC number 1.1.1.35 (3-hydroxyacyl-CoA dehydrogenase) provides an example of this scenario. The C2Card was exported to an Excel file via the web application. This file contains, besides the core table of the C2Card, also the overview of the reaction comparison, and information on the metabolites, gene(s), and EC number(s) in the C2Card. The number of unique reactions, not taking into account compartmentalization, linked to the EC number 1.1.1.35 varies from 2 in HumanCyc and Recon 1 to 62 in EHMN, as shown in the first worksheet.Click here for fileTransferred and obsolete identifiers and EC numbers per database. Number of transferred and obsolete EC numbers, gene, and metabolite identifiers for each of the five pathway databases.Click here for fileDatabase scheme C2CardsHuman. Overview of the tables in the database of C2CardsHuman. Only the three ‘forum_topic’ tables, the overview tables, and the table with the statistics of the comparison of the five human pathway databases are specific for C2CardsHuman. The SQL script needed to generate the database is available at: http://www.molgenis.org/svn/c2cards/trunk/data/c2cardsdb_empty.sql.Click here for file