THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. KinasePathwayDatabase is an integrated database concerning completed sequenced major eukaryotes, which contains the classification of protein kinases and their functional conservation and orthologous tables among species, protein-protein interaction data, domain information, structural information, and automatic pathway graph image interface. The protein-protein interactions are extracted by natural language processing (NLP) from abstracts using basic word pattern and protein name dictionary GENA: developed by our group. In this system, pathways are easily compared among species using protein interactions data more than 47,000 and orthologous tables.

Reference database for pathway mapping in KEGG Mapper. Collection of manually drawn pathway maps representing knowledge on molecular interaction, reaction and relation networks for metabolism, genetic information processing, environmental information processing, cellular processes, organisms systems, human diseases, drug development.

The EPA has developed the Adverse Outcome Pathway Database (AOP-DB) to better characterize adverse outcomes of toxicological interest that are relevant to human health and the environment. Since its inception, the AOP-DB has been developed with the aim of integrating AOP molecular target information with other publicly available datasets to facilitate computational analyses of AOP information. A user application for the AOP-DB has been developed for public accessibility. Potential users of the data (Government, Academic, or Industry scientists and researchers) may investigate specific molecular targets of an AOP, the relation of those gene/protein targets to other AOPs, chemical stressor, pathway, or disease-AOP relationships, for example.

hiPathDB is an integrated pathway database that combines the curated human pathway data of NCI-Nature PID, Reactome, BioCarta and KEGG. In total, it includes 1661 pathways consisting of 8976 distinct physical entities. (2010.03.09) hiPathDB provides two different types of integration. The pathway-level integration, conceptually a simple collection of individual pathways, was achieved by devising an elaborate model that takes distinct features of four databases into account and subsequently reformatting all pathways in accordance with our model. The entity-level integration creates a single unified pathway that encompasses all pathways by merging common components. Even though the detailed molecular-level information such as complex formation or post-translational modifications tends to be lost, such integration makes it possible to investigate signaling network over the entire pathways and allows identification of pathway cross-talks. Another strong merit of hiPathDB is the built-in pathway visualization module that supports explorative studies of complex networks in an interactive fashion. The layout algorithm is optimized for virtually automatic visualization of the pathways.

It is divided to four categories based on extracellular signal molecules (Growth factor, Cytokine, and Hormone) and stress, that initiate the intracellular signaling pathway. SPAD is compiled in order to describe information on interaction between protein and protein, protein and DNA as well as information on sequences of DNA and proteins. There are multiple signal transduction pathways: cascade of information from plasma membrane to nucleus in response to an extracellular stimulus in living organisms. Extracellular signal molecule binds specific intracellular receptor, and initiates the signaling pathway. Now, there is a large amount of information about the signaling pathway which controls the gene expression and cellular proliferation. We have developed an integrated database SPAD to understand the overview of signaling transduction.

A comprehensive list of databases can be found in Pathguide (http://www.pathguide.org). A, automated curation; B, both manual and automated curation; BIND, Biomolecular Interaction Network Database; BioPP, Biological Pathway Publisher; DIP, Database of Interacting Proteins; EcoCyc, Encyclopaedia of E. coli Genes and Metabolism; GNPV, Genome Network Platform Viewer; HPRD, Human Protein Reference Database; KEGG, Kyoto Encyclopedia of Genes and Genomes; M, manual curation; MetaCyc, a Metabolic Pathway database; MINT, Molecular Interation Database; MIPS, Munich Information Center for Protein Sequences; N, No; OPHID, Online Predicted Human Interaction Database; PANTHER, Protein Analysis through Evolutionary Relationship Database; PID, The Pathway Interaction Database; STKE, Signal Transduction Knowledge Environment, UNIHI, Unified Human Interactome; Y, yes. (61 KB DOC)

An interactive, visual database containing more than 350 small molecule pathways found in humans. More than 2/3 of these pathways (>280) are not found in any other pathway database. SMPDB is designed specifically to support pathway elucidation and pathway discovery in metabolomics, transcriptomics, proteomics and systems biology. It is able to do so, in part, by providing exquisitely detailed, fully searchable, hyperlinked diagrams of human metabolic pathways, metabolic disease pathways, metabolite signaling pathways and drug-action pathways. All SMPDB pathways include information on the relevant organs, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Each small molecule is hyperlinked to detailed descriptions contained in the HMDB or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. All SMPDB pathways are accompanied with detailed descriptions and references, providing an overview of the pathway, condition or processes depicted in each diagram. The database is easily browsed and supports full text, sequence and chemical structure searching. Users may query SMPDB with lists of metabolite names, drug names, genes / protein names, SwissProt IDs, GenBank IDs, Affymetrix IDs or Agilent microarray IDs. These queries will produce lists of matching pathways and highlight the matching molecules on each of the pathway diagrams. Gene, metabolite and protein concentration data can also be visualized through SMPDB''s mapping interface. All of SMPDB''s images, image maps, descriptions and tables are downloadable.

Metacyc is a curated database of experimentally elucidated metabolic pathways from all domains of life.

Additional file 2: Table S2. NES values of leukemia expression. The NES scores for each mutation in pathways are calculated using GSEA. The differences between NES of subtype S and subtype L are listed in column 'Ave. NES difference'. The 'CLGS' column describes whether each path belongs to CLGS.

This pathway is based on Pathways in Cancer from KEGG. It represents a combination of pathways affected in various cancers. Interactions depicted in orange are interrupted or disturbed by mutations in related genes.

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) contains information on microbial biocatalytic reactions and biodegradation pathways for primarily xenobiotic, chemical compounds. The goal of the UM-BBD is to provide information on microbial enzyme-catalyzed reactions that are important for biotechnology. This collection refers to pathway information.

The CottonGen CottonCyc Pathways Database, part of CottonGen, supports searching and browsing the following CottonCyc databases: Cyc pathways for JGI v2.0 G. raimondii D5 genome assembly This Cyc database was constructed using PathwayTools version 20.0 using the gene models from the JGI v2.0 D5 genome assembly of Gossypium raimondii. There has been no manual curation of this Cyc database. Pathway predictions were made using PathwayTools and in-silico v2.1 annotations as provided by JGI. Cyc pathways for CGP-BGI v1.0 G. hirsutum AD1 genome assembly This Cyc database was constructed using PathwayTools version 20.0 using the gene models from the CGP-BGI v1.0 AD1 genome assembly of Gossypium hirsutum. There has been no manual curation of this Cyc database. Pathway predictions were made using PathwayTools and in-silico v1.0 annotations as provided by CGP-BGI. Search parameters include genes, proteins, RNAs, compounds, reactions, pathways, growth media, and BLAST search. Resources in this dataset:Resource Title: Website Pointer to CottonGen CottonCyc Pathways Database. File Name: Web Page, url: http://ptools.cottongen.org/

The human pathway database which contains different biological entities and reactions and software tools for analysis. PATIKA Database integrates data from several sources, including Entrez Gene, UniProt, PubChem, GO, IntAct, HPRD, and Reactome. Users can query and access this data using the PATIKAweb query interface. Users can also save their results in XML or export to common picture formats. The BioPAX and SBML exporters can be used as part of this Web service.

Additional file 3: Table S3. Random forest breast cancer grade prediction accuracy. All results with prediction accuracy and cross-validation accuracy larger than 0.55 and min gene importance larger than 0.1. The result ranking was determined by min(cross-validation accuracy, prediction accuracy). The “Top Gene” corresponds to the gene with the highest feature importance score in each pathway.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

RANKL (Receptor activator of nuclear factor-kappa B ligand), RANK (Receptor activator of nuclear factor-kappa B) and the natural decoy receptor of RANKL, OPG (Osteoprotegerin) are three important molecules identified to play a major role in osteoclastogenesis and bone remodelling. They are members of the tumor necrosis factor (TNF) superfamily. OPG was the first molecule to be discovered and proved to inhibit osteoclastogenesis both in vivo and in vitro. Unlike other members of TNF family, OPG lack a transmembrane domain and is secreted as a soluble protein by the cell. RANKL is the only known physiological agonist for its receptor, RANK. Genetic experiments have shown that mice lacking either rankl or rank suffer from severe osteoporosis and defective tooth eruption due to complete lack of osteoclasts. On the contrary, mice deficient of OPG shows osteoporosis due to increased number of osteoclasts. Binding of RANKL to RANK triggers downstream signaling events that leads to the activation of osteoclasts and controlling of lineage commitment. RANKL/RANK signaling is essential for skeletal homoeostasis and its interference leads to inhibition of bone resorption resulting in bone diseases including osteoporosis osteopetrosis and rheumatoid arthritis. RANK being a member of TNF family does not possess any kinase activity. It recruits adaptor molecules to transduce the signal after ligand binding. These adaptor molecules are called TNFR-associated factors or TRAF's that binds to different regions in the cytoplasmic tail of the TNF family receptors and transduces the signal downstream. TRAF6 is the main adaptor molecule which activates NF-κB pathway downstream of RANKL signaling which is required for osteoclastogenesis and osteoclast activation. TRAF6 mutant mice have shown a partial block in osteoclastogenesis and defective activation of mature osteoclasts. Mice lacking NF-κB p50 and p52 proteins have been shown to be osteopetrotic. Catalytic subunits, IκB kinase α and IκB kinase β and the non-catalytic subunit IKKγ (also called NEMO) are also essential for RANKL-RANK signaling and osteoclastogenesis. IKKγ is required for osteoclastogenesis induced by RANKL in mice both in vivo and in vitro whereas IKKα was shown to be required in mice only in in vitro. Several mitogen activated protein kinases (MAPK's) have been shown to be activated downstream of RANK. Studies have shown that pharmacological inhibition of p38 MAPK's blocked RANKL induced osteoclast differentiation. JNK1/2, its upstream kinase MKK7 and c-Jun have also been shown by genetic experiments to be essential for RANKL induced osteoclastogenesis. MAPK1 and MAPK3 phosphorylation was also shown to be dispensable for RANKL mediated osteoclast differentiation in vitro, but another report also show that specific inhibitors to MEK increased RANKL induced osteoclastogenesis suggesting a cross talk between p38 and ERK signaling pathways. NFATc1 is an essential downstream target of RANK. Ca2+ oscillations induced by RANKL activated NFATc1 resulting in terminal differentiation of osteoclasts through the Ca2+- dependent calcineurin pathway. NFATc1 translocates to the nucleus where it interacts with other transcription factors leading to the activation of transcription of genes including ACP5, CTSK, TNFRSF11A and NFATc1 under RANKL stimulation. TRAF6 and c-Src interacts with each other and with RANK upon stimulation with RANKL. This interaction increases the kinase activity of c-Src leading to the tyrosine phosphorylation of downstream molecules such as c-Cbl and activation of Akt/PKB which in turn requires the PI3-Kinase activity. Genetic experiments have shown that c-Src is very important in osteoclastogenesis. In addition to these pathways, aPKC/p62 signaling is also reported to be essential for osteoclastogenesis. Apart from their role in osteoclast differentiation and function, RANKL-RANK signaling is also required for development of lymph node and lactating mammary glands in mice and in the establishment of thymic microenvironment. Please access this pathway at NetSlim database. If you use this pathway, please cite following paper: Raju, R., Balakrishnan, L., Nanjappa, V., Bhattacharjee, M., Getnet, D., Muthusamy, B., Thomas, J. K., Sharma, J., Rahiman, B. A., Harsha, H. C., Shankar, S., Prasad, T. S. K., Mohan, S. S., Bader, G. D., Wani, M. R. and Pandey, A. (2011). A comprehensive manually curated reaction map of RANKL/RANK signaling pathway. Database (Oxford). 2011, bar021.

The Therapeutically Relevant Multiple Pathways Database is designed to provide information about such multiple pathways and related therapeutic targets described in the literatures, the targeted disease conditions, and the corresponding drugs/ligands directed at each of these targets. This database currently contains 11 entries of multiple pathways, 97 entries of individual pathways, 120 targets covering 72 disease conditions along with 120 sets of drugs directed at each of these targets. Each entry can be retrieved through multiple methods including multiple pathway name, individual pathway name and disease name. Additional information provided include protein name, synonyms, Swissprot AC number, species, gene name and location, protein sequence (AASEQ) and gene sequence (NTSEQ) as well as potential therapeutic implications while applicable. Cross-links to other databases are provided which include Genecard, GDB, Locuslink, NCBI, KEGG, OMIM, SwissProt to facilitate the access of more detailed information about various aspects of the particular target or non-target protein. Queries can be submitted by entering or selecting the required information in any one or combination of the fields in the form. User can specify full name or any part of the name in a text field, or choose one item from an selection field. Sponsors: TRMP is supported by the National University of Singapore.

A database of biochemical pathways that provides access to metabolic transformations and cellular regulations derived from the Roche Applied Science Biochemical Pathways wall chart.

This pathway incorporates the most important proteins for breast cancer. The Rp score from the Connectivity-Maps (C-Maps) webserver was used to determine the rank of the most important proteins in breast cancer. These proteins were then used to determine the most important pathways involved in breast cancer by using the Human Pathway Database (HPD). The pathways retrieved from the Human Pathway Database were from several sources such as Protein Lounge, BioCarta, KEGG, and NCI-Nature. The pathways were then annotated. Protein-protein relations for the most important proteins for breast cancer were determined by annotating the pathways and by literature review. The protein-protein interactions are mapped onto this pathway. Proteins on this pathway have targeted assays available via the CPTAC Assay Portal.