Volume 10 Supplement 11
HPD: an online integrated human pathway database enabling systems biology studies
- Sudhir R Chowbina†1, 2,
- Xiaogang Wu†1, 2,
- Fan Zhang1, 2,
- Peter M Li1,
- Ragini Pandey2,
- Harini N Kasamsetty1 and
- Jake Y Chen†1, 2, 3Email author
© Chowbina et al; licensee BioMed Central Ltd. 2009
Published: 8 October 2009
Pathway-oriented experimental and computational studies have led to a significant accumulation of biological knowledge concerning three major types of biological pathway events: molecular signaling events, gene regulation events, and metabolic reaction events. A pathway consists of a series of molecular pathway events that link molecular entities such as proteins, genes, and metabolites. There are approximately 300 biological pathway resources as of April 2009 according to the Pathguide database; however, these pathway databases generally have poor coverage or poor quality, and are difficult to integrate, due to syntactic-level and semantic-level data incompatibilities.
We developed the Human Pathway Database (HPD) by integrating heterogeneous human pathway data that are either curated at the NCI Pathway Interaction Database (PID), Reactome, BioCarta, KEGG or indexed from the Protein Lounge Web sites. Integration of pathway data at syntactic, semantic, and schematic levels was based on a unified pathway data model and data warehousing-based integration techniques. HPD provides a comprehensive online view that connects human proteins, genes, RNA transcripts, enzymes, signaling events, metabolic reaction events, and gene regulatory events. At the time of this writing HPD includes 999 human pathways and more than 59,341 human molecular entities. The HPD software provides both a user-friendly Web interface for online use and a robust relational database backend for advanced pathway querying. This pathway tool enables users to 1) search for human pathways from different resources by simply entering genes/proteins involved in pathways or words appearing in pathway names, 2) analyze pathway-protein association, 3) study pathway-pathway similarity, and 4) build integrated pathway networks. We demonstrated the usage and characteristics of the new HPD through three breast cancer case studies.
HPD http://bio.informatics.iupui.edu/HPD is a new resource for searching, managing, and studying human biological pathways. Users of HPD can search against large collections of human biological pathways, compare related pathways and their molecular entity compositions, and build high-quality, expanded-scope disease pathway models. The current HPD software can help users address a wide range of pathway-related questions in human disease biology studies.
The study of biological pathways has become a central topic in molecular systems biology . While the precise definition of "biological pathway" is still debatable, most researchers regard a biological pathway as a series of inter-connected cellular events among biomolecular entities. A biological pathway can be activated by extracellular stimuli and lead to persistent changes of the biochemical state of cells. There are three major types of molecular pathway events (or, events for brevity) that define biological pathways:
Signal transduction events. Common in signalling pathways (e.g., Wnt signaling pathway ), these events define the interactions among molecular entities during signal transduction cascades, i.e., how external stimuli such as molecules in the cellular environment are transduced into intracellular molecular signals that are relayed among different cellular organelles. Examples of signal transduction events in signalling pathways are protein-protein interactions, protein post-translational modifications, protein translocations, and protein complex formations/dissociations.
Enzymatic reaction events. Common in metabolic pathways (e.g., glycolysis pathway), these events define chemical reactions that metabolites (as either substrates or products) and catalytic enzymes are involved in. Examples of enzymatic reaction events are catabolic reactions (breaking down of larger molecules to produce energy) and anabolic reactions (synthesis of cellular components from smaller molecules).
Genetic regulation events. Common in genetic regulatory pathways (e.g., usually abbreviated as regulatory pathways), these events define the dependent relationships between regulatory entities, e.g., a transcription factor that binds to specific DNA binding motifs, and target entities, and a gene whose transcription is being regulated by a transcription factor. In addition to gene regulation events, regulatory pathways may also include sRNA and sRNA target gene regulation.
Collecting and modeling biological pathways are critical for interpreting "Omics" data . For example, pathway knowledge has been used to identify new functional modules from gene expression profiles [4, 5] and relate gene mutations to one another in polygenic diseases such as breast cancer . The development of biological pathways can also help build disease biology models, from which new hypotheses of targeted drugs and robust biomarkers may be developed. For example, molecular entities in FGFR1/PI3K/AKT signaling pathways, the Akt/PKB pathway, the Met pathway, and the Wnt signaling pathway have all been extensively investigated as potential cancer drug targets [7–10]. Novel drug discovery strategies to screen small molecules based on an entire pathway instead of particular protein targets can also be developed by designing global disease-related pathway inhibitors . Pathway studies have also shown promise in molecular diagnostic applications, e.g., identifying efficacy and toxicity biomarkers , and building new multi-marker panels to improve prediction of disease prognosis and development of treatment plans . Ongoing efforts to represent, develop, and apply pathway models will be crucial for future genome medicine and personalized medicine applications [14, 15].
While there are approximately 300 biological pathway-related online resources reported by Pathguide http://www.pathguide.org/today, these resources have been developed with variable degrees of data coverage, quality, and utility . Examples of high-quality biological pathway database resources are: SPAD , CST , STKE  and COPE  for signaling pathways; TRANSFAC  for regulatory pathways; and KEGG , WIT , ExPASy , UM-BBD  and HumanCyc  for metabolic pathways. In addition, new databases such as HPRD , HAPPI , and STRING  have been developed to provide available high-throughput protein-protein interaction data to help fill gaps in rapidly growing molecular signaling pathway data. Recent efforts to expand biological pathway coverage beyond a single pathway event type have also been reported, e.g., NCI-PID , Reactome , BioCarta , Pathway Commons , Panther , Protein Lounge  and WikiPathways . However, by comparing the coverage of high-quality protein-protein interactions from the HAPPI database  with annotated human pathways documented from the Reactome database, for example, it is not difficult to conclude that current coverage of known human biological pathway events is 1–2 orders of magnitude smaller than the theoretical maximum that can be defined by all known reliable human protein-protein interactions. Therefore, many pathway biology studies begin by expanding biological pathway data coverage and building high-quality integrative pathway models.
The most reliable approach to expanding human pathway data coverage without sacrificing data quality continues to be database integration. While there are several computational techniques that can help predict metabolic pathways , regulatory pathways [37, 38], and signaling pathways , they all have limited applicability and are thus beyond the scope of this work. However, integrating biological pathway from different data sources has been challenging, due to the heterogeneity in pathway data formats, representation schemes, and retrieval methods. For example, at the syntactic level, while many pathway databases such as the NCI-PID , Reactome , and KEGG  provide both molecular component and molecular interaction data as XML documents, Protein Lounge  and BioCarta  provide pathway details (including molecular entities and pathway events) only in TXT file and embedded pathway diagrams. Pathway ontology standards such as PSI-MI  or BioPAX  or GPML  can help resolve syntactic level data heterogeneity; however, these standards are relatively new and are available only in a few recent systems such as cPATH , NCI-PID , Reactome  and WikiPathways . At the semantic level, incompatible pathway names, event representations, and molecular entity identifiers also poses challenges in querying pathway information across pathway data sources, particularly those with complementary information. Pathway names from different pathway data sources for the same pathway often differ slightly and therefore are poor choices as identifiers. Identifying pathways directly using pathway molecular entities can also be problematic, because the ensemble of molecular entities referring to the same pathway may vary among different annotation sources. Pathway molecular entities may be referred to with any public sequence identifier, which includes RefSeq ID, HGNC symbol, GenBank accession, SwissProt ID, UniProt name, KEGG ID, or IPI number. Furthermore, different databases may choose to provide available pathway information at different levels of molecular detail, e.g., with protein post-translational modification status, protein complex association status, or cellular location information. In summary, pathway data incompatibility at both the syntactic and semantic levels has inhibited the growth of high-quality integrative pathway data sources.
In this work, we describe the development of a new online integrated pathway database resource, the Human Pathway Database (HPD). HPD is an ongoing pathway data warehousing project, in which we integrate all three types of human pathway data and compile additional detailed information on pathway genes, proteins, metabolites, protein complexes, and pathway events. The concept of developing an organism-specific integrated pathway database resource is not unique, e.g., MAtDB  for managing all biological pathways for Arabidopsis and FlyMine  for managing both functional genomics and pathway data for Drosophila. Applying semantic-level data integration techniques, we collect, represent, and manage human-specific pathway data in HPD based on information from NCI-PID, Protein Lounge, KEGG, BioCarta, and Reactome databases. HPD provides a comprehensive view of current human biological pathway data, which consists of a total of 999 pathways and 59,341 molecular entities. Online HPD users may search the database for all relevant pathway information related to query protein(s), identify all pathways involving a query protein(s), and examine details related to pathway components, molecular events, and related pathways. Using three case studies, we show how to take advantage of HPD online and backend database querying capabilities to manage, query, and compare different types of biological pathways for systems biology studies. HPD is freely available online at http://bio.informatics.iupui.edu/HPD.
Database content statistics
A comparison of human pathways in HPD against several common pathway data sources.
Scope of Content
Metabolic and signaling pathways
Metabolic, Regulatory, signaling, disease and drug pathways
Metabolic, signaling and regulatory pathways
Signaling and regulatory pathways
Metabolic, signaling and regulatory pathways
Metabolic, signaling and regulatory pathways
Manual and Computational Prediction
Integrated from Manually curated database
Multiple Protein Search
Pathway-Protein Association Table
Pathway-Pathway Similarity Network
Scale distributions of integrated HPD pathways
General online features
To demonstrate the capabilities of HPD, we show three case studies of increasing complexity and biological significance to demonstrate how HPD could be used to solve real-world biological pathway problems.
Case study 1: searching for biological pathways and their components based on a single query protein
Using the standard query box provided at the HPD home page, we can search HPD for all biological pathways involving BRCA1_HUMAN (a major protein involved with breast cancer susceptibility). HPD returns a list of the top 20 BRCA1-related pathways, which are ordered by decreasing number of proteins that each pathway shares among all pathway pairs from retrieved pathways. The better the rank a retrieved pathway has, the more related it should be to both the query protein BRCA1 and all BRCA1-relevant pathways. In this list, highly-ranked pathways such as "Molecular Mechanisms of Cancer", "P53 Signaling", "DNA Repair Mechanism", and "BRCA1 pathway" are all well characterized signaling pathways in breast cancer. All pathways are hyperlinked to their own detailed pathway information pages, which include molecular entities (proteins, complexes and metabolites), related pathways, events, and external pathway images and reference articles. (See Figure 2 for details).
The Web page with the list of pathways related to BRCA1 also contains links to download data. Four types of data, pathway list, pathway-protein association matrix, and pathway-pathway similarity scores are downloadable as flat files.
Note that the pathway-protein association matrix contains proteins that are involved in the top 20 pathways retrieved based on the single protein query, sorted according to their descending maximal pathway involvement by activity count. BRCA1 related proteins are retrieved by pathway, with each of the proteins covered by at least two of the 20 pathways. A close examination reveals that many breast cancer susceptibility genes including BRCA1, BRCA2, P53, PCNA , FOXA1  and STK6  from recent individual studies and breast cancer biomarker genes such as ERBB2, FGFR2, M3K1, and PTEN [49, 50], have all been found in this list.
Particularly noteworthy is the Applet in the HPD Web page that shows all the query-related biological pathways with involved proteins in a heat map. In Figure 3, BRCA1 related pathways and involved proteins are sorted and used as two separate dimensions of the matrix. Mousing over a color-filled cell invokes an applet tooltip message, which shows the pathway and protein names.
HPD users can also visualize the pathway-pathway similarity matrix (Figure 4) which shows the similarity score among the BRCA1 related pathways. The pathway-pathway similarity matrix allows users to visualize a cluster of similar pathway pairs as a 2-D interactive heat map. This heat map allows users to right click on any cell (shown in Figure 4) to compare pathway pair on the heat map (future versions will include multiple pathway selection) by looking at the pathway-protein association matrix. This facilitates better understanding for deriving novel pathways most similar to BRCA1 related pathways.
Case study 2: developing pathway-pathway similarity networks from heterogeneous data sources
A list of HPD pathways retrieved by the query BRCA1.
Molecular Mechanisms of Cancer
DNA Repair Mechanism
Chks in Checkpoint Regulation
Aurora A signaling
BARD1 signaling events
role of brca1 brca2 and atr in cancer susceptibility
Ubiquitin mediated proteolysis
cell cycle: g2/m checkpoint
atm signaling pathway
Fanconi's Anaemia Pathway
DNA Damage Induced 14-3-3Sigma Signaling
brca1 dependent ub ligase activity
FOXA1 transcription factor network
Recruitment of repair and signaling proteins to double-strand breaks
ATM mediated phosphorylation of repair proteins
Case study 3: developing integrated pathway models from heterogeneous sources
While pathway-pathway similarity networks are useful for generating global perspectives on the relationships between pathways, the next case study demonstrates how to connect different types of biological pathways within HPD to form integrated pathway networks. Since pathway data managed at HPD is integrated at the schematic level, "deep integration" and "deep integrative analysis" are possible. We will use two breast cancer-related proteins, BRCA1_HUMAN and FOXA1_HUMAN, as an example. According to the HPD data model (See additional file 2 for details), the table Connect_mol_updated contains mappings among pathways, interactions, and molecules. To search for all related pathways containing the above two proteins within the HPD data warehouse, we can execute the following SQL query:
SELECT pathway_name,mol_in, Mol_In_updated, name_in, Mol_out,
Mol_Out_updated, name_out, interaction_type, SYS_CONNECT_BY_PATH(Mol_In, '/') "Path"
START WITH name_in = 'BRCA1_HUMAN'
CONNECT BY nocycle PRIOR Mol_Out_updated=Mol_In_updated
and level < 3
SELECT pathway_name,mol_in, Mol_In_updated, name_in, Mol_out,
Mol_Out_updated, name_out, interaction_type, SYS_CONNECT_BY_PATH(Mol_In, '/') "Path"
START WITH name_in = 'FOXA1_HUMAN'
CONNECT BY nocycle PRIOR Mol_Out_updated=Mol_In_updated
and level < 3;
The integrated pathway model based on HPD pathways can be used as an investigative tool for disease diagnostic and therapeutic applications. For example, 9-cis-Retinoic acid is recognized as a possible breast cancer biomarker  and FOXA1 has gained increasing attention as a possible breast cancer therapeutic target . The BRCA2-RAD51 interaction is essential for DNA repairs and has also been suggested as a novel target for anti-breast cancer drugs . In addition to breast cancer, links between breast cancer and other diseases can be studied. For example, increased risk of hereditary prostate cancer is known to be a result of polymorphism in the CDKN1B (p27) gene . Epoxide hydrolase 2 has been characterized as a key mediator molecule in hypertensive, cardiovascular, inflammatory, pulmonary, and diabetic-related diseases [64–66]. CHILD syndrome, an X-linked dominant trait with lethality for male embryos, can also be traced to mutations in NSDHL, a gene playing crucial roles in the cholesterol biosynthetic pathway .
Through this case study, we have shown the significance of integrating pathway information from different types and data sources. The interconnected network analysis offers researchers a rare opportunity to gain global perspectives on events previously perceived in isolation. This "deep integrative analysis" opportunity cannot be readily obtained by using multiple online pathway databases. For example, NCI Nature Curated Pathway Interaction Database has a 'Connected Molecules' functionality, which may only be used to find molecular connections within the same pathway data source. In all, the convenience of building new integrative pathway models with the new HPD may greatly facilitate new drug development and biomarker discovery.
We developed HPD as an integrated pathway database system to manage, query, and analyze human biological pathways. HPD integrates all three types of biological pathways from five heterogeneous pathway database sources at syntactic, semantic, and schematic levels, primarily based on data warehousing techniques driven by a unified pathway data model. Pathway molecules, interactions, chemical reactions, and similar pathways can be searched, displayed, and downloaded from a unified online user interface. The current HPD software can help users address a wide range of pathway-related questions in human disease biology studies.
While the human Reactome is still far from complete, an integrative pathway database such as HPD has the capability to help researchers establish a global perspective necessary for understanding molecular mechanisms and develop biomedical applications. We will further expand the database to include pathways from HumanCyc , Wikipathways , NetPath , Panther  and TRANSFAC . We also plan to integrate protein-protein interaction data from HAPPI  with the aim of discovering novel pathways when combined with HPD. Additional functions will also be provided such as pathway reconstruction where users can select pathways and derive a reconstructed pathway expanded with protein-protein interaction data. With ongoing efforts, HPD can become a useful resource, linking proteins, genes, RNAs, signaling reactions, and gene regulatory events for systems biology applications.
Pathway data sources
Pathway data integration
We developed a model-driven approach for syntactic, semantic, and schematic level integrations of heterogeneous pathway data. Since pathway data were collected in a variety of formats, Python XML/HTML data parsers were developed to convert them into a common tab-delimited textual format to ensure syntactic level data compatibility. The semantic compatibility of the data was enforced by cleaning up data attributes and data values to keep them consistent, using a standard data extraction, transformation, and loading (ETL) process characteristics of data warehousing-based data integration approaches. All pre-processed data were parsed, cleaned, and loaded into data warehouse staging tables before reaching their final database table destinations. To maintain schematic data compatibilities, we model relationships among different pathway concepts using an entity-relationship (ER) data model (for more details on the data model, please refer to the documentation on the HPD Website and additional file 2). We further mapped all the involved proteins or genes to their UniProt Name Identifiers  and metabolites to their KEGG compound IDs before loading the HPD pathway data into data warehouse tables defined by the ER data model. All HPD molecular entities, events, and pathways were assigned unique HPD-specific identifiers.
Online HPD software design
The HPD database was developed as a data warehouse application. The online version of HPD is a standard 3-tier Web application, which consists of an Oracle 10 g database at the backend database server layer, Apache/PHP server scripts at the middleware application Web server layer, and CSS-driven Web pages presented at the browser.
Pathway similarity measure
Here, N denotes total number of pathways. P i and P j denote two different pathways, while |P i | and |P j | are the numbers of molecules that can be mapped to UniProt ID respectively in these two pathways. Their intersection P i ∩ P j denotes a common set of molecules that can be mapped to the same UniProt ID, while their union P i ∪ P j is calculated as |P i | + |P j | - |P i ∩ P j |. Here α is a weight coefficient among [0, 1], and we currently use α = 0.8 to count varying degree of contributions from calculations based both on the overlap (left item S L ) and the cover (right item S R ).
We can also make special considerations for subnetwork relationship (defined by the Nature Pathway Interaction database at http://pid.nci.nih.gov/. For subnetwork relationship, we define Si, j= 1.01, if pathway P i has a subnetwork as P j , and Si, j= -1.01 if pathway P i is a subnetwork of P j .
The HPD database was developed with research funding from Department of Defense (DOD) Breast Cancer Research Program (BCRP) Concept Award (W81XWH-08-1-0623) to Dr. Jake Chen. We thank Stephanie Burks and Joseph Rinkovsky from the University Information Technology and Services (UITS) at Indiana University for providing generous support in Oracle 10 g database administration and configuring the Web server for the project. We especially thank David Michael Grobe from UITS at Indiana University for thoroughly proofreading the manuscript and provided helpful comments for this project.
This article has been published as part of BMC Bioinformatics Volume 10 Supplement 11, 2009: Proceedings of the Sixth Annual MCBIOS Conference. Transformational Bioinformatics: Delivering Value from Genomes. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/10?issue=S11.
- Cary MP, Bader GD, Sander C: Pathway information for systems biology. FEBS Lett 2005, 579(8):1815–1820. 10.1016/j.febslet.2005.02.005View ArticlePubMedGoogle Scholar
- Logan CY, Nusse R: The Wnt signaling pathway in development and disease. Annu Rev Cell Dev Biol 2004, 20: 781–810. 10.1146/annurev.cellbio.20.010403.113126View ArticlePubMedGoogle Scholar
- Werner T: Bioinformatics applications for pathway analysis of microarray data. Curr Opin Biotechnol 2008, 19(1):50–54. 10.1016/j.copbio.2007.11.005View ArticlePubMedGoogle Scholar
- Shen R, Chinnaiyan AM, Ghosh D: Pathway analysis reveals functional convergence of gene expression profiles in breast cancer. BMC Med Genomics 2008, 1: 28. 10.1186/1755-8794-1-28PubMed CentralView ArticlePubMedGoogle Scholar
- Frasor J, Danes JM, Komm B, Chang KCN, Lyttle CR, Katzenellenbogen BS: Profiling of estrogen up- and down-regulated gene expression in human breast cancer cells: Insights into gene networks and pathways underlying estrogenic control of proliferation and cell phenotype. Endocrinology 2003, 144(10):4562–4574. 10.1210/en.2003-0567View ArticlePubMedGoogle Scholar
- Chittenden TW, Howe EA, Culhane AC, Sultana R, Taylor JM, Holmes C, Quackenbush J: Functional classification analysis of somatically mutated genes in human breast and colorectal cancers. Genomics 2008, 91(6):508–511. 10.1016/j.ygeno.2008.03.002PubMed CentralView ArticlePubMedGoogle Scholar
- Chen GJ, Weylie B, Hu C, Zhu J, Forough R: FGFR1/PI3K/AKT signaling pathway is a novel target for antiangiogenic effects of the cancer drug fumagillin (TNP-470). J Cell Biochem 2007, 101(6):1492–1504. 10.1002/jcb.21265View ArticlePubMedGoogle Scholar
- Cheng JQ, Lindsley CW, Cheng GZ, Yang H, Nicosia SV: The Akt/PKB pathway: molecular target for cancer drug discovery. Oncogene 2005, 24(50):7482–7492. 10.1038/sj.onc.1209088View ArticlePubMedGoogle Scholar
- Mazzone M, Comoglio PM: The Met pathway: master switch and drug target in cancer progression. FASEB J 2006, 20(10):1611–1621. 10.1096/fj.06-5947revView ArticlePubMedGoogle Scholar
- Takahashi-Yanaga F, Sasaguri T: The Wnt/beta-catenin signaling pathway as a target in drug discovery. J Pharmacol Sci 2007, 104(4):293–302. 10.1254/jphs.CR0070024View ArticlePubMedGoogle Scholar
- Schreiber SL: Target-oriented and diversity-oriented organic synthesis in drug discovery. Science 2000, 287(5460):1964–1969. 10.1126/science.287.5460.1964View ArticlePubMedGoogle Scholar
- Xu EY, Schaefer WH, Xu QW: Metabolomics in pharmaceutical research and development: Metabolites, mechanisms and pathways. Current Opinion in Drug Discovery & Development 2009, 12(1):40–52.Google Scholar
- Fujita N, Tsuruo T: Survival-signaling pathway as a promising target for cancer chemotherapy. Cancer chemotherapy and pharmacology 2003, 52(Suppl 1):S24–28. 10.1007/s00280-003-0591-2View ArticlePubMedGoogle Scholar
- Garman KS, Nevins JR, Potti A: Genomic strategies for personalized cancer therapy. Hum Mol Genet 2007, 16(Spec No 2):R226–232. 10.1093/hmg/ddm184View ArticlePubMedGoogle Scholar
- Sander C: Genomic medicine and the future of health care. Science 2000, 287(5460):1977–1978. 10.1126/science.287.5460.1977View ArticlePubMedGoogle Scholar
- Tateishi HS Naoko, Kuhara Satoru, Takagi Toshihisa, Kanehisa Minoru: An integrated database SPAD (Signaling PAthway Database) for signal transduction and genetic information. Genome Informatics 1995, 6: 160–161.Google Scholar
- CST – Cell Signaling Technology Pathway Database[http://www.cellsignal.com/]
- STKE – Signal Transduction Knowledge Environment[http://www.stke.org/]
- COPE – Cytokines and Cells Online Pathfinder Encyclopedia[http://www.copewithcytokines.de/]
- Wingender E, Chen X, Hehl R, Karas H, Liebich I, Matys V, Meinhardt T, Pruss M, Reuter I, Schacherer F: TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Research 2000, 28(1):316–319. 10.1093/nar/28.1.316PubMed CentralView ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 2000, 28(1):27–30. 10.1093/nar/28.1.27PubMed CentralView ArticlePubMedGoogle Scholar
- Overbeek R, Larsen N, Pusch GD, D'Souza M, Selkov E Jr, Kyrpides N, Fonstein M, Maltsev N, Selkov E: WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction. Nucleic Acids Res 2000, 28(1):123–125. 10.1093/nar/28.1.123PubMed CentralView ArticlePubMedGoogle Scholar
- ExPASy – Biochemical Pathways[http://www.expasy.ch/cgi-bin/search-biochem-index]
- Ellis LBM, Hershberger CD, Wackett LP: The University of Minnesota Biocatalysis/Biodegradation Database: microorganisms, genomics and prediction. Nucleic Acids Research 2000, 28(1):377–379. 10.1093/nar/28.1.377PubMed CentralView ArticlePubMedGoogle Scholar
- Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD: Computational prediction of human metabolic pathways from the complete human genome. Genome Biology 2005, 6(1):R2. 10.1186/gb-2004-6-1-r2PubMed CentralView ArticlePubMedGoogle Scholar
- Peri S, Navarro JD, Amanchy R, Kristiansen T, Jonnalagadda J, Vineeth S, Niranjan V, Muthusamy B, Gandhi TKB, Gronborg M, et al.: Human Protein Reference Database: Building a biological platform for systems biology. American Journal of Human Genetics 2003, 73(5):429–429.Google Scholar
- Chen JYS, et al.: HAPPI: an Online Database of Comprehensive Human Annotated and Predicted Protein Interactions. BMC Genomics 2009, 10(Suppl 1):S16. 10.1186/1471-2164-10-S1-S16PubMed CentralView ArticlePubMedGoogle Scholar
- von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M, Jouffre N, Huynen MA, Bork P: STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res 2005, (33 Database):D433–437.
- Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH: PID: the Pathway Interaction Database. Nucleic Acids Research 2009, 37: D674-D679. 10.1093/nar/gkn653PubMed CentralView ArticlePubMedGoogle Scholar
- Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, de Bono B, Garapati P, Hemish J, Hermjakob H, Jassal B, et al.: Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Research 2009, 37: D619-D622. 10.1093/nar/gkn863PubMed CentralView ArticlePubMedGoogle Scholar
- Pathway Commons[http://www.pathwaycommons.org/pc/home.do]
- Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A: PANTHER: a library of protein families and subfamilies indexed by function. Genome Res 2003, 13(9):2129–2141. 10.1101/gr.772403PubMed CentralView ArticlePubMedGoogle Scholar
- Protein Lounge[http://www.proteinlounge.com/]
- Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C: WikiPathways: Pathway editing for the people. Plos Biology 2008, 6(7):1403–1407. 10.1371/journal.pbio.0060184View ArticleGoogle Scholar
- Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD: Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 2005, 6(1):R2. 10.1186/gb-2004-6-1-r2PubMed CentralView ArticlePubMedGoogle Scholar
- Darvish A, Najarian K: Prediction of regulatory pathways using mRNA expression and protein interaction data: application to identification of galactose regulatory pathway. Biosystems 2006, 83(2–3):125–135. 10.1016/j.biosystems.2005.06.013View ArticlePubMedGoogle Scholar
- Romero PR, Karp PD: Using functional and organizational information to improve genome-wide computational prediction of transcription units on pathway-genome databases. Bioinformatics 2004, 20(5):709–717. 10.1093/bioinformatics/btg471View ArticlePubMedGoogle Scholar
- Frohlich H, Fellmann M, Sultmann H, Poustka A, Beissbarth T: Predicting pathway membership via domain signatures. Bioinformatics 2008, 24(19):2137–2142. 10.1093/bioinformatics/btn403PubMed CentralView ArticlePubMedGoogle Scholar
- Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A, et al.: IntAct: an open source molecular interaction database. Nucleic Acids Research 2004, 32: D452-D455. 10.1093/nar/gkh052PubMed CentralView ArticlePubMedGoogle Scholar
- Luciano JS: PAX of mind for pathway researchers. Drug Discovery Today 2005, 10(13):937–942. 10.1016/S1359-6446(05)03501-4View ArticlePubMedGoogle Scholar
- van Iersel MP, Kelder T, Pico AR, Hanspers K, Coort S, Conklin BR, Evelo C: Presenting and exploring biological pathways with PathVisio. Bmc Bioinformatics 2008, 9: 399. 10.1186/1471-2105-9-399PubMed CentralView ArticlePubMedGoogle Scholar
- Cerami EG, Bader GD, Gross BE, Sander C: cPath: open source software for collecting, storing, and querying biological pathways. Bmc Bioinformatics 2006, 7: 497. 10.1186/1471-2105-7-497PubMed CentralView ArticlePubMedGoogle Scholar
- Schoof H, Ernst R, Nazarov V, Pfeifer L, Mewes HW, Mayer KF: MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource for plant genomics. Nucleic Acids Res 2004, (32 Database):D373–376. 10.1093/nar/gkh068
- Lyne R, Smith R, Rutherford K, Wakeling M, Varley A, Guillier F, Janssens H, Ji W, McLaren P, North P, et al.: FlyMine: an integrated database for Drosophila and Anopheles genomics. Genome Biol 2007, 8(7):R129. 10.1186/gb-2007-8-7-r129PubMed CentralView ArticlePubMedGoogle Scholar
- Balmain A, Gray J, Ponder B: The genetics and genomics of cancer. Nature Genetics 2003, 33(3 s):238–244. 10.1038/ng1107View ArticlePubMedGoogle Scholar
- Nakshatri H, Badve S: FOXA1 in breast cancer. Expert reviews in molecular medicine 2009, 11: e8. 10.1017/S1462399409001008View ArticlePubMedGoogle Scholar
- Wirapati P, Sotiriou C, Kunkel S, Farmer P, Pradervand S, Haibe-Kains B, Desmedt C, Ignatiadis M, Sengstag T, Schütz F, et al.: Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Research: BCR 2008, 10(4):R65. 10.1186/bcr2124PubMed CentralView ArticlePubMedGoogle Scholar
- Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, Ballinger DG, Struewing JP, Morrison J, Field H, Luben R: Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 2007, 447(7148):1087–1095. 10.1038/nature05887PubMed CentralView ArticlePubMedGoogle Scholar
- Gold B, Kirchhoff T, Stefanov S, Lautenberger J, Viale A, Garber J, Friedman E, Narod S, Olshen AB, Gregersen P: Genome-wide association study provides evidence for a breast cancer risk locus at 6q22. 33. Proc Natl Acad Sci U S A 2008, 105(11):4340–4345. 10.1073/pnas.0800441105PubMed CentralView ArticlePubMedGoogle Scholar
- Huan T, Sivachenko A, Harrison S, Chen JY: ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining. BMC bioinformatics 2008, 9(Suppl 9):S5. 10.1186/1471-2105-9-S9-S5PubMed CentralView ArticlePubMedGoogle Scholar
- Shimizu S, Kondo M, Miyamoto Y, Hayashi M: Foxa (HNF3) up-regulates vitronectin expression during retinoic acid-induced differentiation in mouse neuroblastoma Neuro2a cells. Cell Struct Funct 2002, 27(4):181–188. 10.1247/csf.27.181View ArticlePubMedGoogle Scholar
- Williamson EA, Wolf I, O'Kelly J, Bose S, Tanosaki S, Koeffler HP: BRCA1 and FOXA1 proteins coregulate the expression of the cell cycle-dependent kinase inhibitor p27(Kip1). Oncogene 2006, 25(9):1391–1399. 10.1038/sj.onc.1209170View ArticlePubMedGoogle Scholar
- Sacerdoti D, Gatta A, McGiff JC: Role of cytochrome P450-dependent arachidonic acid metabolites in liver physiology and pathophysiology. Prostaglandins & Other Lipid Mediators 2003, 72(1–2):51–71. 10.1016/S1098-8823(03)00077-7View ArticleGoogle Scholar
- Spector AA, Fang X, Snyder GD, Weintraub NL: Epoxyeicosatrienoic acids (EETs): metabolism and biochemical function. Progress in Lipid Research 2004, 43(1):55–90. 10.1016/S0163-7827(03)00049-3View ArticlePubMedGoogle Scholar
- Cousineau I, Abaji C, Belmaaza A: BRCA1 regulates RAD51 function in response to DNA damage and suppresses spontaneous sister chromatid replication slippage: Implications for sister chromatid cohesion, genome stability, and carcinogenesis. Cancer Research 2005, 65(24):11384–11391. 10.1158/0008-5472.CAN-05-2156View ArticlePubMedGoogle Scholar
- Tarsounas M, Davies D, West SC: BRCA2-dependent and independent formation of RAD51 nuclear foci. Oncogene 2003, 22(8):1115–1123. 10.1038/sj.onc.1206263View ArticlePubMedGoogle Scholar
- Ignatoski KMW, Livant DL, Markwart S, Grewal NK, Ethier SP: The role of phosphatidylinositol 3'-kinase and its downstream signals in erbB-2-mediated transformation. Molecular Cancer Research 2003, 1(7):551–560.Google Scholar
- Liang J, Zubovitz J, Petrocelli T, Kotchetkov R, Connor MK, Han K, Lee JH, Ciarallo S, Catzavelos C, Beniston R, et al.: PKB/Akt phosphorylates p27, impairs nuclear import of p27 and opposes p27-mediated G1 arrest. Nature Medicine 2002, 8(10):1153–1160. 10.1038/nm761View ArticlePubMedGoogle Scholar
- Rubin M, Fenig E, Rosenauer A, Menendezbotet C, Achkar C, Bentel JM, Yahalom J, Mendelsohn J, Miller WH: 9-Cis Retinoic Acid Inhibits Growth of Breast-Cancer Cells and down-Regulates Estrogen-Receptor Rna and Protein. Cancer Research 1994, 54(24):6549–6556.PubMedGoogle Scholar
- Nakshatri H, Badve S: FOXA1 as a therapeutic target for breast cancer. Expert Opinion on Therapeutic Targets 2007, 11(4):507–514. 10.1517/1472822.214.171.1247View ArticlePubMedGoogle Scholar
- Ziogas D, Liakakos T, Lykoudis E, Fatourou E, Roukos DH: Exploring the role of BRCA1, BRCA2 and RAD51 as biomarkers for breast cancer. Radiother Oncol 2009, 90(1):161–162. 10.1016/j.radonc.2008.02.020View ArticlePubMedGoogle Scholar
- Chang BL, Zheng SL, Isaacs SD, Wiley KE, Turner A, Li G, Walsh PC, Meyers DA, Isaacs WB, Xu J: A polymorphism in the CDKN1B gene is associated with increased risk of hereditary prostate cancer. Cancer Res 2004, 64(6):1997–1999. 10.1158/0008-5472.CAN-03-2340View ArticlePubMedGoogle Scholar
- Inceoglu B, Schmelzer KR, Morisseau C, Jinks SL, Hammock BD: Soluble epoxide hydrolase inhibition reveals novel biological functions of epoxyeicosatrienoic acids (EETs). Prostaglandins & Other Lipid Mediators 2007, 82(1–4):42–49. 10.1016/j.prostaglandins.2006.05.004View ArticleGoogle Scholar
- Sinal CJ, Miyata M, Tohkin M, Nagata K, Bend JR, Gonzalez FJ: Targeted disruption of soluble epoxide hydrolase reveals a role in blood pressure regulation. Journal of Biological Chemistry 2000, 275(51):40504–40510. 10.1074/jbc.M008106200View ArticlePubMedGoogle Scholar
- Yu ZG, Xu FY, Huse LM, Morisseau C, Draper AJ, Newman JW, Parker C, Graham L, Engler MM, Hammock BD, et al.: Soluble epoxide hydrolase regulates hydrolysis of vasoactive epoxyeicosatrienoic acids. Circulation Research 2000, 87(11):992–998.View ArticlePubMedGoogle Scholar
- Bittar M, Happle R, Grzeschik KH, Leveleki L, Hertl M, Bornholdt D, Konig A: CHILD syndrome in 3 generations – The importance of mild or minimal skin lesions. Archives of Dermatology 2006, 142(3):348–351. 10.1001/archderm.142.3.348View ArticlePubMedGoogle Scholar
- NetPath – Signal Transduction Pathways[http://www.netpath.org/]
- Wu CH, Apweiler R, Bairoch A, Natale DA, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, et al.: The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic Acids Res 2006, (34 Database):D187–191. 10.1093/nar/gkj161
- Wu X, Chowbina SR, Li PM, Pandey R, Kasamsetty HN, Chen JY: Characterizing Mergeability of Human Molecular Pathways. , in press.
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