IntegromeDB: an integrated system and biological search engine
© Baitaluk et al; licensee BioMed Central Ltd. 2012
Received: 19 October 2011
Accepted: 19 January 2012
Published: 19 January 2012
With the growth of biological data in volume and heterogeneity, web search engines become key tools for researchers. However, general-purpose search engines are not specialized for the search of biological data.
Here, we present an approach at developing a biological web search engine based on the Semantic Web technologies and demonstrate its implementation for retrieving gene- and protein-centered knowledge. The engine is available at http://www.integromedb.org.
The IntegromeDB search engine allows scanning data on gene regulation, gene expression, protein-protein interactions, pathways, metagenomics, mutations, diseases, and other gene- and protein-related data that are automatically retrieved from publicly available databases and web pages using biological ontologies. To perfect the resource design and usability, we welcome and encourage community feedback.
Keywordsdata integration search engine biological ontologies
Diverse web resources and databases, such as UniProt , GenBank , GeneCards , RGD , MGD , and many others, catalog various properties of genes, ranging from their mapped chromosomal coordinates to the enzymatic function of the proteins they encode. Scientists have to visit many of these databases or web sites for each gene in their candidate gene list; they must learn and remember how to navigate various web sites, each of which accepts different sets of gene identifiers (Entrez Gene, Ensembl, Refseq, UniGene, and other), thus making the navigation difficult and time-consuming. Although these resources are highly informative individually, the collection of available content has more power, if provided in an integrated, unified, centralized context indexed in a robust manner. Although currently there is no single resource that completely describes everything that a researcher might want to learn about a specific gene, a few integrative approaches towards this goal have been developed and include BioGPS , Ondex , NIF , and several databases for cataloging web resources, such as PathGuide  and MouseBook . The integrated and comprehensive use of biological information is hampered by the large number of available databases and their fragmentation. ID/naming problems and conflicts in biological data pose additional obstacles towards comprehensive biological data integration. Because of the diverse history of these databases and resources, integration with commonly used molecular database resources, such as NCBI's Entrez , is done on a case-by-case basis. At the same time, new online resources are continually being developed and staying abreast of these tools and evaluating their utility is a time-consuming and recurring task.
Web search engines, such as Google, excel at finding a needle in a haystack: a single fact, a single definitive web page. Often, however, the user's objective is not to find a single fact, but to explore all available data concerning a specific genes or proteins. For example, a person diagnosed with diabetes might want to learn all known scientific data about genes associated with this disease. Or, for a researcher who wants to learn 'all about p53 gene/protein', it is much easier to get the information centralized in one place, instead of searching the whole Internet for this information. Even if the researcher knows exactly what to look for, for example, specific post-translational modifications in p53 protein that result in different types of cancer, he/she still might need to browse many different web resources in search of this information.
Here, we propose a solution to the problem of searching biological data, specifically gene/protein-centered data, via semantic integration of data from a broad collection of molecular biology resources and presentation of these data on one web page for each searchable gene/protein. Data have been automatically integrated from more than a thousand databases listed in the Nucleic Acid Research database list , as well as from PubMed, Wikipedia, and millions of public web sites that are referenced from these databases. The presented search engine, http://www.integromedb.org, has been designed for browsing available data on gene regulation, gene expression, protein-protein interactions, pathways, metagenomics, mutations, diseases, and many other gene- and protein-related data. IntegromeDB web page is linked to BiologicalNetworks http://www.biologicalnetworks.org, which is a Java web-start application designed for in-depth analysis of biological data, building and visualization of gene regulatory and protein-protein interaction networks; the presented web resource and BiologicalNetworks both rely on the same integrated database . Application Programming Interface (API) available at http://integromedb.org/api.jsp provides access to the most features available through the web application and can be used by third-party users and resources. The batch search and retrieval options are also provided at the IntegromeDB web site, http://www.integromedb.org.
Construction and content
The major architectural blocks of the system are a web-crawler, archive server, data integration server, internal database, and ontological mediator (Figures 1 and 1S1). The crawler automatically searches public databases, PubMed, and web resources for biological data and extracts them from texts and tables. The system accepts the user's data that can be integrated together with public data. The Data Integration Server accepts external data, semantically mapping them to the schema of the Internal Database, and injects the external data into the database. This allows IntegromeDB to tap into the Deep Web, the portion of the web that is concealed behind web forms. Some estimates have pegged the size of the Deep Web at up to 500 times larger than the Surface Web . Briefly, the procedure to process the data behind web forms works as follows. Starting from an initial prediction of candidate keywords by simple ontological analysis of the text on the page (usually it is ether gene, protein, drug, pathway names), the form is tested and if it returns valid results, the common URL pattern of meaningful pages (for example 'http://www.ncbi.nlm.nih.gov/gene/?term=') is analyzed and further keywords will be extracted from the resulting pages. The iterative process is continued until either no new candidate keywords can be extracted or no more new web pages can be retrieved for a given resource.
Search on the IntegromeDB web page can be executed via keyword, wild card, or multiple word queries. Genes/proteins can be queried using either the gene name (the official HGNC  name, Entrez Gene , Ensembl ), Affymetrix identifier, current UniGene/UniProt  cluster identifier, or GenBank  accession number of a sequence (Refseq ID) associated with the gene through UniGene/UniProt (Figure 1D). We have assembled the largest collection of gene aliases available on the web by combining synonymous data from more than a thousand databases. IntegromeDB can therefore accept practically any known ID as input. To display data for a specific gene (Figures 1F, 2), the user must first select a gene from the search result list (Figure 1E). The Search results are divided into two groups: Exact Search "p53" and Relevant to "p53". The first group contains genes which names contain the query term (p53); the second group contains genes which were found in the same publications or the same database records with the query term.
One of the important features of IntegromeDB is the ability to simultaneously extract data for multiple genes in a batch, thus eliminating the need for laborious cross-referencing of data from external databases. The batch search is particularly useful for functional genomics studies, where it is necessary to regularly update annotations associated with genes/proteins being examined. For example, researchers interested in the mapped position or subcellular localization of a list of genes can extract these attributes and perform further analyses, assessing the enrichment of transcription factor binding sites or a certain functional attributes within clusters of genes. List of genes can be input as a text file via uploading on the server or by pasting it into a search box. The batch search is currently limited to 1000 genes.
The cornerstone of the IntegromeDB search engine is the IntegromeDB ontology and Categorization Engine (ICE) (Figure S1 in Additional file 1). The IntegromeDB ontology, which is described in detail in [13, 14], integrates over a hundred OBO ontologies and consists of millions of terms organized as a directed acyclic graph (DAG), reflecting 'is-a', 'has-a', and 'part-of' relationships; for example, 'p53 is-a transcription factor'. Given a user query, ICE determines the nodes in the ontology that are most closely connected with a query. For example, for the 'p53' query, ICE first determines that p53 is a tumor suppressor, which is related to carcinogenesis and cancer pathogenesis. It also determines that p53 is a family of transcription factors related to cell signaling and playing role in the immune system and cell proliferation.
where K1-K4 - empirical coefficients (K1 = 0.2; K2 = 0.5; K3 = 0.4; K4 = 0.8); PO - frequency of a term A in the ontology which the term A belongs to; PP - frequency of a term A in the document X, this value corresponds to the Lucene keyword search score ; PT - frequency of the term A in different HTML tag fields of the web page (document) X; PR - PageRank  of the document X. In the process of executing the query containing the term A, ICE retrieves the top one thousand ranked documents.
IntegromeDB web interface
IntegromeDB report page uses a two-dimensional newspaper-like layout (Figure 2) rather than as a search results page as in standard search engines (e.g., Google). Data are grouped by data sources and similarity and sorted in each group by the relevance to the query gene/protein as is shown in Figures 2, 3, 4 and 5. The report page also provides a list of topics related to the query gene/protein (Figure 4). The links to the corresponding data sources are provided. Among data displayed for each gene/protein on the left panel of the report page (Figure 3) are aliases and external IDs, ontological terms, chromosome localization with links to NCBI and UCSC genome browsers, orthologs/homologs from multiple organisms, protein and genomic sequences, and related publications. On the right panel of the report page (Figures 4, 5) are shown interaction data and pathways (from KEGG , NCI , REACTOME , BioCARTA ), experiments (e.g., expression, metabolomics and proteomics data from GEO  and ArrayExpress ), miRNA data (from miRBase  and microRNA.org ), images (e.g., protein structures from PDB  and Wikipedia ), and relative mRNA expression frequencies derived from various cell and tissue types (from descriptions/metadata of experiments in GEO and ArrayExpress).
For example, for the query 'p53', the report page (Figures 2, 3, 4 and 5) describes that p53 is involved in apoptosis, cancer, prostoglandin metabolism pathway, and MAPK, Wnt, cell cycle and other canonical pathways. Detailed information for each pathway, including genes, proteins, and small molecules involved in it is provided. For example, for prostoglandin metabolism pathway, the user can learn that it is involved in metabolizing lipids into prostaglandins and plays an important role in pain and inflammation; that the protein encoded by human PTGS1 gene is involved in the conversion of prostaglandin PGG2 into inflammation-causing prostaglandin PGH2; and aspirin has been shown to bind to the PTGS1 gene product (prostaglandin-endoperoxide synthase 1), blocking the ability of this enzyme to produce PGH2 and thereby reducing pain and inflammation.
For each gene/protein, information retrieved from web pages is clustered by data sources (Figure 3). The user can scan the content of the web page by rolling over the respective term (e.g. 'Binary Interactions', 'Sequence Annotation', 'Structure') at the bottom of the snippet under 'In this topic'. For example, for the 'p53' query, among the most relevant (valuable) resources were OMIM, UniProt, UniGene, GenBank, PubMed, Wikipedia, GeneCards, InterPro, p53.free.fr (mutation database), SYSTERS (protein families database), tp53.org. They were followed by (accessible via clicking the 'Show More Pages' button) EMBL/EBI, MGD, STRING, WikiGenes, Genetic Atlas, CancerIndex, ProteinAtlas, KEGG, UCSC genome Browser, HAGR (Human Ageing Genomic Resource), SwissProt, PharmGKB and others.
In contrast to table data stored in listed and well-maintained databases, the tables published in the PubMed articles, as well as separate tables distributed across the web, are barely searchable by any search engine. Here, these data are integrated and become searchable using the same approach that is applied to the tables in the databases. Specifically, it concerns tabular data on gene regulatory regions, gene/protein interactions, and gene expression experiments.
Integration with other resources
One of the central aims of the IntegromeDB is to maintain cross-connectivity and integration with other public resources in a user-friendly manner. Therefore, we provide the programmatic access to our SQL database that is accessible via the following routes. First, the integrated content of IntegromeDB is available via the IntegromeDB API, which is implemented in Java. Through an XML-RPC service, API provides functions to access programmatically most of the features available in the IntegromeDB web interface, such as retrieving aliases, promoter sequences, or transcriptional regulators for a set of genes. Example code of using API and access to the XML-RPC service are available at http://integromedb.org/api.jsp ('API XML-RPC' tab). Second, if the external user/resource wants to visualize retrieved object(s) (interaction network of a protein, promoter region of a gene, microarray experiments for a set of genes, etc.) on his web resource using the BiologicalNetworks integrated research environment  he/she should use API access described at the http://integromedb.org/api.jsp 'API BiologicalNetworks' tab. Thus IntegromeDB/BiologicalNetworks maintains mutual cross-referencing with other web resources, that is not limited to simple text-based HTML links, but also enables partner websites to embed visualization of the BiologicalNetworks objects within their own web pages.
Partner websites or third-party software programs can choose to embed the entire IntegromeDB website into their own software. Thus, an IntegromeDB 'plugin' can be established at the BioGPS  portal, which provides 'plug-ins' through which the users can connect any number of external websites into freely configurable screen layouts.
Discussion and Conclusions
Centralized and publicly available resources and search engines for integrated biological data are critical in enabling biologists to efficiently analyze genome-scale data. To meet this demand, we developed IntegromeDB, the general purpose unifying resource and search engine that can take the exploration of molecular biology data to the next level. IntegromeDB uses a hybrid approach to the Deep Web that combines elements of the crawl and federated search and data preprocessing approaches. It follows the trend established by other integrative resources, BioGPS  and Entrez , for example, that aim at presenting available information in a gene-centered manner. However, the technology behind IntegromeDB differs from that of other resources as it relies on data integration via automatic establishment of Object-Object Property relationships rather than on hyperlinks and manual curation. This approach is scalable in respect of how many data sources can be integrated (the number can be limited only by the hardware availability) and relatively inexpensive to develop and maintain as we deliberately keep human intervention at a minimum. However, as any automatic approach it has the drawback that unrelated data can be occasionally retrieved. With more usage of the site and more errors reported, the search algorithm will be improved. We encourage the users to report the errors and provide critique using the "Provide Feedback" button at the top right of the search results page (Figure 2).
To further enhance the ability of researchers to extract and manipulate the data, we will continue to provide the access to IntegromeDB through the API and data integration tools available in BiologicalNetworks making the most often used tools implemented as web services, thus eliminating the necessity to download BiologicalNetworks. We will also work on providing execution of SPARQL queries on the IntegromeDB data and create a SPARQL to SQL adaptor, using specifically designed dictionaries (that provide description of triples along with exact mappings onto tables).
We plan to continue extending the resource by adding new data sources and more experimental data and further developing the search engine's usability and speed. To improve the resource design and usability, we will rely on external experts (subject of funds availability) and we welcome and encourage community feedback that can be provided using the "Provide Feedback" button at the top right of the search results page. The speed of the presented search engine depends on both software and hardware; and while we will continue optimizing the search algorithms, we also plan to utilize a new hardware, specifically, a supercomputer Gordon that will become available at the San Diego Supercomputer Center in 2012.
Availability and requirements
Project name: IntegromeDB
Project home page: http://www.integromedb.org
Operating system(s): Platform independent. Tested on the Internet Explorer 8, FireFox 8, Google Chrome web browsers on Windows 2000/XP/Vista/7, Linux/Ubuntu/Redhat, and MacOSX OS.
Programming language: Java
Other requirements: Java 1.6 for running BiologicalNetworks program;
License: GNU GPL version 3
Any restrictions to use by non-academics: none
This work was supported by the National Institute of Health grants R01GM084881 to MB and R01GM085325 to JP.
- Wu C, Bairoch A, Apweiler R, Natale DA, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Mazumder R, O'Donovan C, Redaschi N: The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic Acids Res. 2006, 34: D187-191. 10.1093/nar/gkj161.PubMed CentralView ArticlePubMedGoogle Scholar
- Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW: GenBank. Nucleic Acids Res. 2011, 39: 32-7.View ArticleGoogle Scholar
- Safran M, Dalah I, Alexander J, Rosen N, Iny Stein T, Shmoish M, Nativ N, Bahir I, Doniger T, Krug H, Sirota-Madi A, Olender T, Golan Y, Stelzer G, Harel A, Lancet D: GeneCards Version 3: the human gene integrator. Database. 2010, doi: 10.1093/database/baq020Google Scholar
- Twigger SN, Shimoyama M, Bromberg S, Kwitek AE, Jacob HJ, RGD Team: The Rat Genome Database, update 2007 - easing the path from disease to data and back again. Nucleic Acids Res. 2007, 35: 658-62. 10.1093/nar/gkl988.View ArticleGoogle Scholar
- Blake JA, Bult CJ, Kadin JA, Richardson JE, Eppig JT, the Mouse Genome Database Group: The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics. Nucleic Acids Res. 2011, 39 (suppl 1): 842-848.View ArticleGoogle Scholar
- Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL, Haase J, Janes J, Huss JW, Su AI: BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 2009, 10 (11): R130-10.1186/gb-2009-10-11-r130.PubMed CentralView ArticlePubMedGoogle Scholar
- Köhler J, et al: Graph-based analysis and visualization of experimental results with Ondex. Bioinformatics. 2006, 22 (11): 1383-1390. 10.1093/bioinformatics/btl081.View ArticlePubMedGoogle Scholar
- Gupta A, Bug W, Marenco L, Qian X, Condit C, Rangarajan A, Müller HM, Miller PL, Sanders B, Grethe JS, Astakhov V, Shepherd G, Sternberg PW, Martone ME: Federated Access to Heterogeneous Information Resources in the Neuroscience Information Framework (NIF). Neuroinformatics. 2008, 6 (3): 205-17. 10.1007/s12021-008-9033-y.PubMed CentralView ArticlePubMedGoogle Scholar
- Bader GD, Cary MP, Sander C: Pathguide: a pathway resource list. Nucleic Acids Res. 2006, 34: D504-6. 10.1093/nar/gkj126.PubMed CentralView ArticlePubMedGoogle Scholar
- Blake A, Pickford K, Greenaway S, Thomas S, Pickard A, Williamson CM, Adams NC, Walling A, Beck T, Fray M, Peters J, Weaver T, Brown SD, Hancock JM, Mallon AM: MouseBook: an integrated portal of mouse resources. Nucleic Acids Res. 2010, 38: D593-9. 10.1093/nar/gkp867.PubMed CentralView ArticlePubMedGoogle Scholar
- Maglott D, Ostell J, Pruitt KD, Tatusova T: Entrez Gene: gene-centered information at NCBI. Nucl Acids Res. 2011, 39: D52-D57. 10.1093/nar/gkq1237.PubMed CentralView ArticlePubMedGoogle Scholar
- Galperin MY, Cochrane GR: The 2011 Nucleic Acids Research Database Issue and the online Molecular Biology Database Collection. Nucleic Acids Res. 2011, 39: D1-6. 10.1093/nar/gkq1243.PubMed CentralView ArticlePubMedGoogle Scholar
- Kozhenkov S, Dubinina Y, Sedova M, Gupta A, Ponomarenko J, Baitaluk M: BiologicalNetworks 2.0--an integrative view of genome biology data. BMC Bioinformatics. 2010, 11: 610-10.1186/1471-2105-11-610.PubMed CentralView ArticlePubMedGoogle Scholar
- Baitaluk M, Ponomarenko J: Semantic Integration of Data on Transcriptional Regulation. Bioinformatics. 2010, 26 (13): 1651-1661. 10.1093/bioinformatics/btq231.PubMed CentralView ArticlePubMedGoogle Scholar
- Kozhenkov S, Sedova M, Dubinina Y, Gupta A, Ray A, Ponomarenko J, Baitaluk M: BiologicalNetworks--tools enabling the integration of multi-scale data for the host-pathogen studies. BMC Syst Biol. 2011, 5: 7-10.1186/1752-0509-5-7.PubMed CentralView ArticlePubMedGoogle Scholar
- RDF. [http://www.w3.org/RDF/]
- Good BM, Wilkinson MD: The Life Sciences Semantic Web is full of creeps!. Brief Bioinform. 2006, 7: 275-286. 10.1093/bib/bbl025.View ArticlePubMedGoogle Scholar
- Wright A: Searching the Deep Web. CACM. 2008, 51 (10): 14-15.View ArticleGoogle Scholar
- Wang Y, Bolton E, Dracheva S, Karapetyan K, Shoemaker BA, Suzek TO, Wang J, Xiao J, Zhang J, Bryant SH: An overview of the PubChemBioAssay resource. Nucleic Acids Res. 2010, 38: D255-66. 10.1093/nar/gkp965.PubMed CentralView ArticlePubMedGoogle Scholar
- Seal RL, Gordon SM, Lush MJ, Wright MW, Bruford EA: genenames.org: the HGNC resources in 2011. Nucleic Acids Res. 2011, 39: D519-9.View ArticleGoogle Scholar
- Hubbard TJP, et al: Ensembl 2007. Nucleic Acids Res. 2007, 35: D610-D617. 10.1093/nar/gkl996.PubMed CentralView ArticlePubMedGoogle Scholar
- Apache Lucene project. [http://lucene.apache.org]
- Page L, Brin S, Motwani R, Winograd T: The PageRank citation ranking: Bringing order to the Web. 1999Google Scholar
- Levenshtein VI: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady. 1966, 10: 707-10.Google Scholar
- Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010, 38: D355-D360. 10.1093/nar/gkp896.PubMed CentralView ArticlePubMedGoogle Scholar
- Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH: PID: The Pathway Interaction Database. Nucleic Acids Res. 2009, 37: D674-9. 10.1093/nar/gkn653.PubMed CentralView ArticlePubMedGoogle Scholar
- Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, de Bono B, Garapati P, Hemish J, Hermjakob H, Jassal B, Kanapin A, Lewis S, Mahajan S, May B, Schmidt E, Vastrik I, Wu G, Birney E, Stein L, D'Eustachio P: Reactome knowledgebase of biological pathways and processes. Nucleic Acids Res. 2009, 37: D619-22. 10.1093/nar/gkn863.PubMed CentralView ArticlePubMedGoogle Scholar
- Biocarta pathways. [http://www.biocarta.com]
- Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Holko M, Ayanbule O, Yefanov A, Soboleva A: NCBI GEO: archive for functional genomics data sets--10 years on. Nucleic Acids Res. 2011, 39: D1005-10. 10.1093/nar/gkq1184.PubMed CentralView ArticlePubMedGoogle Scholar
- Parkinson , et al: ArrayExpress update--from an archive of functional genomics experiments to the atlas of gene expression. Nucl Acids Res. 2009, 37: D868-72. 10.1093/nar/gkn889.PubMed CentralView ArticlePubMedGoogle Scholar
- Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011, 39: D152-D157. 10.1093/nar/gkq1027.PubMed CentralView ArticlePubMedGoogle Scholar
- Betel D, Wilson M, Gabow A, Marks DS, Sander C: The microRNA.org resource: targets and expression. Nucleic Acids Res. 2008, 36: D149-53.PubMed CentralView ArticlePubMedGoogle Scholar
- Protein Data Bank (PDB). [http://www.rcsb.org]
- Wikipedia. [http://www.wikipedia.org]
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.