Biological information curated for these data elements includes disease associations, phenotype data, pathways, molecular functions, biological processes, and cellular components. Curators are involved in acquiring and validating data elements, attaching biological information to elements, identifying pathways, and making connections among data types. The following glimpse at the curators and curation processes at RGD is designed to illustrate who curators are, what they do, where their results can be seen, and why their efforts make researchers' lives easier.www.geneontology.org) guidelines and has adapted similar guidelines for other types of biological information. Biological annotations are based on experimental results published in peer-reviewed journals. New curators are trained by a senior curator and do test curation projects for several months before curating independently. Curators begin with a single data type, such as gene curation, and a single type of ontology-based biological annotation in order to develop adequate skills in this area before training in other data types. In addition to the curation manual and one-on-one training, there is a weekly curation meeting in which standards, policies, and new data types are discussed in order to maintain existing standards and develop new ones. Standards for new data types are developed through consultations with researchers having expertise in those areas, other model organism database groups, and the curation group at RGD. Inter-curator disagreements, which do occur even between highly experienced curators, are discussed in weekly meetings with pertinent examples from the literature and, when necessary, consultation with outside experts. Consensus decisions are added to the curation manual. There are additional mechanisms through which curators can submit new terms to specific ontologies, often hosted at SourceForge to accommodate data not covered by current procedures. Periodically all curators at RGD curate the same paper and these results are compared and discussed in the meeting to ensure consistency across curators. It takes approximately 1 year for a curator to become competent in curating one area of data and several years for real expertise to develop. While two of RGD's curators are new to the field, the other five have between 2 and 8.5 years of curation experience with an average of 5.7 years. Traits that RGD curators cite as valuable in being a curator include broad knowledge of scientific research and research methods, attention to detail, perseverance, and the ability to collaborate as a group. Many point out that curation is a rewarding alternative career for scientists to one in bench research. While curators are trained to serve in multiple roles and handle various types of data, they do specialize in particular areas to promote standardization and facilitate acquisition of greater knowledge and expertise. Following is a discussion of curation and other functions performed by RGD curators with an emphasis on what is done, where these results can be seen, and why these efforts are important to researchers.Currently, RGD has seven full-time curators who bring diverse educational specialties and research expertise to the curation process. Five of the curators have PhDs and two have master's degrees. Educational specialties include molecular and cellular biology, physiology, biochemistry, microbiology, experimental pathology, and organic chemistry. As is common in the field of curation, the RGD curators bring a vast amount of experience in wet lab research ranging from 9 to 26 years with a mean of 16.9 years. They are well versed in research methods for cell and tissue culture, protein biochemistry, molecular biology, large and small animal physiology and surgery, developmental biology, infectious disease, microbiology, virology, biophysics, and bio-computation. Because of the wide array of experiences of curators, RGD developed a comprehensive training program and curation manual to ensure that curators follow rigorous standards in identifying data elements, assigning nomenclature, and annotating biological information to genes, QTLs, and strains. RGD adheres to the Gene Ontology (GO) , and the HUGO (Human Genome Organization) Gene Nomenclature Committee HGNC; . Other pApproximately 1,500 gene conflicts are resolved annually as more sequence becomes available, gene models are refined, and orthology relationships reviewed and validated. These comprehensive procedures ensure proper gene identification, presented on gene report pages available to users on the RGD Web site , and allQTLs and strains are generally extracted from the literature or are submitted to RGD by researchers. Using data submission forms on the RGD Web site, researchers specify whether the work they are submitting is published or not. If the latter is the case, they can elect to have their submitted data displayed only after its publication. Credit is given for data displayed prior to publication as a reference indicating data submitted via personal communication by the researcher to RGD. After publication, credit is given on the report page as the cited reference containing the data. As with genes, the curators ensure identity and determine definitions and descriptions for QTLs and strains, as well as assign nomenclature. Curators record the flanking and peak QTL markers to ensure accurate genomic positions, and indicate the associated traits, as well as the strains used in the cross. This provides an unambiguous identity for each QTL, allowing researchers to view QTLs on map tools and to differentiate one QTL from another knowing with confidence whether a QTL referenced in one paper is the same one mentioned in another paper. Curators also provide nomenclature and history for strains and substrains to identify the correct genetic background. As next generation sequencing makes it possible to produce genome sequences for individual strains, this accurate tracking of strains and their history will become more important as researchers attempt to link phenotype data from multiple studies with the correct genome data.http://www.ncbi.nlm.nih.gov/Literature/) reveals there are over 1,267,000 rat manuscripts. In order to provide functional information for as many genomic elements and strains as possible and to leverage the information in the vast amount of rat literature, RGD has implemented a number of processes. RGD uses four ontologies to standardize biological information for genes, QTLs, and strains. These include the GO Providing comprehensive biological information on rat genes, QTLs, and strains is a formidable task both in number of elements to be curated and the amount of literature available. Of the more than 35,400 genes in RGD, 31,304 have associated sequence data or RefSeqs, and of these, 15,607 are protein-coding with curated RefSeqs On average the curators at RGD extract information from approximately 5,000 papers per year. From these, multiple types of biological information are annotated for approximately 2,000 genes, 180 rat QTLs, and 200 strains per year. While QTL and strain data are curated as papers are published, RGD targets specific gene datasets for curation. These include genes related to specific disease areas (which generate the most literature), gene families or pathways, as well as those done in conjunction with the Gene Ontology Reference Genomes project The RGD curators bring extensive, diverse research backgrounds to the design process for user interfaces and software tools available at the RGD Web site. Curators map out use case scenarios for searches, data analysis tools, and other Web site functions based on the points of view of multiple types of researchers. This allows RGD to create tools and interfaces that fulfill the needs of naïve users as well as seasoned users and bioinformaticians. Curators work closely with the bioinformatics staff to develop the conceptual and functional design of curation software and they also test all new tools and features for usability and accuracy of results before they are made available to the user via links from the RGD home page. In addition, their familiarity with emerging literature, new research techniques, and data types, as well as their interactions with the community often lead to proposals for new data components and tools and new features on existing tools. In other cases, the curator is responsible for the primary design and implementation process. For example, a curator creates and populates the interactive pathway diagrams that RGD is publishing using a content management system and the Ariadne Pathway Studio software. Curators play a major role in the development of all aspects of RGD technology from the home page design to the search functions and accompanying results layouts to the genome tools. To coordinate curation and informatics projects, there is a weekly RGD operations meeting attended by all curators, software developers, the program manager, and co-PIs to address deadlines and priorities. To minimize conflict and ensure timely completion of projects, tracking and documentation is accomplished through Microsoft Project and Mindjet MindManager software. Tool bugs and issues are recorded and tracked through JIRA software. Documentation of requirements, statements of work, and release procedures minimize confusion and conflict between curators and bioinformatics staff.To assess the value to researchers of curation work done for genes, RGD undertook two audits. In the first, a review of a subset of cancer genes showed that 3 to 43 papers were curated per gene with an average of 19 papers per gene. In the second study, for a subset of 20 diabetes genes, more comprehensive statistics were tracked. Over 16,000 papers were returned in searches, 410 abstracts reviewed, and 104 full papers read. This produced an average of 20 abstracts and 5 full papers read per gene. Of these, ontology annotations were made from 3–4 papers per gene. Time spent reading abstracts and full papers averaged 106 min per gene. If we extrapolate for the cancer genes in the first study, this would have meant approximately 380 min for each of these more highly published genes. Although three of the targeted genes did not receive any annotations during the process, the remaining 17 received 195 annotations. Because each annotation is linked to the reference from which it was taken , the usehttp://www.scivee.tv/), a Web site for video publications for research, and videos are available at RGD as well. Curators present tutorials at major conferences such as Experimental Biology, Society of Toxicology, Neuroscience and Rat Genomics and Models, as well as individual rat research laboratories. In addition, RGD is well represented at major conferences such as Biology of Genomes, Genome Informatics, Intelligent Systems for Molecular Biology, and the International Mammalian Genome Conference with presentations and posters highlighting new tools and datasets. Other outreach activities involve contact with individual researchers for nomenclature assignment to genes, QTLs, and strains, as well as construction of customized datasets.An essential part of the curator's job is to provide education and training for RGD users and potential users. This is accomplished in several ways. The RGD Web site contains a “Help” section developed by the curators and which is accessible from all pages. This component contains a Glossary of Terms, general information on how to use the searches and tools, a Frequently Asked Questions (FAQ) section, and a component that walks users through typical use case scenarios. Curators also handle individual questions through the user request system accessible via the Contact Us button on each page, and through telephone calls and the Rat Community Forum. RGD has published seven tutorial videos at SciVee  currently provides access to full text papers from 450 journals plus those submitted under the NIH Public Access Policy. This policy requires authors funded by NIH to submit full papers published in peer-reviewed journal to PubMed Central within 12 months of publication Author submission of data prior to publication. Those rat researchers who submit data to RGD prior to publication receive RGD IDs for their data and nomenclature review and approval. This ensures that the information in their papers is synchronized with that at RGD and is made available to the public at the time of publication.Use of unique identifiers in publications. A time-consuming activity undertaken by curators around the world is verifying identity of genomic data such as genes and QTLs, as well as rat strains. Use of unique identifiers from recognized, international databases and correct nomenclature would make this a much easier task as well as ensure that data are not assigned to the wrong gene, QTL, or strain on the basis of an incorrect identifier used in a publication.Correct identification of organism used. As mentioned above, some papers are rejected for curation because the organism used is not identified.Acknowledgment of their work. Thousands of researchers worldwide use data from databases in their research on a daily basis. Only a small percentage of them acknowledge in their publications or presentations their use of the data and tools made available through the work of curators. Referencing the sources of the data and tools used in publications would provide recognition of the professional accomplishments of curators just as referencing a colleague's paper provides acknowledgement of the use of his or her work.More sophisticated curation software. Each development in curation software at RGD has resulted in substantial increases in productivity and accuracy. The next generation software would allow curators to customize interfaces for particular tasks, allowing them to incorporate the necessary functions in ways most useful to them. It would allow them to query multiple databases simultaneously, incorporate sophisticated text mining tools, and run quality control checks at will.Text mining tools. Text mining tools are becoming more sophisticated and have gone beyond simple literature search tools. Current work has been focusing on identifying and highlighting ontology terms and other data within the papers. Taking this one step further, an example of a dream tool would be one that takes the annotations that already exist for a gene and matches those against emerging literature to identify novel information, more specific information, or data with better evidence and then would alert the curator to update the gene's information.Each time researchers access RGD, they are presented with the results of countless hours of work by curators dedicated to providing accurate, up-to-date data and tools to help them make exciting discoveries. Implementing the developments listed above would contribute to increased coverage, comprehensiveness, and ease of use of RGD and databases worldwide.We wish to thank RGD members Elizabeth A. Worthey, Jeffrey De Pons, Alexander Stoddard, Burcu Bakir Gungor, and Stacy Zacher for their contributions.


PHIDIAS is a web-based database system serving as a centralized source to search, compare and analyse integrated genome sequences, conserved domains and transcriptional data related to pathogen-host interactions. .The Pathogen-Host Interaction Data Integration and Analysis System (PHIDIAS) is a web-based database system that serves as a centralized source to search, compare, and analyze integrated genome sequences, conserved domains, and gene expression data related to pathogen-host interactions (PHIs) for pathogen species designated as high priority agents for public health and biological security. In addition, PHIDIAS allows submission, search and analysis of PHI genes and molecular networks curated from peer-reviewed literature. PHIDIAS is publicly available at  An infectious disease is the result of an interactive relationship between a pathogen and its host. According to estimations of the World Health Organization, infectious diseases caused 14.7 million deaths in 2001, accounting for 26% of the total global mortality [Brucella Bioinformatics Portal (BBP) [Brucella genome. The same strategy can be expanded to include other pathogens, thereby enhancing our ability to conduct comparative studies. The program can be modified to include additional features not yet available in BBP. For example, protein conserved domains  could be listed. The NCBI Conserved Domain Database (CDD) mirrors several collections, including the Protein families database of alignments (Pfam) [Completely sequenced genomic information provides valuable information for gene and protein functions, and intra-organismic processes. Pathogen genome information also lays a foundation for the study of the interactions between host and microbial organisms. Several genome data resources, such as the National Center for Biotechnology Information (NCBI), European Bioinformatics Institute (EBI) and Swiss Institute of Bioinformatics (SIB), are available to the public. However, data obtained from these sources often are not integrated. Lack of such integration prompted us to develop the al (BBP) . This prs (Pfam) , Simple s (Pfam) , and Clus (Pfam) , and thuet al. [While CDD contains conserved domains derived from various eukaryotic and prokaryotic organisms , it is det al.  developeet al. . Howeveret al. .The molecular functions of pathogen and host genes as well as their roles in specific PHI pathways have been extensively studied. Molecules that play important roles in the virulence of pathogens and in the host immune defense are particularly important for PHI. A systematic collation from the literature of these molecules and their functions is lacking. Once PHI-related molecules are collated, the next step is to illustrate molecular interactions and pathways involving these molecules. Existing pathway databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) , BioCyc Experimental methodologies, including microarrays and mass spectrometry, provide abundant sources of gene expression data. Publicly available gene expression data repositories, including the NCBI Gene Expression Omnibus (GEO)  and the Plasmodium falciparum  that permits integration and analysis of genome sequences, curated literature data for general PHI information and PHI networks, and PHI-related gene expression data. PHIDIAS currently targets 42 pathogens. These include most category A, B, and C priority pathogens identified by the National Institute of Allergy and Infectious Diseases (NIAID) and the Centers for Disease Control and Prevention (CDC) in the USA, and other pathogens deemed of high priority with regards to public health, such as the human immunodeficiency virus (HIV) and um Table .PHIDIAS is implemented using a three-tier architecture built on two Dell Poweredge 2580 servers that run the Redhat Linux operating system (Redhat Enterprise Linux ES 4). Users can submit database or analysis queries through the web. These queries are then processed using PHP/Perl/SQL  against a MySQL (version 5.0) relational database . The result of each query is then presented to the user in the web browser. Two servers are scheduled to regularly backup each others' data.Escherichia coli O157:H7 strain Sakai) and non-pathogenic strains  in the same pathogen species. Pacodom is used to search and analyze conserved protein domains of the pathogen genomes. Customized BLAST programs allow users to perform similarity searches on pathogen genome sequences. Curated PHI data are separated into Phinfo, Phigen and Phinet, based on general PHI information, PHI molecules and networks, respectively. PHI gene expression experiments and gene profiles are searched through the Phix database system.PHIDIAS includes six components that search and analyze annotated genome sequences, curated PHI data, and PHI-related gene expression data Figure . PathogePhiDB is the PHIDIAS relational database that integrates different PHIDIAS components. Figure Brucella serves as an example and demonstrates how PHIDIAS can promote Brucella research. Brucella species are Gram-negative, facultative intracellular bacteria that cause brucellosis in humans and animals [B. melitensis, B. suis, B. abortus, and B. canis are human pathogens in decreasing order of severity. Brucella species have been identified as priority agents amenable for use in biological warfare and bioterrorism and are listed as USA NIAID category B priority pathogens. The genomes of B. melitensis strain 16 M [B. suis strain 1330 [B. abortus strain 994-1 [To illustrate the features of data integration and comparative analyses using PHIDIAS, the pathogenic  animals . B. meliain 16 M , B. suisain 1330 , and B. in 994-1  and strain 994-1  have beePathogen genomes serve as the foundation for the study of PHI in the post-genomic era. PGBrowser integrates data from more than 20 different sources, including NCBI, EBI, and The Institute for Genomic Research (TIGR)  among the different Brucella genomes, analyze the gene sequences before and after a particular gene  available at the Generic Software Components for Model Organism Databases (GMOD), a popular genome browser tool because of its portability, simple installation, convenient data input and easy integration with other software programs . The GBrlla spp.  and Pseuruginosa . PGBrowss Figure . One cane Figure , and obtsodC in B. melitensis strain 16 M  models that are commonly used representations of motifs present in biological sequences. However, the PSSM models cover a broad range of organisms and, therefore, it is difficult to compare conserved domains from select priority pathogens. To circumvent this problem, a pathogen-specific protein conserved domains database module called Pacodom was developed. This program contains all possible conserved domains found in the 77 pathogen genomes of 42 pathogens. To build this system, a local reverse-position-specific (RPS) CDD library was constructed based on the CDD conserved domain data downloaded from NCBI . The RPSM models  in the PBrucella). A search for 'phagocytosis' in Pacodom yields 14 domains; 13 domains do not match any protein from any PhiDB pathogen genome  and is involved in a major transferrin-independent iron uptake system in mammals. The Pfam summary does not list any related microbial Nramp proteins. However, a Pacodom search shows Nramp is very common in the bacterial pathogens listed in PHIDIAS. Those 42 proteins containing the Nramp domain come from many bacterial species, such as Brucella spp., Mycobacterium tuberculosis, and Salmonella enterica. Nramp exists in all strains from these bacteria, whether the strain is pathogenic or non-pathogenic. In contrast, Nramp does not exist in the following species: Campylobacter jejuni, Clostridium perfringens, Coxiella burnetii, Francisella tularensis, and Rickettsia prowazekii. The Nramp domain has been investigated in depth in mycobacteria [Salmonella enterica serovar typhimurium (S. typhimurium) requires both of the divalent cation transport systems, MntH (Nramp1 homolog) and SitABCD (putative ABC iron and/or manganese transporter), for full virulence in congenic Nramp1-expressing mice. These results suggest that bacterial Nramp is required for pathogenesis in S. typhimurium and probably other bacteria by synchronizing with other redundant cation transport system(s) to compete for divalent cations with host cells. The role of Brucella Nramp in pathogenesis remains unclear and deserves further analysis. This example demonstrates how Pacadom can be used to find valuable information and form testable hypotheses by comparative analysis of conserved domains.The conserved domain data from completely sequenced pathogenic organisms provide valuable information for comparative analysis of functional roles of pathogen proteins and their involvement in the interactions between host and microbial organisms. For example, conserved domain data can be used to study phagocytosis, a process where host phygocytic cells  engulf pathogen cells , while found in a list of pathogens in Pacodom, is also found in many bacterial species that are not pathogens. Therefore, it may be important for investigators to cross reference PHIDIAS search results against databases that contain both pathogen and non-pathogen species. Since Pacodom includes conserved domains from both pathogenic strains and non-pathogenic strains of the same microbial species, it can be used to find domains shown in pathogenic but not in non-pathogenic strains. For example, a query of 'bacteriophage' in Pacodom results in many conserved domains being found, such as Phage_Mu_Gp45 (pfam06890) and Phage_Mu_F (pfam04233), which exist in pathogenic ogenesis .Gene or protein sequences among different pathogen genomes can be analyzed by different BLAST search approaches. PHIDIAS BLAST uses the latest web server version of BLAST obtained from NCBI . It inclBrucella strains. A blastp search using human Nramp2  as input yields similar hits. The BLAST search results are consistent with the analysis of conserved domains as described in the section on Pacodom above.The approaches used with BLAST greatly help comparative studies for all the genes available in PhiDB. However, some gene annotations from certain genomes are not satisfactory. Based on sequence similarity, these are readily detected with BLAST. The PHIDIAS BLAST methods can also be used to find a group of pathogen genes using a seeding DNA or protein sequence. For example, a PHIDIAS blastp search for the protein sequence of human Nramp1  yields 65 hits from 77 pathogen genomes, most of which are attributable to a single putative manganese transport protein  found in different pathogens, including four The Phinfo database module stores pathogen and PHI information curated from the biomedical literature and other curated databases. A major source of Phinfo data are PIML documents available from Virginia Bioinformatics Institute (VBI) . A Java Brucella spp. indicates that a PCR assay based on the B. abortus gene wboA  has been used to differentiate B. abortus vaccine strain RB51 from other Brucella strains. Either of the primer sequences can be linked directly by clicking to local nucleotide BLAST analysis. Genes found from local BLAST searches are also linked to the PHIDIAS gene table  are also found, indicating a possible cross-reaction during PCR assays and/or functional similarities among these genes.Phinfo provides an integrative web interface for user-friendly querying and display of curated pathogen and PHI information. Two query programs are available in Phinfo: Keyword Search and Topic Search. The Keyword Search program allows queries for specific pathogen and PHI information. Such information is displayed with the searched keywords highlighted in color. The Topic Search program searches for one or many of 47 topics listed in the hierarchical structure Figure . Comparee Figure . The wboThe interactions between pathogen and host genes have been extensively studied in the post-genomic era . HoweverEvery pathogen gene may be involved in an interaction between the pathogen and its host. The pathogen gene search interface of Phigen allows users to search for any pathogen genes from the 77 genomes of the 42 pathogens available in PhiDB Table . The PhiPhigen provides an efficient online submission system for submitting of data for curation of pathogen genes, especially their roles in PHI. The information is fully referenced from peer-reviewed publications, with direct links to PubMed paper abstracts and full texts for additional details. Submitted information is critically reviewed and verified by reviewers prior to acceptance. Currently, Phigen has manually curated and stored more than 400 genes from 42 pathogens. Instead of altering records from other public databases, the curation is currently focusing on adding PHI-related information, such as host immune responses, gene mutations and resultant pathogenic changes in the host. In addition to integrated gene information, the PHI-specific information assists researchers in surveying, comparing, and studying gene-specific PHI mechanisms.PHI has the ability to reveal complicated networks between pathogen and host molecules. Phinet is targeted at analyzing molecular networks responsible for PHI. Phinet data are stored in PhiDB and are derived from the MINetML XML database extracted through the web service, other curated databases , and manual annotation based on literature curation. Similar to that implemented in Phinfo, a Java program was developed to extract MINetML documents from the ToolBus/PathPort MINetML XML database via the MINet web service . An XSLTA Graphviz-based visualization software program has been developed internally to dynamically display all the biological interactions in Phinet Figure . The visData exchange among different pathway databases is critical for data sharing and integration. BioPAX is a community-supported data exchange format for biological pathway data . CurrentGene expression data for pathogens and/or hosts during PHIs comprise important data for analysis of pathogen pathogenesis and host defense mechanisms. The NCBI GEO  and EBI To improve further integration of different PHIDIAS components, the PHIDIAS web site contains a keyword search engine that simultaneously allows searching for information from all PHIDIAS components. All results are sorted based on the components and displayed in one page for convenient data analysis (data not shown).Brucella research by allowing users to search and analyze integrative Brucella data derived from different sources and compare these data with those from other pathogens.A deeper understanding of PHI is required for effectively combating infectious diseases. To efficiently analyze the ever-increasing amount of PHI data in the post-genomics era, PHIDIAS was developed. This program permits integration of PHI related data from genome sequences, the biomedical literature, curated databases, and gene expression experiments. PHIDIAS covers 42 microbial and viral pathogens of high priority for public heath and security. The gene and protein sequences from each genome are available for browsing and analysis using PGBrowser and customized BLAST searches. The conserved domains are analyzed and stored in Pacadom. PHI data extracted from existing databases, or internally manually curated, are stored in Phinfo , Phigen (PHI genes) and Phinet (PHI networks). PHI-related gene expression experiment records and profiles from public GEO and ArrayExpress repositories can be directly searched in Phix. The PHIDIAS components are interconnected Figure . ScenariArabidopsis thaliana subjected to pathogen infection and elicitor treatment [Similar PHI-related biological programs exist. PHI-base is a web-accessible database devoted to the identification and presentation of information on fungal and oomycete pathogenicity genes and their host interactions . PathoPlreatment . In contMany biological systems allow systematic genome comparison. MicrobesOnline is a publicly available suite of web-based comparative genomic tools designed to facilitate multispecies comparison among prokaryotes . The datEight Bioinformatics Resource Centers (BRCs), sponsored by the USA NIAID, provide web-based resources for organisms that are considered potential agents of biowarfare or bioterrorism or cause emerging or re-emerging diseases . Each BRBrucella genomes [Brucella-specific mutation and genetic interaction information has allowed a comprehensive investigation of Brucella pathogenesis [PHIDIAS is unique in that it integrates existing knowledge about a broad range of human or zoonotic priority pathogens, and focuses on efficient searching, visualization, comparison, and analysis of pathogen genes and their interactions with their hosts using genome sequences, manually curated literature data, and gene expression data from public resources. PHIDIAS utilizes online data submission systems for efficient data curation, making integrative PHI data more comprehensive. All the PHIDIAS components are scalable, and more pathogens and PHI systems may be added to the system. Due to inclusion of an ever increasing number of pathogens in PHIDIAS and in view of the dramatically increasing amount of literature information, it will be an ongoing challenge to curate all the significant genes and keep the PHI-related information in PhiDB current. Therefore, one of our future directions will be to explore ontology-based natural language processing and statistical methods for efficient literature acquisition and curation. In this regard, we have now developed a literature mining and curation system (Limix). This system has been used efficiently for literature mining and curation for four  genomes . Systemaogenesis . Limix i


PAZAR is an open-access and open-source database of transcription factor and regulatory sequence annotation with associated web interface and programming tools for data submission and extraction. , is open for business.PAZAR is an open-access and open-source database of transcription factor and regulatory sequence annotation with associated web interface and programming tools for data submission and extraction. Curated boutique data collections can be maintained and disseminated through the unified schema of the mall-like PAZAR repository. The Pleiades Promoter Project collection of brain-linked regulatory sequences is introduced to demonstrate the depth of annotation possible within PAZAR. PAZAR, located at  The study of gene regulation has emerged as a focus of efforts to understand how genome sequences give rise to diverse and complex cells and tissues. From gene-centric dissection of promoter sequences  to regulExisting regulatory sequence data collections are generated primarily in a need-driven manner. A dedicated researcher pursuing an idea will extract from the scientific literature a sufficient set of annotations to support their own studies. For example, the widely used JASPAR collection of transcription factor binding profiles  was deveThe existing pool of annotated data for transcriptional regulation is not optimal. There is an unfortunate long-term problem that stems in part from the fact that database maintenance is tiresome. The operators of the boutique databases quickly move on to other tasks, motivated equally by a dearth of monetary support and the excitement of the next project. Few regulatory sequence collections have endured for long periods of time with evidence of substantial expansion. The widely used TRANSFAC collection of regulatory sequences has been a central tool for bioinformatics . HoweverThe boutique model of database development suffers from several fundamental problems. As mentioned, collections can stagnate after the initial enthusiasm of the creator wanes. For current research, reference collections must increasingly map onto genome sequence coordinates, and thus the utility of the collections rapidly diminishes if such coordinates are not kept up to date. Furthermore, data need to be delivered in a dynamic manner accessible by web interfaces, programming interfaces and emergently via support of semantic interfaces. Flat file data models are too rigid and cannot capture data at its full granularity.In this report we introduce the PAZAR information mall for regulatory sequence annotation Figure . BuildinPAZAR is designed around two main concepts: first, the necessity for unambiguous identification of the chromosome location for any given cis-regulatory element (CRE) using genomic coordinates ; and second, a flexible database schema allowing for the capture of annotations derived from a wide range of experiments Figure . In brieThis flexible design enables PAZAR to represent data consistent with our current understanding of transcriptional regulation. First, the system refers to 'transcription start region' instead of 'transcription start site' as increasing evidence shows that transcription start sites are more 'fuzzy' than previously thought and often cannot be confined to unique nucleotides ,26. SecoThe aforementioned design features have been implemented using the mySQL relational database. The current database structure is developed and maintained through the DBDesigner software application, which provides an integrated graphic development interface and tools for automatic SQL script generation and data exchange.The wide array of PAZAR hostable datasets contains a great heterogeneity of information. To overcome the challenges imposed by such data diversity, we incorporate controlled vocabularies as a means to consistently annotate regulatory sequences and expression patterns. Bio-ontologies offer common semantics for biological functional annotations . Two topAs illustrated in Figure If one is looking for regulatory information for a specific gene, one should click on the 'Genes' department store and enter the gene identifier . As a result, the gene view page is loaded with a summary table of all genes corresponding to the query. For each of the displayed genes, the list of all annotated regulatory sequences is located in tables further down the page Figure . More inWhen looking for binding sites for a given TF, one can use the 'TFMART' department store. Various identifiers can be used for the query and the results will be displayed in the 'TF View' Figure . First, One might desire to limit queries to a single collection. To do so, the user must find the corresponding boutique in the mall map or directory and click on it. The 'Project View' provides a brief description of the dataset as well as some statistics on the data it contains Figure . Below, PAZAR provides a submission interface that one can access by clicking on 'Submit' in the left menu. This web-based streamlined user interface provides a simplified entry point to the database for non-professional curators, such as scientists that want to deposit their own experimental data to the public repository.We have developed a Perl API (application programming interface) that hides the intrinsic complexity of the schema from database users. The object-oriented approach provides programmers with different layers of abstraction, allowing advanced users to create 'high-layer' objects and methods to suit project-specific needs.database module. Currently, three modules have been developed to interact with the GeneKeyDB [To best serve users, PAZAR must frequently retrieve data from external sources. For example, sequence coordinates must be updated when genome assemblies are released, updated, or re-annotated. The API pazar::talk modules make this possible by delegating all external queries to an appropriate pazar::talk::eneKeyDB , JASPAR eneKeyDB , and EnseneKeyDB  databaseA PAZAR-specific exchange format has been implemented in XML (extensible markup language). In addition to facilitating data transfer between 'boutiques' and the central master database, the XML format can support custom stand-alone user interfaces that do not have direct database access. Some basic sequence features can also be exported in GFF . API methods are available to parse PAZAR XML or GFF format data for importation into the database.Each data collection within PAZAR is called a project and is identified by a project ID, a project name, a status and a list of users. The project status can be 'restricted' (only the project-specific users have read and write access), 'published'  or 'open' (everyone has read and write privileges). For this purpose, each record in the database is linked to a project ID, allowing all projects to share the same tables within the database schema, yet retaining their project identity so that they remain independent data collections.At the time of submission of this manuscript, there were 11 projects present in the database  are available through the PAZAR website  or the dA large fraction of gene regulation data comes from high-throughput techniques such as gene expression and chromatin immunoprecipitation microarrays. Unfortunately, the observed data are difficult to interpret as they often reflect contributions from overlapping processes. One means to improve the interpretation of results is to incorporate prior knowledge of regulatory processes ,40. The Considering the future of gene regulation databases, three things are apparent. First, the motivation and expertise of individual researchers, as well as their focus on deep annotation of specific pathways and processes, make boutique operators a key resource in long-term compilation of regulatory sequences and annotations. Second, based on principles shared by the authors, any database should provide data and software in an open, unrestricted manner to all researchers in all settings. Third, the ongoing technical challenges for databases require a long-term commitment of talented technical staff. PAZAR was developed based on these observations.While our laboratory will maintain PAZAR for the long-term as it is necessary for our on-going research, ideally the project would expand through the engagement of a cooperative research community. Recent events suggest that the global research community is prepared to participate in regulatory sequence annotation projects. In late 2006, a group of open-access motivated scientists contributed regulatory sequence annotations to the ORegAnno database . While PDevelopment of PAZAR will require ongoing effort to expand the data represented, the means to access the data and the quality of the data curation tools. At present, existing data collections are being added to PAZAR with the permission and collaboration of the boutique operators. We anticipate the boutique database creators will be strongly motivated to use the system as it eases their own work. For instance, most high-throughput datasets currently generated never become available through a database and web interface because of the limited time researchers want to put into this effort. PAZAR provides an easy way to make these data available and to maintain them. Readers of this paper are encouraged to consider opening a boutique or working with the PAZAR team to move an existing data collection into the system.Our goal is for PAZAR to become the public repository for data and annotations pertaining to transcriptional regulation. By promoting strong integration with tools for computational analysis and prediction of cis-regulatory sequences, boutique database operators will be motivated to participate in the expansion of the system.API, application programming interface; CRE, cis-regulatory element; GFF, general feature format; TF, transcription factor; XML, extensible markup language.EPC and SK participate in creating the vision of the system, designed the database and implemented the software. EPC prepared the initial draft of the manuscript. JL participated in the software and database design. SL, MS and AT tested the system, developed documentation, and compiled the Pleiades data collection. AT produced the Cytoscape figure and contributed to the importation of OregAnno data. JS co-supervised the project and participated in the creation of the vision. WWW co-supervised the project, participated in the design of the system, and revised the manuscript. All authors read and provided feedback on the manuscript.


Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information.In this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data.Using this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes. In this way pathologists or researchers may perform reactions in a parallel way on tens or hundreds of tissues at a time, with a decrease of experiment time and reagents costs, an improvement in normalization between samples, which are treated in a single reaction, and enabling the reuse of scarce resources such as tissue blocks [pre-array images. Then, if we consider that from the elaboration of a single TMA block hundreds of slices are obtained, that these can be processed with different reagents, and that each slice of the block can contain hundreds of samples, we can prefigure the amount of single spot images, or post-array images, that an institute of pathology must handle.One of the most promising biomolecular approaches of recent years in the cancer research field is the Tissue MicroArray (TMA) technique. A paraffined embedded TMA is a matrix of cores of fixed tissue samples: tissues come from e blocks . The TMAHandling and mining all TMA data is extremely difficult without a suitable management system to deal with storage and data integration. To accomplish this task, a number of applications can be used. Some software tools are commercially available, like tools developed by SlidePath , for infet al. propose an interesting application developed at Stanford University [et al., who propose the TMAJ [et al., who developed TMABoost [Long Liu C. iversity , which ithe TMAJ  system, TMABoost . The forNevertheless, neither of these two systems allows deep interactions among pathologists and researchers either limiting intense tissue sharing and neglecting the idea to increase possibilities of improved TMAs creation by making thousands of samples stored in the anatomo-pathology institutes available to researchers. Moreover, there is a lack of data integration of clinical and biomedical information with bioinformatics knowledge, an approach that enriches the traditional histo-pathology vision, adding the molecular biology information to the histological and clinical data, and providing analysis methods based on statistics and data mining.The aim of the work is to provide pathologist community a simplified tool to manage their experiments in order to facilitate data exchange and integration with other information available on line. The platform arises from the necessity, not solved by other existing platforms, to enrich the support given to TMA experiments, because in our opinion the communication between pathologists, researchers and bioinformaticians of different institutes is important for the spreading of this technique. Indeed, a peculiarity of the proposed system is the integration in the web application of a strong ontology mechanism to guarantee standardized descriptions for biological materials and experiment outputs. Moreover, the system provides high integration to molecular biology knowledge: although the technique is pretty close to the bioinformatics field, there are no references in other platforms about managing genomic, transcriptomic and proteomic data coming from TMA experiments. The described work contains a first attempt to provide statistics on the bioinformatics data, such as genes and proteins related to metabolic pathways.Our system relies on different components. Storage and data integration are provided by a MySQL RDBMS, which is useful for creating links between data associated with tissues and TMA experiments. Two ontology terms services were developed to integrate and improve information about cancer topic, one applied to pre-array HE treated tissue images and the other associated to post-array immunohistochemical and biomolecular reactions images. In both cases we used the ontology to provide semantic web integration with on line papers, in order to provide literature data related to the experiment.In order to share data among the research community, a number of information must be associated to samples. Working on literature information  and deve○ patients' clinical data;○ sample origin, extraction and preservation;○ TMA block aim and structure;○ post-analysis image data.The TMA database structure is presented in Figure individuals, which are the basic objects, classes, that are the categories which objects belong to, attributes, which are the features the objects can have, and relations, that are the ways objects can be related one another. Open Biomedical Ontology (OBO) [In order to standardize descriptions we chose to use medical ontology terms. Ontology is a collection of terms naming descriptors in a hierarchical structure that allows searching at various levels of specificity in a particular domain. Ontologies generally describe gy (OBO)  is a colobo ontology format we integrated in our web site the open source Ontology Lookup Service (OLS) [obo ontologies in a single database. It can be accessed interactively by users or through a specific client. Using the web interface, the database can be queried using a user-friendly auto-completion search mechanism, to look in the ontology for a single specific term. Otherwise it is possible to browse the complete ontology tree using AJAX library and the system can be queried using a standard SOAP web service described by a WSDL descriptor.To easily handle the ce (OLS) . It provpre-analysis phase each image representing a slice of HE treated tissue is followed by a text description, to add precious information to the samples. This is important to better understand which kind of tissue is stored and if it can be useful for some researchers in designing their TMA experiments. To provide a complete overview of ontology terms correlated to TMA we also developed a post-analysis phase, concerning ontological classification of genes and proteins detected in the experiments.Following the traditional pathologists specification, the site provides the SNOMED classification , for terobo format, which allows simple handling of ontology terms, independently from the MeSH web site.We used the Medical Subject Headings (MeSH) ontology  to proviTo provide standard descriptions in both pre and post analysis contexts, we installed the whole OLS platform uploading the MeSH ontology. Users can browse the subset A (Anatomy) for the tissue-related terms, and the subsets C - D12 - D06 - D08 - D13 - D23 , for gene and protein representation. All these services are accessible from a web user interface as shown in Figure Considering that TMA experiments always aim to find the presence of proteins or genes in selected tissues, the possibility to integrate information about Gene Ontology (GO)  becomes The user is asked to insert the gene or the protein probed on the TMA in a form. The value is sent to Entrez , the NatAnother important issue of our ontology compliance TMA-oriented tissue bank is that the use of standardized descriptions provides an easy access to articles related to specific topics. In this way we can support literature mining, specific for each sample in the database, thus promoting data integration. In order to handle a semantic approach for finding related articles, we integrated a connection to MeSH database through NCBI Entrez in our system. Using this tool, the implemented system browses the MeSH ontology based classification for a semantic web search in both tissue-oriented pre-analysis and gene/protein post-analysis terms, to give users supplementary information about tissue histology and correlation between gene and pathology. Using the NCBI web service a properly formatted ontological query is sent to PubMed  in orderA web interface has been developed to consistently access data stored in the database. The web site is implemented in PHP language, with the integration of PEAR library to improve functionality, while all the ontological aspects are managed by OLS. In order to provide a user-friendly system AJAX functions have been used for searching ontology terms even in the auto-completion terms mode. A crucial aspect the system takes care of is the security problem, considerably important in all biomedical applications dealing with sensitive patient information. To protect data in transit between the user's computer and the web server and vice-versa, we chose the HTTPS protocol, the secure HTTP connection for data transfer, which works just alike to HTTP but with a different default TCP port (443), and an additional encryption/authentication layer between the HTTP and TCP.A very important aspect for security relies on the policy used to grant access credentials. Users must register previously to use our on-line support to the TMA oriented tissue bank in order to guarantee data security. Then, a specific role is assigned to each user according to his necessity:researcher: has the possibility to see every sample inserted in the tissue bank and to browse all related information but can not insert any new sample;○ technician or pathologist: is able to insert new samples or modify existing ones and can browse every sample belonging to the institute he works for;○ supervisor: can insert or modify all samples belonging to his institute and can browse all the samples contained in the database.○ According to the double aim of the site, both to provide a tissue data bank and to facilitate the creation of TMA blocks, users logged into the TMARep have access to samples and TMA experiment descriptions management according to their assigned role, as shown in Figure collections, which are groups of tissues referring to the same disease. This work is specifically oriented to cancer affected tissues, so every collection should be defined as a type of cancer. Users who want to upload a new sample firstly have to insert the patients' clinical data, then to complete the form about tissue extraction and localization information, specifying preservation data for every sample obtained from the patient. In this form users must also upload an image of the hematoxylin-eosin reaction made on the first slice of the paraffin embedded tissue.Samples belonging to each institute must be assigned to A short description must be associated to every image, using suggested ontologically-compliant terms. The ontology based system is crucial to support tissue sharing and information integration. For example terms like “colorectal cancer” and “colon cancer” describe the same phenomena. If a pathologist uses the first expression while another prefers the second one, the associations of the two terms in a free-text based description system can be difficult. The problem has been solved thanks to the ontology approach, which suggests unique terms among which pathologists must chose. Moreover, pathologists can describe samples at different levels of details: a biopsy can be annotated as belonging to the “colon ascending” tract, while a shorter description could involve only the term “colon”. In this situation the ontology tree plays a crucial role in correlating these two terms, because the second is parent of the first one. Moreover, ontologies are useful for data integration from external resources: terms can be used to interact with the MeSH semantic web system to link the sample description with related articles reported in PubMed.When a researcher browses the tissue bank and finds some interesting samples, he can tick them off in order to include them in his TMA experiments, according to their effective availability. To facilitate TMA experiment design, the web system provides a complete description of the selected samples which can be downloaded in a standard xls spreadsheet thanks to the integration in the web site of specific PEAR functions. This is very useful both for supporting the creation of the TMA, because it represents a summary of the characteristics of the samples, and for uploading results in the web site, in the post analysis phase.in situ and a score to quantify its presence in the spot, as shown in Figure staining intensity by the grade. The staining intensity is scored on a scale 0-3, where 0 means no staining, 1+ weak, 2+ moderate and 3+ high, and is determined on the basis of at least 10 representative fields of 0.125mm2 (x500). The grade of gene or protein presence also lies between 0-3: 0 (0-4%), 1 (5-29%), 2 (30-59%), 3 (60-100%).After producing and processing TMA blocks, users can complete the dedicated form, explaining experiment aim and design, and uploading images of single spot reactions. This is an important step because the researcher is asked to report a quantification of the results obtained in his experiments: he is asked to associate to every processed spot the name of the gene or the protein that has been hybridised This scoring step is critical for integration of TMA results. The correlation between biological sequences found in tissues and pathologies is one of the innovations proposed by our system. To integrate information about genes and process involved in different pathologies, firstly we use disease information to cluster TMA experiments into groups of tissues affected by the same disease, independently from the institute they belong to. Then we consider genes and proteins with a high IRS (>4) and relying on GO organizing principles  we make statistics of components involved in a specific disease.Using the developed Perl algorithm we can estimate, for each disease, which are the most visited pathways. While uploading the stained tissue images, the user is asked to specify not only the disease studied but also the biological target of the experiments. The Perl algorithm, relying on GO, tracks all the parents of the indicated target. For every pathology a core database table is created, containing all the GO IDs associated to a counter, which is increased every time the ID is involved in the ascending ontology path to the root term. The values of these variables are visualized in the web site as bar lines of proportional length. Statistics are visualized in a shared page of the web site that can be freely accessed by all authenticated users. The ontological approach of these statistics, which relies on GO categories, allows the identification of biologically consistent linkage between specific pathways and the examined pathology. This aspect is crucial for a deeper understanding of the biomolecular mechanisms mainly related to every specific cancer type and provides an effective integration of bioinformatics data in pathological analysis.It is worthy to note that the use of conventionally defined terms also provides information about research topics, and consequent linking of papers related to the same context. When a page of our site displaying sample information is shown, the ontology terms are hyperlinked through the MeSH system and the user can easily access published papers concerning the same topic.The TMA management system can be accessed through the World Wide Web using any recent web browser. URL: pre TMA construction (sample description level), allowing the uploading of HE reacted sample images followed by an ontology description to better identify the kind of tissue, and in the post construction , giving the possibility of adding information about genes or proteins searched on tissues and of performing statistical analysis on these data, allowing data integration between different cancer types and specific gene or protein presence. The innovative feature of ontology based description for tissue origin, morphology and staining, provides a complete and community-recognized identification of samples, which, according to us, is crucial for tissue sharing. The availability of ontological terms allows the development of semantic web searches in papers contained in the PubMed repository, thus improving data integration.The developed infrastructure is useful in A first attempt to add bioinformatics methods to TMA analysis is represented by the study of the most involved molecular pathways.Users of this database will be the pathologists of medical institutions which can archive data related of their preserved tissues, and the members of the scientific community who are interested in creating their own TMA use cases. The system represents a place where different competences and abilities can meet, moving towards biological sample sharing, in order to better design TMA experiments and obtain interesting and reliable results. Sharing is, in fact, the only way to build rich and complete TMA blocks collections, to verify researcher's expectations about the biomolecular content of cancer tissues.The groups which are involved in this project are the Institute for Biomedical Technologies of the National Council for Research, Biorep s.r.l, a company which specializes in biomaterial nitrogen based storage, and Multimedica, a private hospital in where there is an active anatomo-pathology division. A previous additional contribution came from National Cancer Institute , which participated to metadata definition and interface organization.AJAX: Asynchronous JavaScript and XMLGO: Gene OntologyGOA: Gene Ontology AnnotationHE: Hematoxylin-EosineHTTP: HyperText Transfer ProtocolHTTPS: Secure HTTPID: IDentifierINT: National Institute for TumorIRS: ImmunoReactive ScoreMeSH: Medical Subject HeadingNCBI: National Center for Biotechnology InformationNIH: National Institutes of HealthOBO: Open Biomedical OntologyOLS: Ontology Lookup ServicePEAR: PHP Extension and Application RepositoryPHP: Hypertext PreprocessorRDBMS: Relational DataBase Management SystemSNOMED: Systematized NOmenclature of MEDicineSOAP: Simple Object Access ProtocolTCP: Transmission Control ProtocolTMA: Tissue MicroArrayWSDL: Web Service Definition LanguageThe authors declare that they have no competing interests.FV designed the database, the data integration system and the web interface. IM was involved in the technical aspects of the implementation and in the database definition. AC, BL and AS designed and developed the GO link system. LM coordinates the database specification and the development of the full text literature mining interface. All authors read and approved the final manuscript.


Bioinformatics is a relatively new discipline. It is a field of science in which Computer science, Mathematics, Molecular biology and Information technology merges to form a single discipline. Database development, sequence alignment, protein structure prediction, RNA folding, evolutionary tree construction are some of the areas covered under this discipline.A biological database is a large organized body of persistent data usually associated with computerised software. It can be considered as electronic filing cabinets which store large amount of data in a systematic manner. One can update, query or retrieve components of data stored within the system. In 1960, Margaret Dayhoff established the first database of protein sequence while in 1982 (five years after the DNA sequencing techniques were discovered), the first nucleotide sequence database, GenBank was established. Databases are classified as Universal  and Locus or Disease specific.  or Primary (nucleotide sequence) and secondary (Protein sequence) database. At present there are number of databases on specific genes and proteins pertaining to human, animals, plants, bacteria etc.In 1967, Victor McKusick started collecting data on human mutations. This ultimately resulted into OMIM (Online Mendelian Inheritance in Man) which mainly gives the list of Mendelian monogeneic disorders. It gives a lot of information about mutations, however all entries are not fully comprehensive. Human Gene Mutation Database (HGMD) was established in April 1996 by David Cooper–4 RecentToday novel DNA sequencing and genotyping techniques are being used to detect mutations. Such mutations are being identified at an exponential rate by researchers in various population groups of different countries. Currently publishing only mutations in a particular gene is difficult because many journals do not accept such articles. Similarly many laboratories do not consider reporting of such data as an essential activity. In such circumstances incorporating the mutation data in the appropriate database is the best way of reporting the DNA variation and eventually this could replace the conventional scientific publication as the predominant form of communication.Regional or Ethnic Databases provide valuable data for the study of population history, genetic testing and disease association studies. Spectrum of mutations in a particular ethnic group helps to devise a strategy to detect mutations or to run prenatal diagnosis programs. It also enhances awareness about the various genetic disorders and provides insights into the genographic history of human populations.There are very few reports from India which provide regional and ethnic data on the spectrum of genetic disorders. A search on the OMIM using the term ‘Indian sub-continent, gives 14 entries while a separate search for India yields 72 entries. This resOn the other hand countries like Iran, Singapore, Turkista


The past twenty years have witnessed an explosion of biological data in diverse database formats governed by heterogeneous infrastructures. Not only are semantics (attribute terms) different in meaning across databases, but their organization varies widely. Ontologies are a concept imported from computing science to describe different conceptual frameworks that guide the collection, organization and publication of biological data. An ontology is similar to a paradigm but has very strict implications for formatting and meaning in a computational context. The use of ontologies is a means of communicating and resolving semantic and organizational differences between biological databases in order to enhance their integration. The purpose of interoperability (or sharing between divergent storage and semantic protocols) is to allow scientists from around the world to share and communicate with each other. This paper describes the rapid accumulation of biological data, its various organizational structures, and the role that ontologies play in interoperability. During the 1960s, there was a simultaneous evolution of digital protein and taxonomic inventories. By the 1980s, these had matured and were institutionalized with an attendant proliferation of biological data. These datasets were, however, maintained in closely-guarded proprietary repositories or ‘silos’ with little or no communication between them . The 199As the cumulative body of biological knowledge increases, generating a comprehensive and consistent account of biology hinges upon the ability of scientists to draw upon and synthesize vast datasets across distributed digital resources. The ultimate objective of biodiversity informatics is to generate a “global inventory of [all] life on Earth” , p 365, The rise of ‘omics’ science—genomics, proteomics, and metabolomics for the identification and prediction of genetic product components, signatures, and processes , p 550—hcoding regions. The objective of automated gene prediction is thus to determine the “coding potential” of genetic sequences , and functional prediction tasks. New technologies such as micro arrays generate huge and ever-changing volumes of data . The rapequences , p 414. equences . Where gequences . Gene fiequences . The potequences . Howeverequences . In the equences . This isesources .Unlike systems architectures, the integration of which constitutes an ‘IT problem’ , data arcontexts from which biological concepts emerge—at the database level. This is because functional prediction hinges upon the identification not just of sequence homologues but similar cellular components participating in a similar biological process. The component cellular, molecular, and biological details are often located in separate data sources, a function of the narrow scope of biological information produced by any given laboratory . We describe ontological concepts—and their role bioinformatics—using the examples of two preeminent ontological efforts in biology: the Gene Ontology (GO), which is itself part of the umbrella Open Biomedical Ontologies (OBO) initiative. Subsequently we explain how ontologies can be exploited to facilitate information sharing and data integration efforts for bioinformatics with reference to real-world, large-scale biological information portals, namely the cancer Biomedical Informatics Grid (caBIG), and WikiProteins, a proprietary knowledge commons for proteins.Furthermore, we describe a methodology for using ontologies as a basis for comparing semantics across health registries in order to illustrate how medical informaticians have imposed interoperability on disjunct datasets. Once semantic and schematic heterogeneity is resolved between data-sets, we explain how ontologies can be used to facilitate knowledge creation tasks in biology, such as automating gene/protein annotation and functional prediction.To provide a global overview of ontologies for biology, we also draw upon a related community of research—health/medical informatics—which uses and shares with bioinformatics a series of knowledge representation constructs for the capture of biological information. Both use genetic information in the era of “ ‘post-genome’ science” , p 233. In philosophy, ontology has traditionally been understood to be the essence of being—or what something really is . In the formal ontology is a machine-readable model of the objects allowed into a formal universe and their associations or relationships between them upon which some automated reasoning tasks can be performed. In a formal environment, an ontology constitutes a surrogate of knowledge abstracted from the real world—in this case, the cumulative body of biological science—in a coded form that can be translated into a programming language  and the possible relationships between them. The final stage or level is the development of code that can be run by computers . Ontologrelations between concepts in the hierarchy. Relational expressions are the implementation basis for all subsequent computing and modeling tasks in a software environment. Formal semantics permit the distinction of concepts declared into the model . To saticontext—akin to usage in natural language—in which concepts are used or from which they emerge. The utility of capturing relationships between concepts is thus that they convey semantics; content semantics are expressed by identifying how concepts relate to each other in the hierarchical knowledge space. This hierarchical knowledge space is a parent-child structure that conveys the semantic granularity of the relation between any two concepts by rendering entities to be either more specific of more general than each other (is-a) relationship is implied by the hierarchical nesting of terms and denoted by their position relative to each other in the family tree on the basis of their subsumption (where a concept is a subclass or member of another) and specialization (where one concept is the superclass of or contains another) .The ability to define relationships between concepts distinguishes formal ontologies from earlier integration and interoperability approaches. How they are expressed are detailed in the subsequent sections on the GO and OBO efforts. Relationships are an expression of the ch other . This foanother) . Additioconcepts , their properties (or relationships between said entities), and instances  in a hierarchical model , which specifies what the ‘triplet’ structure (concepts + properties + instances described above) of ontological expression. A standard schema ensures that when OWL statements are parsed or transformed into the component data structure of the target formal ontology, the parser knows which part of the expression constitutes the concept, which section the relation, and which the instance. It is this structure which makes the grammar of an ontology meaningful—in the case of bioinformatics, for example, it anchors annotations to the gene products they characterize  relationships between concepts  . This peThus far, we have described the problem of semantic and schematic heterogeneity and introduced ontologies as a means of mitigating the problem. The formal implementation of ontologies—as well as necessary conditions for formality—has also been discussed as well as its advantages for promoting computer reasoning. In the next section, we describe in detail the genesis and development of a bioinformatics portals, GO and its role in biological data interoperability. In addition, we briefly illustrate the implementation of ontologies in two database as well as an ontology-based method for comparing data from different registries or jurisdictions.The use of ontologies for bioinformatics is being driven by the proliferation of genome-scale data-sets and the diffusion of the Internet and its protocols for data sharing and exchange . Bio-ontSaccharymyces (yeast) Genome Database in 1999 , and the  in 1999 . The for in 1999 . A poten in 1999 . As ther in 1999 . In otheThe chief impediment to this task were not the unique identifiers for the gene products themselves as researchers had been tapping into protein and gene databases such as GenBank and Swiss-Prot, TrEMBL and PIR for decades . Because sequences are unique, they could be easily accessed on the basis of sequence characteristics (though there was sequence redundancy between protein repositories). Computationally, because sequences can be quantified, this is a trivial integration task that simply requires the normalization of unique codes . Rather,metadata, or ‘data about data’ Historically, annotation has been stored as free-text or at best semi-structured descriptions semantically particular to the terminological or classification systems unique to many of the databases . This includes information about their function, position relative to coding/non-coding boundaries, participating process, etc. . Annotatatabases . There watabases . This isatabases .The GO Consortium formed as a response to the pervasive semantic heterogeneity of biomedical data and its lack of formality. Indeed the GO was designed for making historically free-text based annotations tractable . The thredges represent relationships between concepts (Gene product semantics are organized into three categories which capture the primary ‘aspects’ of genes: i) biological process, which captures the larger process in which the gene product is active; ii) molecular function, the biochemical function a gene product contributes to that process, and iii) cellular component, the location in the cell where that particular function is fulfilled or expressed . Conceptconcepts . These tconcepts . Annotatconcepts . A 2005 concepts . All posconcepts .is-a, which denotes that concepts are kinds of entities, and part-of, which can signify the participation or contribution of a concept in a sequence or process  flat file structure which is underwritten by a modified subset of Web Ontology Language (OWL) description logics (DL) concepts for content specification .Like OWL, OBO is an ontology language, and standard ‘file format’ for GO annotations. It is however less expressive than OWL. These relations are unidirectional and linear as per the DAG data model and do not require the recursive relational declarations  characteristic of OWL statements. Thus a flat file structure that only supports sequential reading is appropriate for the GO because relations are read from broader or general to more specific or precise concepts.At the level of the database, the GO is represented as a structured vocabulary; more specifically, as gene product annotations expressed using concepts and their tripartite  structure defined in the GO . The GO Notwithstanding, the GO provides the standard vocabulary for semantic integration and automated tasks for bioinformatics. As such it is more than merely a sophisticated data dictionary. Whereas controlled vocabularies or data dictionaries provide a definition of the terms used by a community of practice and these may indeed be machine-readable and thereby formal, a nomenclature does not capture the hierarchical representation of knowledge nor the corresponding relations between all concepts in the data space, and thereby does not support computational reasoning . Severalglobal ontology  initiative . The OBOraison d’etre of the GO, the potential of their ontological encoding is not to have a hierarchically structured record of concepts used to annotate the data of biology, but rather to exploit the ontology for a series of bioinformatics services which remove the burden of data-intensive tasks from molecular biologists and moreover produce knowledge over and above facilitating its reuse.The largest public contributor of annotations to the GO project is the Gene Ontology Annotation Database (GOA) . While aontology browser on the basis of these common data elements and indeed use the existing GO annotations to not only discover sequence similarity but to also automatically populate or their own database using the existing annotations for homologues from other curated data sources. Thus the ontology functions as a ‘translation schema’ , prediction services, and database population. Prediction services supported by ontologies yield new biological knowledge. Gene location using current generation algorithms uses data from a pair of genomes to locate areas of genetic affinity; these areas of ‘overlap’ are often the sites of new genes , which participate in a certain role (as Immunogens) in a specific process (such as Immunization)” ” , p 489.ontology-based metadata which uses ontologies as a component in a metadata-based framework for the comparison of a series of eight ‘near’ but non-equivalent terms that have been identified as an obstacle to integrating perinatal (pregnancy and antepartum) health data registries across Canada. Our objective is to provide health researchers and data stewards with a basis for drawing meaningful parallels between data elements to enable the legitimate integration of peri-natal data registries. Ontology-based metadata for each term is first collected via a series of electronic forms which standardize the description of each concept. Each constituent database is responsible for detailing how these terms are used in their particular jurisdiction—or context. This includes a specification of the classification standard used (e.g. ICD-10), the identification of thresholds for measurement specifications, and space for free-text descriptions of any policy constraints which may influence how the term is used in a given jurisdiction. In addition to these ‘annotations’, we encode each perinatal database as a formal ontology in OWL. These ontologies capture the semantic structure of database terms. These are then merged into a single ontology, with the relationships between each and every concept defined in the product tree. Both the ontology-based metadata and the ontologies are inputs to a semantic data discovery portal where researchers specify which terms in two respective databases are to be compared via a graphical user interface (GUI). The application returns to the user both the encoded relationship between the concepts extracted from the OWL code—for example, where pregnancy-induced hypertension is a KIND-OF hypertension complicating pregnancy—and the ontology-based metadata for each term in the selected databases. Thus the researcher is provided with both a marker for the granularity of the semantic relationship between two concepts, as well as valuable metadata which are used to inform perinatal database decisions.Ontologies can further be used as a basis for exploring datasets. We have devised a methodology called A). In yet other registries, any form of hypertension presenting during pregnancy is considered gestational such that a pre-existing condition which first manifests itself during pregnancy is still encoded as pregnancy-related (database B). There is thus a semantic incommensurability between what ‘gestational hypertension’ represents in databases A and B, precluding a direct mapping between these concepts indicating semantic equivalence. Rather, ‘gestational hypertension’ in database A would be a kind of gestational hypertension as the concept is reified in database B. If a researcher were to query ‘gestational hypertension’ across both databases, she would logically accept them as referring to the same concept on the basis of lexical coincidence. However, the lack of an encoded equivalence between these two concepts would preclude their conflation. Thus our this approach not only provides information regarding how concepts should be associated, but also uses formal ontologies to restrict which concepts may be legitimately compared. This nesting of relationships between semantic terms is described in The gestational hypertension/hypertension example above would indicate that hypertension experienced during pregnancy is a more general concept which includes gestational hypertension but also encompass pre-existing hypertension. In some databases, hypertension and pregnancy-induced or gestational hypertension are not differentiated from chronic or pre-existing incidences of disease. Alternatively, in other databases, these concepts are distinguished from each other on the basis of the periodicity of disease onset such that chronic hypertension and pregnancy-induced or gestational hypertension are disjoint , a Web-based National Institutes of Health (NIH) data consortium for cancer research , and frameworks that exploit their machine-readable semantics to support bioinformatics tasks, such as the cancer Biomedical Informatics Grid (caBIG) and the incipient WikiP-roteins knowledge community. In addition, we introduced the concept and early implementation of ontology-based metadata in order to demonstrate the role that context plays in clarifying hierarchical and schematic relationships between like but non-equivalent semantic terms in different databases. Each of these is a unique approach to surpassing the problems associated with a lack of congruity in language and meaning across scientific databases.Ontology has a different meaning in philosophy than computing. In the former, it means the essence of being. In computing and information sciences, an ontology is a formal universe in which each entity is precisely defined and its relationship with every other entity in the specific categorical or computing realm is precisely determined. Ontologies in this context are the range of what is possible—in a computing context. They can be thought of as simply a classification system, a map legend, or a data dictionary.Epistemology is the study of “how we know what we know.” In other words, epistemology is the lense through which we view reality. Epistemology refers, in the broadest sense, to the methods that we use to study the world and the perspective that a researcher uses to interpret entities and phenomena.Semantics refers to the ways in which language is interpreted differently in different context, environments or in different institutional cultures.Gene prediction—also referred to as gene finding—uses algorithms to identify biologically functional regions—or exons—of sequences which explicitly code for proteins. These are referred to as coding regions.Gene mapping is the creation of a genetic map in which DNA fragment are linked to chromosomes.


The rapid progress of post-genomic analyses, such as transcriptomics, proteomics, and metabolomics has resulted in the generation of large amounts of quantitative data covering and connecting the complete cascade from genotype to phenotype for individual organisms. Various benefits can be achieved when these "Omics" data are integrated, such as the identification of unknown gene functions or the elucidation of regulatory networks of whole organisms. In order to be able to obtain deeper insights in the generated datasets, it is of utmost importance to present the data to the researcher in an intuitive, integrated, and knowledge-based environment. Therefore, various visualization paradigms have been established during the last years. The visualization of "Omics" data using metabolic pathway maps is intuitive and has been applied in various software tools. It has become obvious that the application of web-based and user driven software tools has great potential and benefits from the use of open and standardized formats for the description of pathways.In order to combine datasets from heterogeneous "Omics" sources, we present the web-based ProMeTra system that visualizes and combines datasets from transcriptomics, proteomics, and metabolomics on user defined metabolic pathway maps. Therefore, structured exchange of data with our "Omics" applications Emma 2, Qupe and MeltDB is employed. Enriched SVG images or animations are generated and can be obtained via the user friendly web interface.L-lysine producing strain Corynebacterium glutamicum DM1730. During fermentation, oxygen supply was switched off in order to perturb the system and observe its reaction. At six different time points, transcript abundances, intracellular metabolite pools, as well as extracellular glucose, lactate, and L-lysine levels were determined.To demonstrate the functionality of ProMeTra, we use quantitative data obtained during a fermentation experiment of the The interpretation and visualization of the results of this complex experiment was facilitated by the ProMeTra software. Both transcriptome and metabolome data were visualized on a metabolic pathway map. Visual inspection of the combined data confirmed existing knowledge but also delivered novel correlations that are of potential biotechnological importance. To obtain a complete understanding of the function of cells, it is important to identify the roles of genes and their products. The analysis of gene transcripts (transcriptomics) and proteins (proteomics) is accelerated through the use of microarrays, ultra fast sequencing, and mass spectrometry. Additionally, cells contain numerous other organic molecules not directly encoded in the DNA, the metabolites, which are critical for cell function. Knowledge about metabolites is crucial for an understanding of most cellular phenomena -3. All oIntegrated approaches combining metabolomics with transcriptomics and proteomics have been reported ,5 and reThe aim of scientific data visualization is to display properties of a data set that help researchers to identify quickly its most important characteristics. In functional genomics and post-genomic techniques there are recurring visualization strategies that are generally favored by researchers. For example in metabolomics, a frequent way in which molecular biologists like to visualize data is through the use of metabolic pathway maps. For this purpose a several packages and tools have been implemented which will be presented in the next section in more detail. We will highlight the important features and limitations of existing approaches which led us to the decision to implement the web-based ProMeTra tool which is the focus of our work.Initially, there were databases such as KEGG  or the dSeveral tools have been developed to visualize and analyze biological networks together with data obtained from functional genomics measurements. Most interesting in this context are tools that visualize experimental data in the form of biochemical networks. The authors of the VANTED system for advanced data analysis and visualization in the context of biological networks  presenteSBML, the Systems Biology Markup Language, facilitates the description of models and enables their exchange between various simulation and analysis tools. The XML-based SBML is a free and open format distinguished to represent biochemical reaction networks via a clear notation system . The CelA major component of KEGG, the Kyoto Encyclopedia of Genes and Genomes, is the PATHWAY database which represents most of the known metabolic pathways . The datpathway element is the root element that specifies one graph object. The nodes of the graph object are represented by the entry elements, whereas the relation and reaction elements specify the edges . An entry element contains information about a node of the pathway, like id, name and type. The relation element specifies a relationship between two proteins or protein and compound, which is indicated by an arrow. The reaction element describes the chemical reaction between substrates and a products . XML-files, which are defined by the KGML schema, can be downloaded from .The KEGG Markup Language (KGML) is an XML-based exchange format and contains computerized information about graphical objects and their relations in the KEGG pathways. In KGML a KaPPA-View is a web-based tool and was developed to represent quantitative data for individual transcripts as well as metabolites on plant metabolic pathway maps. The aim of the system is to support the generation of hypotheses of gene function in the metabolic pathways through an intuitive visualization of the transcripts and metabolites. The system uses SVG vector graphic images for the representation of the biochemical pathways and the experimental datasets that are mapped on the pathway representations .Arabidopsis thaliana Affymetrix arrays) onto diagrams of metabolic pathways or other processes. It has been developed specifically for data generated in Arabidopsis thaliana experiments measuring transcript or metabolite levels. The visualizations focus is on the display of experimental data in hierarchical and pre-defined pathway maps.MapMan is a user-driven tool that displays large datasets  or spreadsheets, the support to directly access "Omics" databases containing experimental results via Web Services is to our knowledge not well established. Data integration using Web Services is an elegant method to connect heterogeneous "Omics" frameworks and we will explore this approach for the following functional genomics experiment which combines transcriptomics and metabolomics measurements.Corynebacterium glutamicum is widely used for the production of industrially important amino acids. L-glutamate (1.5 million tons) and L-lysine  are the major products and the amino acid market is growing at an annual rate of 7% . Th. ThC. glse (ldh) . Both enldh gene decreased and at the same time (t5 and t6), those of the gene encoding the dissimilatory lactate dehydrogenase lldA [C. glutamicum already reported [C. glutamicum, carbon sources which enter the metabolism downstream of pyruvate use the glyoxylate pathway as anaplerotic reaction [After aeration is switched on again, transcript levels of the ase lldA  were increported . The obsreaction . This isenolpyruvate (PEP) phosphotransferase system ptsG was lower due to the fact that its transcriptional repressor SugR [pyk) was found downregulated (t6), possibly to avoid efflux of PEP into the TCA. Probably, due to the fact that at the same time the genes of the two PEP-consuming enzymes PtsG and Pyk displayed lower transcript levels, PEP itself accumulated.In carbon metabolism, drastic differences relative to t1 appeared when all carbon sources had been consumed (t6). Again, several correlations between transcript level and metabolite pool ratios were observed. After glucose was consumed (t6), the transcript level of the gene encoding the glucose-specific enzyme II of the phosphosor SugR ,55 was uL-lysine precursor D, L-diaminopimelate accumulated in the cell and the internal L-lysine pool rose after aeration was switched off for a longer time. Diaminopimelate is not only the precursor for L-lysine but also for peptidoglycan biosynthesis, which is used in cell wall production. Since biomass formation and apparently cell wall synthesis almost stopped during low-oxygen conditions, diaminopimelate accumulated  due to decreased consumption. These higher internal pools stayed almost constant, even after aeration is switched on again and were reflected by higher extracellular L-lysine concentrations  and lysE (L-lysine exporter) varied over the fermentation process in an inverse manner. The L-lysine exporter LysE is transcriptionally regulated by LysG [L-lysine is the positive effector of LysG, acting as a sensor for internal L-lysine concentrations. Here, lysE was found upregulated at t5, correlating with a high internal L-lysine pool. It was interesting to note that the lysA gene encoding diaminopimelate carboxylase, the final step in lysine synthesis, showed an inverse expression behavior. The reason for this is unclear since no transcriptional regulation of the argS-lysA operon [It was apparent that the s Figure . The tra by LysG . L-lysinA operon  is known by GeneGo and Ingenuity Pathways™  which also provide sophisticated metabolic pathway maps and functionality to map experimental data, ProMeTra uses open standards such as SBML, SVG and Web Services, provides free access to the functionality and offers several public pathway maps. Similar to ProMeTra, the commercially available systems contain a set of predefined pathway maps and allow users to generate their own pathway maps. They also allow to visualize quantitative experimental data from e.g. metabolomics or transcriptomics measurements. Nonetheless, the MetaCore and Ingenuity Pathways system can only visualize results of multiple experiments, time points and dosages through animated graphics according to the systems manuals. This is a limitation since animated pathway visualizations can not be used in publications or on posters. A comprehensive overview of e.g. the progress of a fermentation experiment as presented in Figure We have created the web-based ProMeTra application that is able to visualize and combine data from complex functional genomics experiments. The user-friendly web interface allows researchers to easily visualize and integrate their datasets on pathway maps using the established SVG graphics format. Additional information and graphical content can be added to the vector-based images using drawing software . Unlike other tools such as Omics-Viewer  or MapMaThe API of the ProMeTra system has an object oriented, modular design. The popular Design Patterns have been used in the creation of the application and allow to easily extend the system to include new data sources and visualization methods. With the use of the MVC approach, we have created a versatile and extendable web interface.Through the conversion of CellDesigners SBML pathways, we provide means to use the pathway mapping functionality with existing pathway maps and we plan to extend this by conversion of the pathway maps from the KEGG database to our enriched SVG format. In summary, ProMeTra offers a flexible and extendible approach for the analysis, visualization, and integration of functional genomics datasets. Since ProMeTra can access complex experiments processed in MeltDB, Emma 2, and Qupe, we have a system that allows the experienced researcher to focus on the interpretation of the experimental results in an intuitive and visual way rather than on the time consuming conversion of vast tabular data into hopefully meaningful results.C. glutamicum could easily be generated if additional experimental data becomes available.The visualization of transcript abundances from the application example on ProMeTra GenomeMaps confirmed known transcription units and provides an intuitive genome wide overview on transcriptomics datasets. The function of ProMeTra to visualize transcript abundances and metabolite pool deviations onto user-defined pathway maps verified that coherence between transcript level and metabolite pool sizes exist. The visualizations of the application example confirmed existing knowledge and spurred new insights in gene regulation and the corresponding phenotype of cells. In this application example, we used data from two functional genomics techniques, namely transcriptomics and metabolomics. ProMeTra is by design not limited to these data-sources. The pathway map that we employed for this study contained identifiers for the compounds and the transcripts. An extended version of this pathway map that also contains identifiers for the proteins of . The project info page at  provides further information. We have set up a wiki page together with a user manual in PDF format which details the work flow of a typical ProMeTra analysis, both is available at . Researchers can access the system using the public prometra account without further registration. To review the generated SVG images, a recent web browser which supports the SVG image format is needed. For Microsoft Internet Explorer, a SVG plugin is required wich is freely available from Adobe.ProMeTra is publicly available at The authors would like to point out that this is a joined work of MP and HN and that both have equally contributed to the ProMeTra system and pathway maps. HN has designed and implemented the ProMeTra web application and database. MP has performed the experiments and contributed the biological background and experimental description as well as the biological results and discussion sections. MP has also written the ProMeTra user manual available via the project wiki. TB has implemented an initial prototype and contributed to the development of ProMeTra. JW has contributed to the core ProMeTra API, JS has provided code for the conversion of CellDesigner models. SA and MD have contributed the Qupe and Emma 2 web services and provided substantial intellectual input. AG and JK have supervised the project and manuscript. All authors have read and approved the manuscript.Biological material and methods. Details on the bacterial strain, media and growth conditions as well as the metabolomics and transcriptomics analyses applied in this study.Click here for file