Most research scientists working in the fields of molecular epidemiology, population and evolutionary genetics are confronted with the management of large volumes of data. Moreover, the data used in studies of infectious diseases are complex and usually derive from different institutions such as hospitals or laboratories. Since no public database scheme incorporating clinical and epidemiological information about patients and molecular information about pathogens is currently available, we have developed an information system, composed by a main database and a web-based interface, which integrates both types of data and satisfies requirements of good organization, simple accessibility, data security and multi-user support.From the moment a patient arrives to a hospital or health centre until the processing and analysis of molecular sequences obtained from infectious pathogens in the laboratory, lots of information is collected from different sources. We have divided the most relevant data into 12 conceptual modules around which we have organized the database schema. Our schema is very complete and it covers many aspects of sample sources, samples, laboratory processes, molecular sequences, phylogenetics results, clinical tests and results, clinical information, treatments, pathogens, transmissions, outbreaks and bibliographic information. Communication between end-users and the selected Relational Database Management System (RDMS) is carried out by default through a command-line window or through a user-friendly, web-based interface which provides access and management tools for the data.epiPATH is an information system for managing clinical and molecular information from infectious diseases. It facilitates daily work related to infectious pathogens and sequences obtained from them. This software is intended for local installation in order to safeguard private data and provides advanced SQL-users the flexibility to adapt it to their needs..The database schema, tool scripts and web-based interface are free software but data stored in our database server are not publicly available. epiPATH is distributed under the terms of GNU General Public License. More details about epiPATH can be found at  Infectious disease is defined as any illness caused by a specific pathogenic microorganism or its toxic product that results from the transmission of that agent or its product from an infected to a susceptible host, either directly or indirectly through an intermediate . There aMolecular genetic information is being increasingly used in the epidemiological study of infectious diseases to understand transmission, virulence or resistance patterns of microorganisms , pathogeTo study the interaction between a patient and a pathogen from a population, evolutionary and epidemiological perspective, a huge amount of molecular sequences is needed and current molecular techniques provide, with increasing affordability, this kind of data. An essential component to perform this kind of studies is having all these data properly organized and accessible ,6 to allUntil now, programs to perform molecular epidemiology studies of infectious diseases were, on one hand, devoted to the storage of information on patients and, on the other, public databases repositories  or interAn information system consists of a group of elements related according to some rules, which provides the necessary information to fulfil its purposes. The goals of such an information system are the collection, processing, storage, production and presentation of data. Our information system is composed by a main MySQL  databaseFrom the moment a patient arrives to a hospital or health centre to the analysis of sequences of the infectious pathogen in the laboratory, lots of information are collected. We have extensively discussed with researchers and physicians that actually work in these and related fields about their needs and finally we have divided all the sources of information into 12 conceptual modules around which the database schema has been organized  diagram defines the entities of the database and their relationships, so we can have a global view of the information stored, as shown in Figure The data model chosen to represent data is the relational model , composeViews utility of MySQL. A view is a virtual or logical table composed for the result of a query. Using views allows different accesses to the same database schema, either for different users, pathogens or any database characteristic of interest. Views can be created as needed by the system administrator and they also contribute to the security and integrity of the data stored preventing a user from editing or deleting other user's data.In designing the database we have taken into account all the existing infectious pathogens, either virus or bacteria, so the system can store information from different organisms at the same time. This is very useful mainly in cases of multiple infections, when studies involve more than one pathogen per patient and all relevant information  should be available. In these situations, it is easier for users and system administrators to deposit and control all the information in the same database schema instead of using different or independent database schemas. Moreover, the infectious pathogen general design contributes to the versatility and suitability of this software that can be used in many different ways as needed by different users. For example, epiPATH could be used for a unique pathogen exclusively or multiple pathogens simultaneously related studies: a user may start studying one single pathogen, such as HCV, and later he starts a new project with HCV/HIV coinfected patients or with a completely new pathogen. In this case there is no need to modify this software and, obviously, the learning curve is reduced to a minimum. Furthermore, although this database schema has been designed for multiple pathogens, it can be used as a database for individual pathogens on a per user basis by means of the Conceptual modules, in which information is divided, add versatility to this software so that users choose which kind of information is relevant for their studies by storing that part of the database schema. Moreover, advanced SQL-users could adapt it to their needs since epiPATH is distributed under GNU license.InnoDB tables because they allow transactions. Currently, it is implemented in MySQL Server version 5 but it also has been implemented in earlier versions of MySQL Server without any problem. Database design and scripts to create the schema have been developed and obtained with DBDesigner 4. Scripts were debugged and implemented into the RDMS with MySQL Query Browser . Tes. Tes16].Views utility. At the same time, it incorporates all data in a unique schema for those cases when multiple infections and comparisons between different pathogens are relevant, and additionally, being a single software for many different users and projects contributes to the internal daily work and data organization of many different institutions. The web-based interface is a very easy-to-use tool that allows the management of users' data stored in a local MySQL database server. The database schema and the web-based interface of epiPATH are open source software under the GNU license and can be modified by end users as needed.epiPATH is designed to be a local software for laboratories and institutions interested in molecular epidemiology, population genetics and evolutionary biology of infectious diseases. The database schema covers all relevant information of these fields and becomes a very complete and versatile tool. Due to its modular conceptual tables it can be used in a full manner or only in those parts of information that a researcher is actually interested in. Being a multi-pathogen designed database, it can be used as virtual database for a single pathogen or on a per user basis by means of the We performed a usability test  18]19] [19] 18][ [19] 18]Subjects were given a 10-minute tutorial and they were asked to complete 5 different tasks on a test database. Each participant was tested and recorded independently. The main results are summarized in Table In the near future we plan to develop and integrate in the web-based interface new and already existing analytical tools for this kind of data in order to study thoroughly infectious pathogens from molecular, population, epidemiology and evolutionary points of view.epiPATH is an open source information system for storing and managing data from clinical and molecular information of infectious diseases, which is available for download and local installation, in order to help researchers in related fields in their daily tasks in the laboratory and in collaborative projects with other institutions, preserving the information from public access given the sensitivity of the data under analysis. It is a very complete, suitable and versatile tool and an unique software for many different users and projects that satisfies requirements of good organization, simple accessibility, data security and multi-user support system.Project name: epiPATHProject home page: Operating system(s): Client: a web broswer ; Server: a MySQL Server (epiPATH distribution with views requires MySQL 5 or higher) running on UNIX/Linux or Microsoft Windows systems.Programming language: Web-based interface: HTML, PHP and JavaScript; Database: SQL/MySQL.Other requirements: Apache HTTP Server, MySQL client and MySQL Query Browser.License: GNU General Public License.The epiPATH software is freely available from the above web site. Data stored in our database server are not publicly available.RDMS – relational database management systemSQL – structured query languageHCV – hepatitis C virusHIV – human immunodeficiency virusER – entity-relationshipHTML – hypertext markup languagePHP – php hypertext pre-processorHTTP – hypertext transfer protocolAA developed and implemented the database schema and the interface. FG designed the project and supervised its development. AA and FG wrote the manuscript.The pre-publication history for this paper can be accessed here:Description of database terms. Detailed information of each field and table of the database schema.Click here for file


The completion of the Human Genome Project has resulted in large quantities of biological data which are proving difficult to manage and integrate effectively. There is a need for a system that is able to automate accesses to remote sites and to "understand" the information that it is managing in order to link data properly. Workflow management systems combined with Web Services are promising Information and Communication Technologies (ICT) tools. Some have already been proposed and are being increasingly applied to the biomedical domain, especially as many biology-related Web Services are now becoming available. Information on biological resources and on genomic sequences mutations are two examples of very specialized datasets that are useful for specific research domains.The architecture of a system that is able to access and execute predefined workflows is presented in this paper. Web Services allowing access to the IARC TP53 Mutation Database and CABRI catalogues of biological resources have been implemented and are available on-line. Example workflows which retrieve data from these Web Services have also been created and are available on-line.in-silico analysis.We present a general architecture and some building blocks for the implementation of a system that is able to remotely execute workflows of biomedical interest and show how this approach can effectively produce useful outputs. The further development and implementation of Web Services allowing access to an exhaustive set of biomedical databases and the creation of effective and useful workflows will improve the automation of  The Human Genome Project has transformed biology. Since its completion, the field has expanded to the management, processing, analysis and visualization of large quantities of data from genomics, proteomics, medicinal chemistry and drug screening.This huge amount of data and the heterogeneity of software tools that are used for its distribution make the tasks of searching, retrieving and integrating the information very difficult. Data is typically retrieved and analysed by hand by accessing several bioinformatics servers and by transferring the data by means of FTP clients or web browsers often using the "cut and paste" technique.Generally speaking, data integration needs stability that in turn can be provided by standardization of information systems, well established domain knowledge, well defined data and well defined goals. Instead, data integration in biology suffers from heterogeneous data and systems, uncertain domain knowledge, fast evolution of data, highly specialized data and the lack of predefined and clear goals.There is a clear need for a system that can improve information accessibility. Such a system should be able to automate the accesses to the remote sites, in order to retrieve the information from the specific database or to make use of the appropriate software tools to achieve the desired analysis. At the same time, in order to create the correct links between data it should also be able to "understand" the information that it is managing, i.e. its' semantics. Among current Information and Communication Technologies (ICT) technologies, workflow management systems in conjunction with Web Services seems to be the most promising solution.Workflows are defined by the Workflow Management Coalition as "computerized facilitations or automations of a business process, in whole or part" . Their gSome workflow management systems have already been proposed and are being increasingly applied in the biomedical domain. Some of them are add-ons to other tools, like biopipe , a perl Other systems are autonomous applications that are being developed either by industries, like the Bioinformatic Workflow Builder Interface – BioWBI from IBM , and PipWeb Services (WS) are software oriented network services which communicate usually by using SOAP  over HTTP. They offer a good, standard solution for automated retrieval of information. Standards are available or have been proposed for their retrieval and identification, description and composition . They alMany WS and WS deployment tools have recently been set up in the biomedical domain and perspectives for their widespread use has been proposed ,8. AmongBiological data are usually found in primary or secondary databases. While the former are the unique reference for all different research domains, the latter are often very specialized databases that are essentially useful for specific domains for which they represent the most important data.Essential but often underestimated biological information includes biological resources such as microbial strains, human and animal cell lines, plasmids, phages, plant cells and plant cell viruses,. They are collected in specialized centres where they are appropriately characterized and stored with detailed descriptions of the resources being held in catalogues. This information is not only intended to identify single elements of the collection, but also to fully describe their biological properties and special functions. Often catalogues include bibliography references that can help scientists to find further information. The main limitation to accessibility of this information is that catalogues are usually separate and scientists are therefore obliged to search many of them in order to find the resource that is the most appropriate for their research.Linking information that is available in culture collection catalogues to bioinformatics databanks available in Internet can further significantly improve accessibility of biological resources and can make the collections' holdings more effectively available after a search in molecular biology databanks and vice-versa. As a consequence, additional certified information can be made available to researchers and a growing number of them will hopefully refer to culture collections and make a wider use of biological material of certified quality. In order to promote this, extensions of catalogue information are being carried out at many centres, depending on their research interests.In oncology, one of the most interesting sources of information relates to mutations, either induced or spontaneous, occurring in genomic sequences and their products. One of the most studied proteins is the human p53 protein (reference sequence is SwissProt P04637) whose importance in the regulation of the apoptosis process in the cell is well known and recognized. This protein can be considered as a tumour suppressor, in the sense that it prevents the occurrence of tumours by promoting apoptosis in pre-cancer, altered cells. Modifications in the regulation of the apoptosis process are positively correlated to the presence of mutations in the TP53 gene (reference sequence is EMBL X02469), thus suggesting that it can, at least partially, be the cause for missing functionality. Moreover, the presence of mutations in the TP53 gene has been observed in about half of all cancers, in the 80% of all colon cancer tumours, in the 50% of lung cancer tumours, and in the 40% of breast cancer tumours.We present here the architecture of a system that has been designed to manage, organize and execute a set of predefined and tested workflows of interest to oncology. We also present a set of Web Services which allows the researcher to search and retrieve information from CABRI catalogues of biological resources and our implementation of the IARC TP53 Mutation Database. Finally, we present some of the workflows that have been designed with the twofold aim of allowing access to above Web Services and of representing an example of data integration that can be achieved through workflow management systems.We have designed a general architecture of a system  as part of the  project . It allo project  can optiThe system includes three main blocks: the workflow manager, the user interface and the workflow executor. Workflows are created and tested by an administrator using the Taverna Workbench. They are then stored in a repository by adopting the Taverna Simple conceptual unified flow language (Scufl) format. At the same time, their main processing steps are annotated in workflows profiles by using a specially designed ontology. This ontology describes bioinformatics tasks on the basis of their input and output data, processing type and application domain. These main processing steps may actually represent more real tasks grouped to achieve a functionally significant processing step.The user interface supports end users authentication and profiling and allows for the selection and launch of workflows. Workflow selection can be assisted by the user profile and by searching through annotated workflows significant processors.Workflows are executed by the third block that is based on FreeFluo and it is also able to store input and output data of actual workflow executions, so that they can later be analysed and possibly reused.Common Access to Biological Resources and Information (CABRI)  is a netLinks from CABRI catalogues to other databanks available on-line and vice-versa were added as part of the the European Biological Resource Centres Network (EBRCN) project , funded CABRI catalogues have also been implemented in SRS , a well known search engine for biomedical databanks . This waThe implementation was carried out by first comparing the data structures and contents of all collection databases and then defining common data sets, unique for the different kind of resources. Guidelines for data input and authentication have also been defined and agreed upon. These often include references to common data sets and vocabularies .For each catalogue, information is completed with reference data sets referring, e.g., to culture media, synonyms, hazard and restrictions to distribution. For many catalogues, explicit links have been defined to Medline and plasmids' maps, while links from EMBL Data Library to CABRI catalogues have been added in the EMBL version 81.The TP53 Mutation Database  of the IRelease 10 includes 21,587 somatic mutations whose description has been derived from 1,876 papers, 1,839 of which are included in the Medline database. Information on somatic mutations includes data on the mutation, the sample, the patient and his/her lifestyle. Reference vocabularies and standardized annotations are used extensively for the description of the mutation, tumour site, type and origin and for literature references. Examples of the former are ICD-O  and SMOMED nomenclatures.This database is available through the IARC web site , where qThe IARC TP53 Mutation Database and all its data subsets have been downloaded and reformatted for easier indexing and searching through SRS. Reformatting involved a few changes and additions in the database fields and their contents. All datasets have then been implemented as separate libraries. Data fields have been defined in agreement with controlled vocabularies that were used for data input at IARC, so that SRS indexes are based on them as well.All IARC TP53 Database datasets have been made available on-line and can easily and effectively be queried through SRS query forms . Due to Some original Web Services have been implemented. They allow the retrieval of information from CABRI and TP53 databases through a programatic interface, by also allowing for the inclusion of calls in pipelines and workflows management systems. We implemented Web Services allowing for the execution of a search either by name, by identifier or free text to CABRI catalogues and of a search by interesting properties towards the IARC TP53 Mutation Database.Integration of these data with other sources was planned by using either unique IDs or common terms. In order to do this, two types of services were implemented, searching either for a specific feature and returning IDs or searching for an ID and returning full records.Web Services were implemented by using Soaplab, a SOAP-based Analysis Web Service providing a programatic access to applications on or through remote computers ,21. SoapOnce the server has been installed new Web Services are added by defining simple definitions of the related execution commands (see figure A set of Web Services have been designed and implemented by using Soaplab and are now available on-line, both through their own WSDL descriptions  and the Workflows have been designed to access to and to retrieve data from the CABRI and IARC TP53 Mutations Database Web Services. They can also be viewed as an example of data integration that can be achieved through workflow management systems. Scufl formatted versions of these workflows have been created and are available on-line  for use We have presented in this paper a general architecture for the implementation of a system that is able to execute workflows of biomedical interest remotely. We have presented as well some Web Services and workflows that have been designed and implemented to allow automated retrieval through a programatic interface of information related to CABRI catalogues of biological resources and to the IARC TP53 Mutations Database.in-silico analysis.The further development and implementation of Web Services allowing the access to and retrieval from an exhaustive set of molecular biology and biomedical databases being carried out by many research centres and network service providers in the biological and medical domains and the creation of effective and useful workflows by interested scientists through widely distributed workflows management systems such as those presented in this paper will significantly improve automation of


Many proteomics initiatives require integration of all information with uniformcriteria from collection of samples and data display to publication of experimental results. The integration and exchanging of these data of different formats and structure imposes a great challenge to us. The XML technology presents a promise in handling this task due to its simplicity and flexibility. Nasopharyngeal carcinoma (NPC) is one of the most common cancers in southern China and Southeast Asia, which has marked geographic and racial differences in incidence. Although there are some cancer proteome databases now, there is still no NPC proteome database.The raw NPC proteome experiment data were captured into one XML document with Human Proteome Markup Language (HUP-ML) editor and imported into native XML database Xindice. The 2D/MS repository of NPC proteome was constructed with Apache, PHP and Xindice to provide access to the database via Internet. On our website, two methods, keyword query and click query, were provided at the same time to access the entries of the NPC proteome database..Our 2D/MS repository can be used to share the raw NPC proteomics data that are generated from gel-based proteomics experiments. The database, as well as the PHP source codes for constructing users' own proteome repository, can be accessed at  The completion of human and other model-organism genome projects has provided a sequence infrastructure to allow an improved understanding of the dynamic processes of cellular signaling, regulation, and metabolism. Although all cells contain the complete genome, only a fraction of the genes are expressed within a given cell. Under different conditions or within different tissues of the same organism, a specific set of proteins are expressed and/or post-translationally modified to carry out the special function of the cell -3. The tIn order to understand the roles of different proteins played and to dissect protein-protein interaction networks, high-throughout methodologies are being applied in the emerging field of proteomics. As a result, large amounts of experimental data have been generated by high-throughout proteomics methodologies, such as large-scale two-hybrid systems, high-throughout mass spectrometry technology, and multi-dimensional chromatograph. Meanwhile, with the volume of proteomics information increasing rapidly, there has been a great need for a public proteomics repository and for exchanging these raw proteomics experiment data between labs . The rawRecently, a new special organization called PSI (Proteomics Standards Initiative) was founded at the HUPO (Human Proteomics Organization) meeting in Washington D.C. USA to define community standards for data representation in proteomics to facilitate data comparison, exchange and verification . Since tCurrently, there have been some XML models developed as standards with relevance to the whole of proteomics such as PEDRo, HUP-ML and AGML -11. Amon to construct a user's own proteome repository.NPC is one of the most common cancers in southern China and Southeast Asia, which demonstrates remarkable geographic and racial differences in incidence. Public proteome repository is the infrastructure to study the complicated mechanisms of caner. Although there are many cancer proteome databases, there has been no NPC proteome database to our knowledge. In this paper, we used HUP-ML editor to collect the raw NPC proteomics data inclued the experiment results and experiment conditions. Then, these XML documents were imported into Xindice database, and PHP was used to pass the query request from web clients to database manage system (DBMS) and to convert the query results back to the clients with HTML format. The PHP source codes can be downloaded from our website An example of an Xpath query result of the NPC gel-proteomics experiment data in the NPC 2D/MS repository is shown in Figure In our NPC proteomics repository, peak list of the monoisotopic peaks of every peptide mass fingerprint (PMF) maps is extracted with Mascot Distiller and saved as mgf file. All mgf files have been changed to text files and imported into HUP-ML documents. When the users click the hyperlink of MS-Map of the identified spot, the DBMS queries that node and extracts the monoisotopic peaks from ms_peak_list tag to PHP, which will then be transformed to a mimic PMF map. By this method, the mimic of raw PMF maps can be shared by everyone without limitation of file format defined by the mass spectrometry manufacturer. Figure There are currently two types of DBMS used to store proteomics experiment results, there are relational database manage system (RDBMS) and XML database system. Currently, most public 2D/MS databases adopt SWISS-2DPAGE database or free RDBMS, such as MySQL, to store and manage their data. The SWISS-2DPAGE database is based on the Make2ddb software of SIB (Swiss Institute of Bioinformatics). The backend database system of Make2ddb is PostgreSQL RDBMS. Although the SWISS-2DPAGE database is well established, certain important experimental information and raw data still can not be integrated into database, such as the condition of protein separation and identification, the detailed descriptions of experimental samples, the raw mass spectrometry maps and etc. If researchers use other free RDBMS, they have to spend a substantial effort on designing and optimizing the database for the information. The advantage of RDBMS is that it can be used easily to store, manage, and query the structural information because of its specially designed structural and relational model. Nevertheless, complicated data structure in the proteomics data integrated with HUP-ML model makes it difficulty to construct a proteomics repository with RDBMS because of some problems in mapping hierarchical structure to relational schema. In addition, if we use RDBMS as back-end and map the proteomics data into tables, such DBMSs force us to fragment our data into many pieces to satisfy the third normal form requirement. The fragmentation can also impose efficiency problems, as a query can cause the DBMS to perform many joins to reassemble the fragments into the original data.XML technology is the next generation of the Internet language. It has powerful capability in exchanging data, and XML technology is particularly well suited to represent biological data and methods and is presently the consensus choice in most areas including proteomics because XML is highly flexible and XML provides an open framework for defining standard specifications ,14-16. APEDRoDB is another new database system for storing, searching, and disseminating experimental proteomics data, and it stores the raw proteomics data as XML format with Xindice. PEDRoDB is a database system based on raw data capture software Pedro, which has been developed to encode laboratory data and to generate an XML-based PEML (Proteomics Experiment Mark-up Language) file based on PEDRo model for local storage or submission to a database. Unlike 2D/MS databases based on Make2ddb, which emphasizes more on gel annotations, the PEDRoDB database was designed to provide more information, allowing detailed comparisons of the ways by which the results were obtained . HoweverThe HUP-ML document uses a flat file structure and it can be treated as a database or a table of RDBMS in some sense. The related XML document can be directly put into the same directory and processed by the file manage system. But the functionality of this method is still insufficient, as it cannot provide the merit of a database, such as event security rescue mechanism, parallel control, and high efficient indexing and querying. Therefore, by deploying the NXD to process the HUP-ML documents, the whole system can be more efficient and secure.Xindice is an open source native XML database, featuring efficient querying based on Xpath, XUpdate support, and tight integration with existing XML development tools. However, Xindice is subjected to the common limitations of NXD because of its short existence compared with RDBMS, and not too many NXD-supported technologies and applications have been available.Both PEDRo and HUP-ML represent the current efforts in using XML technology to exchange the raw proteomics data. At present, it is a good choice to use an existing effort, PEDRo or HUP-ML, as a starting point for system design rather than a new one. To choose a raw proteome data capture software, we think that the annotation of gels may be more useful than detail description of experiment conditions. Therefore, we select the HUP-ML to intergate the different source information of gel-proteomics data.Peptide mass fingerprint map and tandem mass spectrometry are currently the two most commonly used technologies in proteomics for protein identification. Because the mass spectrometry used in different labs were made by different manufacturers, the raw PMF maps and MS/MS maps generated by different equipment use different file formats which can only be opened with the special software from the mass spectrometry manufacturers. This has greatly increased the difficulty for exchanging the raw mass spectrometry data. A standard peak list format, such as mzData, provided by PSI requires many agreements from the original software provided by the spectrometry manufactures to third party software developing companies and will be implemented into next version . We extrAlthough Xindice is fit for serving as backend of NPC repository, some factors should be taken into account in order to improve the performance of database query. Database indexes are a powerful technology to improve the efficiency of database querying. Suppose the browsers commonly use the protein name and NCBI accession to query database, here we adopt element "protein_name" and "protein_data" element's "accession" attribute to index the NPC collection but it costs almost the same time with no database index. Unexpected observation might be due to a bug with Xindice or a problem with our implementation. The size of the data file is another affecting factor. Now the size of a file integrating all the data of 216 spots into Xindice is about 600 KB, which can be considered as a medium sized file compared to the 5 Mb Xindice file limit. Because Xindice was specifically designed for managing many small to medium sized documents, it is not a good way to integrate everything into one file even though the present sizes of NPC documents are still acceptable. Integrating everything into one big file increases the file complexity and the required more time for database query, especially as the identified spots increase. We think one solution is to extract the data of each spot into a single file and import all these files into one collection when the data expand, and this is also an important optimization step involved in constructing our NPC repository. Although the benchmark test of database has not been performed, it is better to be done before optimizing and tuning the database.With our PHP source code, 2D/MS experimental data can be delivered over the World Wide Web in an easily understandable format. One advantage of our platform inherent to representing information as alphanumeric strings is that the data can be easily stored and transmitted between different computer platforms and applications using the emerging XML technology, which is particular suitable for the development of proteomics web-services. Another advantage of PHP plus XML is that this platform can be rapidly constructed and it can greatly decrease the efforts on databases designing, storing and exchanging between different labs by using the same standard formats. Our website provide information more focused on the results of 2D/MS experiments, such as identified spots, 2-DE maps, Peak Lists.Fresh nasopharygeal biopsy specimens obtained from a pool of 5 patients presented with symptoms possibly indicative of NPC at the Xiangya hospital, Hunan province, China, were used in this study. The specimen was immediately frozen in liquid nitrogen after excision and flushing out of blood, and stored at -80°C until analysis while were histological proven low-differentiated squamous cell carcinoma. The protocols for sample preparation, 2-DE and spot identification by mass spectrometry were the same as previously described . The seaWe use Compaq compliant 370 running Windows 2000 Professional as our computer server. We use J2SDK1.4.2 java development environment, Apache1.3.29 as web server, and PHP sever to take the requests of client's browser and return the search results of XML documents to the browser. Xindice-1.0 as a native XML database was installed on the database sever to store and manage the collection of raw proteomics XML documents, to process the query request, and to update the experiment results using XUpdate. Xindice-XMLRPC 0.6 was installed on the web server to serve as a simple XML-RPC access API (Application Program Interface) to manipulate the Xindice database. XMLDBGUI was downloaded from DSTC and installed on the web server to monitor the Xindice status on the local computer and update the repository with XUpdate function at the local machine as shown in Figure The XML repository has been designed according to the rules proposed by Appel that were successfully used in constructing the 2D/MS database of ExPASy . Unlike FL and MYL implemented the software and coordinated the data capture activity. PFZ and JLL conducted or led experimental activities that generated the data in the database, and contributed to feedback on querying the database. ZQX and ZCC oversaw the database design and development activity, and the latter led the write-up.


A key abstraction in representing proteomics knowledge is the notion of unique identifiers for individual entities (e.g. proteins) and the massive graph of relationships among them. These relationships are sometimes simple (e.g. synonyms) but are often more complex (e.g. one-to-many relationships in protein family membership).We have built a software system called LinkHub using Semantic Web RDF that manages the graph of identifier relationships and allows exploration with a variety of interfaces. For efficiency, we also provide relational-database access and translation between the relational and RDF versions. LinkHub is practically useful in creating small, local hubs on common topics and then connecting these to major portals in a federated architecture; we have used LinkHub to establish such a relationship between UniProt and the North East Structural Genomics Consortium. LinkHub also facilitates queries and access to information and documents related to identifiers spread across multiple databases, acting as "connecting glue" between different identifier spaces. We demonstrate this with example queries discovering "interologs" of yeast protein interactions in the worm and exploring the relationship between gene essentiality and pseudogene content. We also show how "protein family based" retrieval of documents can be achieved. LinkHub is available at hub.gersteinlab.org and hub.nesg.org with supplement, database models and full-source code.LinkHub leverages Semantic Web standards-based integrated data to provide novel information retrieval to identifier-related documents through relational graph queries, simplifies and manages connections to major hubs such as UniProt, and provides useful interactive and query interfaces for exploring the integrated data. Biological research is producing vast amounts of data  at a prodigious rate. Most of this data is made freely available to the public, and this has created a large and growing number of internet and world wide web-accessible biological data resources which are characterized by being distributed, heterogeneous, and having a large size variance, i.e. huge, mega-databases such as UniProt  down to Semantic Web [Web search engines and hyperlinks are the basic and commonly used ways to find things on the web and navigate web content but they do not enable fine-grained cross-site analysis of data. To improve upon this, one key issue is the need for standardization and its widespread use, and tools supporting and enabling it. Biological data is too vast for brute-force centralization to be the complete solution to data interoperability. We must have standards and systems for people and groups to work independently creating and making data available  but still in the end all or most of the pieces of biological knowledge and data are connected together in semantically rich ways. The W3C's [ntic Web -6 is a pntic Web  are alsontic Web .A key abstraction or "scaffold" for representing biological data is the notion of unique identifiers for biological entities and relationships among them. For example, each protein sequence in the UniProt database is given a unique accession, e.g. Q60996, which can be used as a key to access its UniProt sequence record. UniProt sequence records also contain cross-references to related information in other databases, e.g. related Gene Ontology and PFAM identifiers . Two identifiers such as Q60996 and GO:0005634 and the cross-reference between them can be viewed as a single edge between two nodes in a graph, and conceptually then a large, important part of biological knowledge can be viewed as a massive graph whose nodes are biological entities such as proteins, genes, etc. represented by identifiers and the links in the graph are typed and are the specific relationships among the biological entities. Figure homologs) and likely have the same or similar functions. Gene Ontology consists of three widely used structured, controlled vocabularies (ontologies) that describe gene products such as proteins in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner. The conceptual graph of identifier relationships is richly connected, and a transitive closure even a few levels deep can lead to indirect relationships with a great number of other entities. Being able to store, manage, and work with this graph of entities and relationships can lead to many opportunities for interesting exploratory analysis and LinkHub is such a system for doing this.A basic problem preventing this graph of relationships from being more fully realized is the problem of nomenclature. Often, there are many synonyms for the same underlying entity caused by independent naming, e.g. structural genomics centers assigning their own protein identifiers in addition to UniProt's. There can also be lexical variants of the same underlying identifier (e.g. GO:0008150 vs. GO0008150 vs. GO-8150). Synonyms are a small part of the overall problem, however, and more generally there are many kinds of relationships including one-to-one and one-to-many relationships. For example, a single Gene Ontology or PFAM identifier can be related with many UniProt identifiers . PFAM represents an important structuring principle for proteins and the genes they come from, the notion of families (or domains) based on evolution; proteins sharing common PFAM domains are evolutionarily related  and see how many and which of them are present as evolutionarily related homologs in worm (C. elegans), also known as interologs [Proteins rarely act in isolation and often interact with one another and other molecules to perform necessary cellular actions. Experimental determinations of protein interactions are expensive and computational methods can leverage them for further interaction predictions. With this query we want to consider all the protein interactions in yeast  but are not expressed into functional proteins; they are regarded as defunct relatives of functional genes ,22. In tAbstractly, for each yeast gene name in the list of essential genes, we determine its pseudogenes by traversing identifier type paths in the LinkHub graph like the following:yeast gene name → UniProt Accession → yeast pseudogeneFor each essential yeast gene we then determine how many pseudogenes it has. We can then inspect the list of essential genes to see if there is a relationship between essentiality and number of pseudogenes. Humans have a large number of known pseudogenes  but geneyeast gene name → UniProt Accession → Pfam accession → human UniProt Id → UniProt Accession → Pseudogene LSIDPart of the SeRQL for the first query (for yeast pseudogenes) and results from both can be seen in figure data warehousing [federation [The basic conceptual underpinnings of LinkHub, i.e., the importance of biological identifiers and linking them, was given by Karp . LinkHubehousing ,28 and fderation -31. Essederation . LinkHubLinkHub differentiates itself by not integrating all aspects of biological data but rather focusing on an important and more manageable high-level structuring principal, namely biological identifiers and the relationships (and relationship types) among them; hyperlinks to identifier-specific pages present in the "Links" section of the LinkHub web interface give access to additional attributes and data. In fact, our YeastHub system addressed integration more generally by transforming many datasets to common RDF format and storing and giving RDF query access to them in an RDF database. The problem with YeastHub was that the integration was thin, with rich connections among the integrated datasets being limited. LinkHub is thus useful and complementary to YeastHub in this respect as a "connecting glue" among the datasets in that it makes and stores these cross-references and enables better integrated access to the YeastHub data; the example queries above demonstrated this.LinkHub's primary web interface can be viewed as a kind of "Semantic Web browser". Other work has also attempted to build browsers for Semantic Web data, including HayStack , SealifeCurrently, LinkHub has limited web document hyperlinks attached to its nodes, and if this could be increased the utility of the novel information retrieval based on querying the LinkHub relational graph, e.g. "path type" interface, would be enhanced. We are working to leverage the rich information in the LinkHub relational graph for enhanced automated information retrieval to web or scientific literature (MedLine) documents relevant to identifier nodes, e.g. proteomics identifiers, in the graph. A simple search for the identifier itself would likely not give optimal results due to conflated senses of the identifier text and identifier synonyms. In general, we need to consider and query for the key related concepts of an identifier, and these are present in the LinkHub subgraph surrounding the identifier. We consider the web pages attached to the identifiers in the subgraph as a "gold standard" for what additional relevant documents should be like, and we plan to use them as training sets to construct classifiers used to score and rank additional documents for relevance. We feel that this idea could be generalized and that the Semantic Web, which provides detailed information about terms and their relationships, could be leveraged to provide enhanced automated information retrieval or web search for Semantic Web terms.We also hope to explore how other relevant Semantic Web-related technologies could be effectively used in LinkHub, in particular named graphs  and LifeOur paper demonstrates the natural use of Semantic Web RDF to inter-connect identifiers of data entries residing in separate web-accessible biological databases. Based on such a semantic RDF graph of biological identifiers and their relationships, useful, non-trivial cross-database queries, inferences, and semantic data navigation can be performed through web interactive and query access. In addition, these semantic relationships enable flexible and novel information retrieval access based on queries of the LinkHub graph's relational structure to web documents attached to identifier nodes. LinkHub also can simplify and manage connections to major hubs such as UniProt for a lab or organization. LinkHub can be evaluated by considering its current active and practical use in a number of settings. We have already established the "hub of hubs" relationship between UniProt and LinkHub (i.e. UniProt cross-references to our LinkHub). In addition, LinkHub cross-references the targets of the structural genomics initiative to UniProt and serves as a "related links" and "family viewer" gateway for the Northeast Structural Genomics Consortium with which we are affiliated; LinkHub also serves as the "family viewer" for MolMovDB. LinkHub is a step towards answering the question "a life science Semantic Web: are we there yet?" .A key problem in populating the LinkHub database (described below) is how to determine the relationships among biological identifiers, a specific case of the so-called ontology alignment problem ,43. Biol and , and it is actively used and populated with data from the Gerstein Lab [Efficient, exact sequence matching programs were developed and used to do quick inter-database cross-referencing or alignment based on exact sequence matches . A custom Perl module was developed and used to index UniProt  to supptein Lab  and relatein Lab ) to UniPtein Lab  with whitein Lab , and psetein Lab .LinkHub is conceptually based on the Semantic Web (graph) model and we thus represent it and store it in RDF. RDF is a popular data model  for the Semantic Web that represents data as a directed labelled graph. Essentially, in RDF URIs  are usedThe relational structure of LinkHub, shown in figure The primary interactive interface to the LinkHub database is a web-based interface  author and is responsible for the majority of the work, implementation, and writing. KYY did the conversion of the relational (MySQL) version of LinkHub to RDF, integrated it into YeastHub, and wrote and executed the demo RDF queries over the joined YeastHub/LinkHub. KHC, MS, and MBG are faculty advisors and provided high-level direction and guidance to the work.


The cell cycle is one of the biological processes most frequently investigated in systems biology studies and it involves the knowledge of a large number of genes and networks of protein interactions. A deep knowledge of the molecular aspect of this biological process can contribute to making cancer research more accurate and innovative. In this context the mathematical modelling of the cell cycle has a relevant role to quantify the behaviour of each component of the systems. The mathematical modelling of a biological process such as the cell cycle allows a systemic description that helps to highlight some features such as emergent properties which could be hidden when the analysis is performed only from a reductionism point of view. Moreover, in modelling complex systems, a complete annotation of all the components is equally important to understand the interaction mechanism inside the network: for this reason data integration of the model components has high relevance in systems biology studies.In this work, we present a resource, the Cell Cycle Database, intended to support systems biology analysis on the Cell Cycle process, based on two organisms, yeast and mammalian. The database integrates information about genes and proteins involved in the cell cycle process, stores complete models of the interaction networks and allows the mathematical simulation over time of the quantitative behaviour of each component. To accomplish this task, we developed, a web interface for browsing information related to cell cycle genes, proteins and mathematical models. In this framework, we have implemented a pipeline which allows users to deal with the mathematical part of the models, in order to solve, using different variables, the ordinary differential equation systems that describe the biological process.This integrated system is freely available in order to support systems biology research on the cell cycle and it aims to become a useful resource for collecting all the information related to actual and future models of this network. The flexibility of the database allows the addition of mathematical data which are used for simulating the behavior of the cell cycle components in the different models. The resource deals with two relevant problems in systems biology: data integration and mathematical simulation of a crucial biological process related to cancer, such as the cell cycle. In this way the resource is useful both to retrieve information about cell cycle model components and to analyze their dynamical properties. The Cell Cycle Database can be used to find system-level properties, such as stable steady states and oscillations, by coupling structure and dynamical information about models. Biological systems are made by different, multi-functional elements which interact selectively and often non-linearly in order to produce coherent and complex behaviours. Generally the effective behaviour of a biological system is not predictable In systems biology data integration is an important approach to better understand the main features of a biological process, because it represents a way to combine interesting information related to the reaction involved in a specific network. A biological process of particular interest in systems biology is the cell cycle, a complex and crucial event for the life of every organism. The cell cycle implies the interaction of a large number of genes and proteins which create complex networks of cellular transduction signalling. The knowledge of the molecular aspect of this biological process is crucial in the context of cancer-oriented research. The study of the cell cycle is of great importance because it involves many proteins which form a complex network of interactions, but also because it is related to other relevant biological processes, for example the apoptosis and mitogenic signalling pathways.well-stirred chemical reactor and sufficient concentrations, ODE are very useful to mathematically express the dynamical behaviour of a molecular interaction network in time. The models simulations can be useful to identify the emergent properties of the system and are also useful to analyze some peculiarities of a biological network. Other methods for the mathematical modelling of a biological system, which differ from the differential equation model in the way to define the state of the system, are available, but they are less suitable than ODE based models.The key elements of systems biology studies are the models, which can be defined as abstract representations of biological components and processes in order to mathematically describe their structural and dynamical properties. Biological processes can be represented as a network of reactions which can be described in deterministic terms using ordinary differential equations (ODE) systems, in order to mathematically simulate their dynamics. Indeed, granted two basic assumptions, the In order to annotate the different model components, systems biology studies have to tackle the problem of finding information related to all the elements involved. In this scenario the need to collect information about genes and proteins in a unique resource becomes a crucial problem, despite the fact that several resources on biological pathways, like KEGG (Kyoto Encyclopedia of Genes and Genomes) Pathway Database  and ReacThe KEGG Pathway Database covers a larger field because it is a wide collection of pathway maps for metabolic processes, genetic and environmental data from different organisms such as signal transductions pathways and human diseases. In the KEGG Pathway Database there is the map of the cell cycle that reviews the main reactions of this cellular process. For each component of the KEGG pathway a short report is given: the report contains only the essential information both for genes and proteins and basic links to some genomic and proteomic databases are provided.Reactome is a curated resource for human pathway data related to biological processes which relies on information about single reactions grouped into pathways. The Reactome data enlarges the concept of a biochemical reaction to include, for example, the association of two proteins to form a complex, or the transport of an ubiquitinated protein into the proteasome. Reactome contains the principal reactions of mitosis and the checkpoints of the human cell cycle and it can be used as a curated source of cell cycle related information. However, this resource does not integrate the cell cycle related pathways, such as the MAP kinase signalling pathway and apoptosis pathway. Since it is principally based on the assembly of single reactions, it lacks the complexity of the entire cell cycle pathway.Since we are considering the cell cycle process from a systems biology point of view, the main repository of biological models must be taken into account. The BioModels Database  is the rJWS Online, a systems biology tool for the simulation of kinetic models from a curated SBML model database , is anotBoth JWS Online and Biomodels allow the model simulation powered by the software Mathematica (web version 2.0) and a static visualization of the simulation results is possible. However none of them gives users the possibility to directly simulate the ODE system. Biomodels, CellML and JWS Online contains a considerable number of cell cycle models, but none of them is complete since some cell cycle published models are missed.The aim of our project is to give an exhaustive view of the cell cycle process starting from its building-blocks, genes and proteins, arriving to the pathway they create, represented by the models. We have developed a new database able to collect the most important information related to cell cycle genes and proteins, which are drawn from the analysis of the cell cycle information available in literature and the existing pathway databases. Furthermore, we have built a repository of the most recent published cell cycle models, at the moment based on ODE systems, to allow the exploration of their mathematical structure through SBML components and their mathematical simulation. The integration system is designed to be automatically updated and to be easily integrated with information about other organisms and models.The Cell Cycle Database is a new resource which collects useful information about genes and proteins involved in the cell cycle process and the cell cycle models, in a wider systems biology context.Saccaromyces cerevisiae and the Homo sapiens. We primary consider cell cycle information from human organism since we intend to create a resource as support to biomedical studies in the context of cancer research. Then we extend the database content towards the budding yeast cell cycle in order to create a link between these two evolutionary correlated organisms. Saccharomyces cerevisiae is a widely used model organism in systems biology: it is relatively similar in its structure to human cells and many human key-proteins belonging to relevant cellular processes, such as cell cycle and signalling pathways, were first discovered by studying their homologs in Saccharomyces cerevisiae. Moreover in the context of systems biology and the mathematical modelling the budding yeast is the most studied organism, since the experimental data can be obtained easier from yeast than human cells. In conclusion, these two organisms were chosen due to the evolutionary conservation of the logic of basic regulatory mechanisms between them [We started integrating information from two eukaryotes, which are the budding yeast een them , the deeThe data we collected are based on KEGG and Reactome gene information. The database contains the human and yeast genes involved in the complete cell cycle pathway and in the MAP kinase signaling pathway, the human genes involved in the apoptosis pathway from KEGG, and it also integrates more specific information related to mitotic and checkpoint pathways from Reactome. Starting from these data, the database system is able to automatically perform the retrieval of the information related to each gene and protein by querying several freely available external biological resources. The information retrieval has been developed through a set of programs used for importing specific information about genes and proteins into the database.Saccaromyces cerevisiae (SCPD) for promoter sequence information [The data sources selected for the yeast and human gene information are Entrez Gene for the general information about genes  that is ormation  and YEASormation . For humormation , Mammaliormation , the Datormation . Moreoveormation , Unigeneormation , the Quaormation  and Onliormation .We also considered different data sources for yeast and human protein information, such as Uniprot for general information about proteins , such asWe developed the integration system through a data warehousing approach , which asnowflake schema [The relational database, which is managed by a MySQL server, has been implemented using a data warehousing approach with a e schema . The datsnowflake schema is a method of storing data in a relational database. This schema presents a core table, where main data about yeast and human genes are stored. The core table is connected to many external tables, where auxiliary data about genes, proteins and models are stored. The external tables are all linked to the core table by a 'one-to-one' or 'one-to-n' relationship through the specific identification number (ID) for genes and proteins. The snowflake schema has been chosen in order to facilitate the automatic data insertion and the automatic updating of the database content. The automatic updating system has been realized through a pipeline that automatically performs the queries to the public databases in order to import new data into the database.The in cascade, while when a new entry is inserted in one of the external table no inward updating occurs. As a result all tables of the database are updated according to the infrastructure which is designed for automated data integration.The database administrator can update the database content by gene name through the web interface and he can also verify the status of the updating pipeline through messages which can be read directly on the web interface When a new entry is inserted in the core table, all the external tables will be updated A specific section of Cell Cycle Database has been created to store yeast and mammalian cell cycle models published in recent literature and based on linear and nonlinear differential equations systems. In order to achieve complex behaviours, like oscillations, and to fit experimental data, these models often use algebraic relations, delays and events.As the primary data source we consider the BioModels Database  from whiThe relevant point of this section is the possibility to directly simulate models stored in the Cell Cycle Database. We have chosen to use the simulation software XPPAUT , a powerCell Cycle Database is accessible through a web interface made up of a set of HTML pages dynamically generated from PHP scripts. The user interface allows the user to browse of the data integration system in order to retrieve information about genes and proteins related to the cell cycle process. Users can query the database contents by inserting the gene/protein name and selecting the organism of interest, or by using gene/protein IDs of public databases. Moreover, users can query the database using key-words. The key-word search engine allows database exploration by typing a single word or a sentence in order to retrieve a list of genes related to the concept. This engine performs a match between the key-concept and the gene's and protein's description, in order to retrieve a list of genes and proteins which deal with the key-concept itself. Another query possibility is the sequence similarity search by using the BLAST algorithm  which isFinally a search related to the cell cycle models stored in the database is possible: users can retrieve the list of the mathematical models, choose one of them and visualize significant information on the web pages, such as wiring diagram, model description, main model players and a direct link to the mathematical section through which the mathematical simulation is possible.Mathematical expressions included in SBML models are coded following MathML specifications. To put them on the web we create a XHTML+ MathML page in a pop-up window. In this page MML is in-lined with HTML and at the beginning of the page an instruction calls a XSL stylesheet which allows the formulas to be viewed correctly.The use of XHTML+MathML technology allows the generation of high quality documents in which the search for a particular component included in the expressions is possible. Moreover it is possible to change the size of the page content as one can do with text in a HTML page, operation that is not possible if the maths is shown relying on images or other kind of objects, as is the case in the majority of websites.The principal aim of our work is to integrate cell cycle information which can be useful for researchers in the context of systems biology studies. From the user's point of view, this work presents two important features: the first is a data integration system for genes and proteins involved in yeast and human cell cycle processes; the second is a section dedicated to cell cycle models and their mathematical simulation.The database has been developed in order to provide users with complete information on each gene and protein involved in the cell cycle process of different organisms, starting from yeast and human, and to automatically maintain the information stored in this resource up to date. The web interface presents two distinct reports, one for the genes and the other for the proteins Figure , containThe gene report lists all the information related to each gene which is stored in the database, starting from the basic gene description, its sequence and its corresponding protein, but also including more specific information, such as the list of the SNP characterizing that gene, or the list of cDNA and isoform. Furthermore in the gene report, particular attention is given to the information related to the promoter regions and to the transcription factors specific for each yeast and human gene, in order to facilitate research on cell cycle gene regulation. We also provide links to experimental data on gene expression taken from the GEO (Gene Expression Omnibus ) repositAs far as the protein report is concerned, particular attention is given to the network of protein-protein interactions involved in the cell cycle. The database contains protein-protein interactions taken from several resources making the information on the cell cycle interaction network as complete as possible. In the protein report the graphical visualization of the domains from the InterPro database  is proviIn recent years a large number of mathematical models have been developed both for budding yeast -49 and mEach model is presented in a report which is structured in three sections: the publication data, the SBML data structure, the numerical simulation part. The first section contains the detailed publication data , the diagram of the model and the related XML file, if available, and the list of all the proteins involved in the model which are linked to the related Cell Cycle Database protein report.In the SBML data structure section users can explore the SBML components of the selected model including its mathematical expressions Figure . Users cx axis and one or more for the y axis, in order to plot their behaviours. In this way users can plot both time courses and phase diagrams [Figure The simulation section allows users to submit a simulation job and to plot results on the fly in order to capture the model dynamical properties. For a selected model the web interface lists its species, parameters, algebraic rules and XPPAUT internal options, using default values. Users can change the initial values in order to test the robustness of the selected model against changes of initial concentrations and kinetic parameters. Many types of integration methods, which differ according to the computational efforts required and according to the stiffness of the Initial Value Problem (IVP), are provided. When the computation is completed users can download XPPAUT input and output files and plot results. The web interface allows users to select species (variables of the IVP), one for the s Figure . Resultss Figure , the popUsing this system the user can interact with the model, with the possibility to dynamically simulate the ODE system, in order to verify its robustness and its properties. The simulation of different cell cycle models is useful to verify common behaviours of this biological process and allows the retrieval of dynamical properties along with system structure. Thus the importance of the data integration and simulation system presented here in the context of systems biology studies on the cell cycle consists in the immediate availability of gene and protein related information and in the possibility, through simulation, to identify hidden or emergent properties of the system.The cell cycle data integration system has been developed with the aim to facilitate research on the cell cycle, in particular in the context of systems biology. In order to fully understand the complex behaviours of the cell cycle components their dynamical properties are of fundamental importance. Now the key features of this complex pathway, such as emergent properties, can be understood through the analysis of the model's dynamical behaviours using numerical simulations. According to this idea, the Cell Cycle Database focuses on cell cycle models and is developed with the aim to integrate the information related to genes and proteins involved in this process. The significant information related to cell cycle genes and proteins is a useful annotation of the models' components and facilitates the exploration of the relevant features of the whole network. The structure of this resource allows the storage of new data deriving from cell cycle models, due to its particular structure and the pipeline for the automatic data updating.Future developments for this work will be the dataset improvement by integrating information on how each gene or protein interacts with other genes or proteins. We will also include the cell cycle information of other organisms such as mouse and Xenopus, as mathematical models are already available, with the aim to analyze the correlation of cell cycle data among different eukaryotes. Moreover, we intend to perform other simulation analysis in the context of cell cycle modelling using XPPAUT: first of all the bifurcation analysis will be implemented and will be also available through the web interface. We also plan to include different simulation methods and related software, such as Petri nets, Boolean networks and language-based simulations. Regarding the availability of the SBML for all the models stored in Cell Cycle Database, we are working on the manual SBML generation since for some models the SBML is not yet available in literature., using the most popular web browser .The Cell Cycle Database can be freely accessed at the URL:  and Java 3D API  on their browser.To view the Java applet users have to install Java Plug-in To properly view MathML formulas correctly users have to install a font plug-in.RA 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. EM implemented the cell cycle model section of the database and the simulation pipeline. LM coordinates the database specification and the whole project. All authors read and approved the final manuscript.


The number of molecular biology databases continues to explode. Presently, few problems in genomic molecular biology can be addressed without analyzing data stored in them. However, these databases reside in many different locations and often use nonstandard data formats requiring specialized data parsers. As a result, integrating and comparing data from multiple biological databases is difficult and tedious. Genome databases offer solutions to this problem by integrating data from multiple databases in a uniform and standardized manner. However, principally because of the intrinsic complexity of genome data, exploiting the full power of these databases also has a considerable learning curve. This is particularly true if one wants to query multiple genomic regions in an automated manner, rather than simply analyze individual genes, one at a time. Here, using the University of California Santa Cruz (UCSC) Genome Database for illustration, I describe tools that have been developed for facilitating automated, genome-database–querying and present some applications for which they are well-suited.The genome browsers at UCSC , Ensemblone hundred polymorphisms rather than just a single one. Interactively verifying which ones are already in dbSNP, or are within CpG islands, or are represented by ESTs, or are at highly conserved sites would quickly become tedious, time-consuming, and error prone, even with an integrated genome browser. Similarly, if one has identified hundreds, or even thousands, of genome locations of interest from a microarray experiment, and wants to ask a set of biological questions about each of them, interactive querying is not feasible.However, for all its power and convenience, interactive querying of genome databases does have limitations. For example, we can imagine a situation where we have identified Moreover, without batch-querying, many important biological questions cannot practically be addressed at all. One might want to search for new genes, by looking for regions where ESTs overlap gene predictions and also are highly conserved among related species. Or one might want to study exon evolution, by searching for ALU repeat sequences found in coding exons . Yet anoConsequently, both the UCSC and Ensembl Genome Browsers include tools for direct and automated “batch”-querying of their underlying databases. Currently the NCBI MapViewer Browser and the model organism databases do not offer integrated batch-querying tools, but NCBI is planning to introduce such tools for the MapViewer Database in the future . The aim of the present work is to introduce the reader to the available, batch-querying tools, to illustrate some of their features and capabilities, and to indicate the types of applications for which they are useful. The reader is assumed to be familiar with interactive use of at least one of the genome browsers. No programming experience is required for performing basic batch-querying procedures. However, for the more complex tasks described, experience with some computer language such as C, Perl, or Java, as well as familiarity with database-querying, i.e., Structured Query Language (SQL) , is neceBatch queries of the Ensembl and UCSC Genome Databases are possible using the conventional database SQL  or usingSo, for example, with the Table Browser, we can answer our earlier question of identifying which of 100 polymorphisms were previously known (as evidenced by dbSNP annotations), in the following manner. First, we would make a custom table  of the genomic coordinates of our polymorphisms, if necessary obtaining the coordinates from the gene sequences themselves using the UCSC BLAT tool . Then wehttp://g2.bx.psu.edu). Galaxy is a relatively new Web site that provides a set of built-in post-processing tools  that caAlthough Galaxy alleviates the difficulties of multiple table intersections, our example also highlights a more fundamental limitation to the Table Browser approach. This limitation is that answering biological questions often requires data post-processing, and this post-processing may sometimes be more involved than what one can be accomplished using just the Galaxy toolset. For example, to determine if our polymorphisms have been observed in an EST not only requires intersecting our custom table with the EST table, but also obtaining the actual EST sequences, and, finally, inspecting those ESTs at the polymorphism locations. If we have 100 polymorphisms and multiple ESTs overlapping any single one, manually screening all of the ESTs is not feasible. Similarly, the example of searching for candidate ADAR sites by comparing genomic DNA with EST data also requires writing sequence-comparison software in addition to batch-downloading of data.For more complex types of genomic data analysis, it may be necessary to write one's own computer program or script to perform the required post-processing of the data. Now, in such cases, it is of course possible to simply take the data downloaded from the Table Browser or Biomart and analyze it with software written “from scratch” in any computer language of one's choice. However, this is typically an inefficient approach. Rather, it is generally preferable to use the extensive API libraries of data extraction and manipulation tools that have already been developed by the Browser Teams for these purposes. In this way one has immediate access to functions that perform essentially all the data manipulations that we are accustomed to performing interactively on the respective browsers.http://hgdownload.cse.ucsc.edu/admin/jksrc.zip). These programs were originally developed by Jim Kent and are typically referred to as the “kent source tree.” The included utility programs, which can generally be run either in stand-alone mode or be incorporated into one's own code, include programs for sorting, splitting, or merging fasta sequences; record parsing and data conversion using GenBank, fasta, nib, and blast data formats; sequence alignment; motif searching; hidden Markov model development; and much more. Library subroutines are available for everything from managing C data structures such as linked lists, balanced trees, hashes, and directed graphs to developing routines for SQL, HTML, or CGI code. Additional library functions are available for biological sequence and data manipulation tasks such as reverse complementation, codon and amino acid lookup and sequence translation, as well as functions specifically designed for extracting, loading, and manipulating data in the UCSC Genome Browser Databases. The code is open-source and, except for the browser-specific libraries, is completely free to all. The UCSC-Browser–specific code, while free for academic, research, and personal use, does require licensing for commercial use.For the UCSC Databases, the principal API is in C and contains a comprehensive collection of utility programs and library routines  and then selecting “Describe table schema”  is the time required to learn how to use them and to obtain access to a copy of all or part of the database. The steepness of the learning curve is primarily due to the complexity of the data, but is also because these databases were designed more for interactive querying than for programmed use. In particular, programmed access to the UCSC database can be challenging to learn, since the appropriate table and code documentation are sometimes not easy to locate. For UCSC database table descriptions, often a good place to start is by selecting the table of interest in the Table Browser  exact mirror of the UCSC browser database, data extraction and manipulation code taken from the kent source tree will typically run without any modification .http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database), or the UCSC DAS [An alternative to using a public mirror, when data from only a small number of database tables is required, is to simply download the needed tables, using either the Table Browser, the UCSC http download site , and installation should be reasonably straightforward for someone with Unix experience. Installation requirements for building an Ensembl mirror are comparable with those for the UCSC system and are documented at http://www.ensembl.org/info/software/website/index.html. Setting up a private mirror does entail considerable initial effort. Moreover, if one's applications need genome annotations incorporating the most recent database updates, using a public mirror database may be advantageous. On the other hand, maintaining one's own private mirror database does provide several significant advantages. In particular, concern about the restrictions and limitations of using a shared resource are no longer relevant, and, once created, one has a permanent, customizable resource that can be used repeatedly for multiple investigations.Finally, the third option is to install one's own local mirror of all  of the genome database. Although performing such an installation requires a substantial amount of local disk space, is not trivial, and, at first, may even appear daunting, the steps involved are generally well-documented . SpecifWe now look at the code for the principal subroutine of the program shown in  in a bitWith these definitions, the subroutine first creates a list of all gene structures for all genes that overlap the input bed region. If the gene-data access method is via a mirror database (indicated by gpHash being null), obtaining this list of gene structures requires only a call to the library routine genePredReaderLoadRangeQuery (defined in genePredReader.h). If, instead, gene-table access is via a local file, one needs to write a short additional access routine bkToGenePreds shown in , which iOnce the list of overlapping genes is retrieved, the longest one is found by simply calling the linked-list sort function, slSort.  Next, the program is ready to cycle through all of the exons of the longest overlapping gene and to compute intron lengths by simple subtraction. Then the code determines whether the intron overlaps the input bed using the function positiveRangeIntersection, and, depending on the result of the intersection test, appends the length to either of the linked lists, overlapList or otherList, using the library routine slSafeAddHead. Finally, the memory allocated for the gene structures is freed.By examining this code, it should be apparent that, once the initial learning curve has been overcome, using the UCSC database and the kent source code can turn what would otherwise be a rather large programming project into a relatively straightforward exercise.Web-based, interactive querying of the genome databases has enabled the analysis of genomes in an integrated and visual manner that previously was difficult or impossible. However, many important biological questions cannot practically be answered using simple interactive methods that query only a single genomic location at a time. Addressing these questions requires batch- and programmatic database-querying. Although these approaches involve an initial, one-time cost of learning how to use the associated API and establishing an access method for programmed querying, the important capabilities they provide for addressing significant and otherwise relatively complex genomic questions often makes this effort well worthwhile. Moreover, as Web-based interfaces like Galaxy—which provide batch data acquisition and post-processing, but do not require programming—evolve, the powerful tools of genome-wide data analysis should become accessible to an ever wider range of biologists.Dataset S1Installation and usage instructions for the included sample software.(5 KB TXT)Click here for additional data file.Dataset S2Code for the example described in the article text.(445 KB TAR)Click here for additional data file.Dataset S3(7 KB TXT)Click here for additional data file.


Scientific articles are tailored to present information in human-readable aliquots. Although the Internet has revolutionized the way our society thinks about information, the traditional text-based framework of the scientific article remains largely unchanged. This format imposes sharp constraints upon the type and quantity of biological information published today. Academic journals alone cannot capture the findings of modern genome-scale inquiry.Like many other disciplines, molecular biology is a science of facts: information inherently suited to database storage. In the past decade, a proliferation of public and private databases has emerged to house genome sequence, protein structure information, functional genomics data and more; these digital repositories are now a vital component of scientific communication. The next challenge is to integrate this vast and ever-growing body of information with academic journals and other media. To truly integrate scientific information we must modernize academic publishing to exploit the power of the Internet. This means more than online access to articles, hyperlinked references and web-based supplemental data; it means making articles fully computer-readable with intelligent markup and Structured Digital Abstracts.Here, we examine the changing roles of scholarly journals and databases. We present our vision of the optimal information architecture for the biosciences, and close with tangible steps to improve our handling of scientific information today while paving the way for an expansive central index in the future. The scale of biological data varies tremendously and encompasses a wide array of results: from observations on the order of a single gene to genome-wide studies and comparative genomics. The specifics differ in each case, but this full range of data is inherently complementary: drawn from living systems, it invites interrelation. However, such interrelation proves difficult because our methods for dealing with these data differ depending on their size and the scope of inquiry.Ideally, scientists should record and share all useful findings. In reality however, results sometimes do not coincide with the standard ration suitable for journal publication. Some facts are simply too trivial to merit a whole paper, so isolated findings or negative results are often withheld from the published record. Conversely, some data sets are too large to include in article format. This latter problem is visible in a series of papers presenting whole-organism protein-protein interaction networks -4 or regJournals and databases are naturally positioned to handle different types and amounts of data. In addition, each offers a distinct and useful approach to the recording and transmission of scientific information.Journal articles are optimized for human consumption. They are straightforward to create, intuitively organized and incorporate authoritative peer-review; however, they are not suited to handle very large or very small results and lack the rigorous organizational structure required for machine indexing and recall. Moreover, academic publishing is largely dominated by corporate publishers whose subscription barriers make third-party indexing of full-text material difficult or impossible. Nonetheless, journal articles are the established currency of scientific research: investigators must publish papers to advance their careers.Databases are highly structured, efficient and machine-readable, but are not optimized for full-text discussion and require significant effort to establish and organize. They often lack true peer review – beyond a rudimentary oversight panel ensuring compliance with proprietary formats – and interrelating data between independent databases remains a challenge. Furthermore, contributing results even to flagship databases has yet to achieve similar status to publishing in a respected journal. Because no uniform citation system exists to track the database contributions of a given researcher , very little professional incentive exists to populate, annotate or revise information stored in databases. As such, large teams of curators manually extract small-scale data from journal articles and deposit them in databases at great cost and effort .The once-sharp distinction between journals and databases is beginning to blur. We now search abstracts and access journal articles in a manner similar to database objects. Gone are the days of paging through an entire journal issue; now, articles are accessed through database-type portals such as PubMed, downloaded in PDF format and consumed piecemeal. The structure and format of the scholarly article should reflect this mode of access.The optimal information architecture for biology would capture a broad range of data in digital format and facilitate database deposit alongside manuscript publication. It would index all full-text journal articles, associate keywords and identifiers with database records, and link textbooks, laboratory Web sites and high-level commentary. It would provide multiple levels of peer-review, community comment and annotation, and search results tailored to individual user profiles. This vast network of information would be interrelated, linked and accessed via a single seamless portal. Such a centralized system has been proposed before, and here we elaborate upon this vision .From a technological standpoint the aforementioned functionality is entirely reasonable. Indeed, current technology allows for tremendous integration in the way we sift and access scientific data.  What is needed is a set of standards to actually accomplish this integration, which, in turn, requires the cooperation of disparate corporate and government entities – often entrenched and resistant to change.de novo information architecture designed to exploit modern digital technology, the reality is that journals and databases exist today in a form that does not allow instant adoption of such a highly-connected structure. Nonetheless, we can still aim to improve our current system while working toward an interconnected future. We have identified three key issues that we feel will lay the groundwork for tomorrow's expansive super-index while improving access to scientific information today.Thus, as enticing as it is to imagine the vast functionality of a We must expand what it means to publish in the biosciences. Traditional journal articles alone are ill-suited to capture the fruits of modern research; and databases, Web sites, archived presentations and high-level commentaries are a valuable and real part of the scientific information landscape. Academic publication should reflect this.To modernize academic publishing, we propose three main changes.First, all scientists should be able to publish, share and access data on the Web. Funding agencies should include stable digital storage with research grants, and tie continued funding to the appropriate use of this storage. This ensures that every author is able to archive pre-prints, host supplementary and unpublished data, and make their findings widely available in digital format.PLoS Medicine), and a machine-readable XML summary of pertinent facts in the article which we term the Structured Digital Abstract  A Structured Digital Abstract will facilitate such deposit; individual journals could partner with related databases to decide on format and spearhead such practices. We might begin by using currently available information extraction software such as BioRAT  – a prog®) [A key thrust of our future vision is the interconnection of disparate data sources: journal text is intelligently linked to database resources, third-party commentaries, archived talks and Web sites. To begin this process, we must establish a reliable way to identify database objects – similar to the Digital Object Identifier (DOI®)  already ®) . This coAs a corollary, editorial boards should regulate a move towards a unified, standard naming convention for biology. The LSID proposal to unify database-specific ID-management issues into a single system and assign a unique identifier to all objects in the life sciences represents a promising advance towards such unified nomenclature.Once consistently labeled, gene names and other biological identifiers in article text should be annotated for association with their database counterparts. This need not be overly tedious, as much of this process could be automated and implemented by journals during publication; a software system could identify most putative anchors and produce a checklist which authors would simply approve or alter ; the namThe efficient interrelation of biological data sources has been the subject of much recent work. In particular, current applications of the semantic web to the biological world are promising: for instance, projects such as Atlas  (large-sImmediate gains will accompany Web search engines indexing the full text of scientific articles. Already underway to some degree with the Google Scholar service , this wiUntil widespread open access to published literature is a reality, local archiving on institutional Web space is a convenient stopgap to permit interim indexing of full-text documents. It is estimated that over 90% of academic journals allow some form of author archiving, but with widely differing rules . Until tScientific contribution should not be measured solely by journal publications. Database maintenance is already vital to modern research, and we should implement a consistent citation system to credit database contributions. Full-text publication will remain the cornerstone of the research process – after all, human-readable discussion will always be in high demand – but recognition should also be afforded to those who create, maintain and update the database records we depend upon daily. If database contribution is properly acknowledged, we will see more widespread attention devoted to maintaining these key resources in the future.Moreover, the ability to quickly establish if an idea has previously been put forth – and to properly credit it, if so – is important to scientists. Full author identification and centrally searchable content will simplify this process, and facilitate attribution and acknowledgment.PLoS One [Community annotation of published research is a key step towards harnessing the full power of the Internet in scientific communication. The new journal PLoS One  offers cFinally, overall progress toward our future vision will likely change how we view authoring and editing in science. Specifically, curating biological databases is of increasing importance. This complex task demands scientific expertise paired with writing, editing, programming and database administration skills. We believe data management techniques will one day be taught to undergraduate-level scientists, as students and researchers of all levels learn to oversee and tend their corner of the digital data landscape.The scholarly publishing industry remains heavily invested in a dwindling share of today's scientific information landscape: the traditional journal. Change is in order. This does not mean we should rush headlong into building a massive digital index, for technologies must mature and stabilize to support such a system. Rather, it is time to recognize the centrality of biological databases to modern research, and work to better integrate them with the vast corpus of knowledge contained in journals, news sources, commentaries and talks. To achieve this, we must consider how best to apply current technology to the task of capturing and communicating scientific information. By overhauling the publication process today, we not only improve our current handling of scientific information; we invest in a connected future, an information architecture capable of linking and interrelating knowledge like never before.MS and MG conceived of the commentary and collaborated in writing the article.