Trichophyton rubrum is the most common dermatophyte species and the most frequent cause of fungal skin infections in humans worldwide. It's a major concern because feet and nail infections caused by this organism is extremely difficult to cure. A large set of expression data including expressed sequence tags (ESTs) and transcriptional profiles of this important fungal pathogen are now available. Careful analysis of these data can give valuable information about potential virulence factors, antigens and novel metabolic pathways. We intend to create an integrated database TrED to facilitate the study of dermatophytes, and enhance the development of effective diagnostic and treatment strategies.T. rubrum during conidial germination in time-course experiments and challenged with antifungal agents are deposited in the database. In addition, comparative genomics hybridization results of 22 dermatophytic fungi strains from three genera, Trichophyton, Microsporum and Epidermophyton, are also included. ESTs are clustered and assembled to elongate the sequence length and abate redundancy. TrED provides functional analysis based on GenBank, Pfam, and KOG databases, along with KEGG pathway and GO vocabulary. It is integrated with a suite of custom web-based tools that facilitate querying and retrieving various EST properties, visualization and comparison of transcriptional profiles, and sequence-similarity searching by BLAST.All publicly available ESTs and expression profiles of T. rubrum and comparative results of dermatophytes. It is devoted to be a comprehensive resource and platform to assist functional genomic studies in dermatophytes. TrED is available from URL: .TrED is built upon a relational database, with a web interface offering analytic functions, to provide integrated access to various expression data of  Epidermophyton, Microsporum and Trichophyton. T. rubrum is the most commonly observed dermatophyte worldwide and especially dominant in onychomycosis with a prevalence of approximately 80% [Dermatophytes are fungi that can cause superficial infections of the skin, hair, and nails. They are the most common agents of fungal infections worldwide and impact millions of individuals annually ,2. Becautely 80% .However, very few biochemical identification procedures are available for dermatophytes; thus, diagnosis of dermatophyte infections is based on the gross and microscopic morphology of the colony. Fungal culture, which can be useful to confirm the diagnosis and treatment, would take two to four weeks and pleomorphic growth can lead to misidentification. Molecular epidemiology of an outbreak of fungal infections is also not possible due to the lack of molecular methods . Though T. rubrum were recently proposed for genome sequencing by the National Human Genome Research Institute [Fortunately, a set of five dermatophytes including nstitute . It woulT. rubrum life cycle, which represented a first significant step towards the comprehensive description of cellular functions involved in T. rubrum biology [T. rubrum cDNA microarrays from the EST clones and applied them in the following studies. One is tracing changes of genes expression during T. rubrum conidial germination in time-course experiments to reveal molecular mechanisms in developmental stage at the cell level [T. rubrum response to several antifungal agents to make clear the mechanism of drug actions in this pathogenic fungus [Recently, our group reported a sequencing program of over thirty thousands ESTs derived from ten different stages of  biology . Since mll level . The othc fungus ,9. And tT. rubrum needs to be well organized and presented for researchers focusing on dermatophytes. TrED is therefore developed to the accurate interpretation and incisive exploitation of massive datasets. The database integrates of genetic, transcriptomic and metabolomic data of T. rubrum as well as comparative genomics results of dermatophytes. As a result, TrED provides a wealth of invaluable information about the evolution, life cycles, cell biology, and virulence of dermatophytes.The enormous information from T. rubrum, (ii) transcriptional profiles of T. rubrum and (iii) CGH results of 22 dermatophytes.TrED currently collects the following three types of data: (i) EST sequences of T. rubrum EST sequences as well as a set of our newly determined ESTs derived from T. rubrum mycelia grown under conditions mimicking virulence. To suppress the potential sequencing errors that inherent in single-pass reads, all ESTs sequenced by our group were reevaluated from the original chromatograms by setting the Phred quality score cutoff to 20, which means less than one expected error per 100 base pairs (bp). Public data deposited by other contributors were directly retrieved from dbEST [The raw EST data in TrED include all publicly available T. rubrum response to different antifungal agents and its expression data during conidial germination reported previously were available from Gene Expression Omnibus (GEO) [Transcriptional profiles of us (GEO) . Data frAll ESTs were screened to remove contaminating sequences, including restriction site, adaptor, cloning vector, poly(A/T) tail and bacterial sequences by the trimming script SeqClean  with NCBT. rubrum sequences (unisequences) composed of 4,566 contigs and 5,658 singletons in the current release of TrED.Since ESTs are typically partial, redundant and error-prone, the TGICL software  was usedTrichophyton coding sequences (CDS) were limited from public domains as mentioned above, we used a combination of CDSs from genomes of two relatives, Coccidioides immitis and Aspergillus fumigatus, as the training dataset for building hidden Markov models. The 944 ESTs without a detectable coding region are largely (81.4%) singletons and short in size, which are probably mostly made of untranslated region.ESTScan2 was used to detect possible coding regions in all unisequences . Since aThe predicted peptides were then used for conserved protein families search in Pfam database  by HMMERT. rubrum [The nomenclature from MEROPS database was adopted for the classification of putative proteases found in . rubrum . Possibl. rubrum .TrED is built on a RedHat Linux 9.0 operation system and the data are stored as a MySQL relational database that is accessible directly through an Apache web server. The Perl programming language and some common modules, such as DBI, GD and CGI, are used to generate interactive web pages for the query interfaces. A revision of the KEGG pathway map-viewer  is emploThe weekly update of datasets for dermatophyte BLAST is accomplished through automated downloading new data from GenBank by BioPerl scripts. A bimonthly execution of BLASTX comparison between all unisequences in TrED and the current NR database is scheduled on a background Linux-cluster, and the results will be transferred and imported into the database in a semi-automated fashion by a series of Perl scripts.The TrED database supports the following basic tasks: (i) browsing by listing of cDNA libraries, assembled unisequences or related metabolic pathways, (ii) querying based on accession numbers, clone names, functional classifications or protein properties as well as sequence-similarity searching by BLAST, (iii) visualizing and comparing transcription profiles and CGH data and (iv) downloading the raw data and analysis results.T. rubrum including EST sequences and transcriptional profiles. Though most of them are also available from other public databases, such as dbEST and GEO, they keep only the raw data with virtually no biological content. However, researchers who interested in further data mining prefer resources with comprehensive biological information rather than raw data depositories. TrED described here are essential for this goal. It provides additional information for individual ESTs including assembly structure, predicted peptide, Gene Ontology associations, and multiple sources of comparison to infer functional annotation. Moreover, different types of data are interconnected within the database, which is very convenient for further interpretations.TrED integrated several different types of expression data for T. rubrum is a model organism for dermatophytes research and most data in TrED were directly derived from this organism. But the CGH results of 22 dermatophyte strains by the T. rubrum cDNA microarrays were also integrated into the database. It actually extends the contents of TrED to make it a valuable resource not only for T. rubrum study but also for studies of other dermatophytes. We further constructed a dermatophyte BLAST tool to facilitate the future researches in pathogenic superficial fungi (see below for details).In the TrED database, individual EST sequences are clustered and assembled to produce a set of more complete and reliable consensus sequences (i.e. unisequences) representative of putative genes. A typical page for display each of the unisequence contains the following information:• Basic features including sequence length, G+C content and number of related ESTs;• Clickable schematic view of assembly structure with the component ESTs color-coded by clone libraries (for contigs only);• Biologist curated tentative annotation for the unisequence (if assigned);• Functional classifications including Gene Ontology associations, KEGG Ontology (KO) classes and KOG clusters assigned (if any);• Sequence-similarity based prediction of enzyme nomenclature (EC numbers) and possible metabolic pathways involved (if any);• Peptidase family by the nomenclature from MEROPS database (for detectable protease only);• Graphical representation of potential Pfam domains and putative transmembrane regions in the sequence (if any);• Comparison table of its transcriptional responses to antifungal agents (if available);• Plot view of its expression during conidial germination in time-series (if available);• CGH results of 22 dermatophyte strains (if available);• Unisequence with clickable link for download in FASTA format;• Nucleic acid sequence of the potential coding region identified by ESTScan2 (if detectable);• Predicted peptides that conceptually translated by ESTScan2 (if detectable);Most of the above mentioned components of the webpage provide clickable inter links to other function pages within TrED, as well as direct links to related pages in other valuable public resources, such as GenBank, Gene Ontology, KEGG, KOG, Pfam and MEROPS. By combining various aspects of information with the ability to access different web repositories, TrED brings researchers a synergy of dynamic resources publicly available over the internet.However, the convenience of retrieving comprehensive information from a single page is disadvantaged by requiring more network resources, which could be a serious obstacle to users who have limited local net speed. To alleviate the problem, we setup a user-friendly configure menu for users to fully customize the content to be displayed in the page. It's also amenable to researchers who have specialized interests in particular aspects of the data.Another configurable feature for TrED users is the ability to customize similarity cutoffs for advanced data mining. Sequence-similarity based function interpretation is still the major annotation method widely used nowadays. However, the stringency of the homologous match that defines a biological function is not a constant applicable to all cases. A stringent criterion tend to lead to less information, while a loose one may result in vast of spurious matches. So predefined similarity cutoffs could be arbitrary to some researchers. The customized cutoff values take effect to all kinds of sequence-similarity based analysis in TrED, such as GO terms assignment and metabolic pathway reconstruction.T. rubrum conidial germination can be graphically represented in batch by choosing a dataset from the analysis menu. CGH results performed on 22 superficial fungal strains can be easily browsed in a tabular style upon request. Moreover, in order to facilitate further comparative analysis on the CGH data, an auto filter was set up to offer users the potential to rapidly examine commonly shared or lost genes within each genus.The microarray page available from TrED menu provides a platform for specialized explorations among all types of microarray-based data. The expression variations of genes during the T. rubrum response to three antifungal agents are integrated into TrED, including ketoconazole, amphotericin B and a novel synthetic fatty acid synthase inhibitor PHS11A [Currently, transcriptional profiles of r PHS11A ,9. TheseTrED provides a suite of web-based tools that allow users to query and extract information from the database: (i) text based search, (ii) annotation based function category enumeration and (iii) BLAST based sequence-similarity search.The text based database interrogation enables extracting the current tentative annotations using any querying keywords. A single entry, that is instantly familiar to users of other internet search engines, is offered for alternative query words, separated by blanks, or complex phrases enclosed by double quotation marks. The database is also searchable by using EST accession number, clone name, internal ID or a combination thereof.T. rubrum.The tentative annotation for each unisequence in TrED was manually assigned by biologists based on the sequence similarities among available resources, such as GenBank and Pfam. But potential false annotations are always unavoidable and they might mislead users in some cases. The extended text search engine was designed to amend this gap by providing users the ability to query the original top 100 BLASTX hits in NR database directly. As the BLASTX comparison results are automated update bimonthly, the extended search engine is particularly pertinent to active researchers by offering the most current information with potential clues for further deciphering the biology of T. rubrum biology. Functional classifications based on controlled vocabulary defined by GO, KOG cluster and KEGG ontology were currently adopted in TrED. The querying results, which depend on customized similarity settings (see above), are displayed in an explicit table with each hit represented by a row. Furthermore, each listed row in the output table provides inner link to the individual unisequence page as well as outer links to related public resources.Enumeration genes possibly related to given function category provides a simple way to retrieve specialized information for researchers focused on particular areas of BLAST algorithm has been widely used for fast sequence-similarity searches in numerous databases. By adopting a web-based BLAST server TrED allows users to perform sequence comparison against all sequences in the database. Moreover, to facilitate the genomic research on dermatophytes, we developed a dermatophyte BLAST tool in TrED. While sequence comparisons with common databases, such as GenBank, have been invaluable for gaining a broad understanding of genomes, single gene comparisons across the relative species are often useful to researchers focused on particular areas of biology . The derPerhaps one of the most characteristic features of the TrED user interface is the assignment of unisequences to various functional and structural categories, which allows users to focus on specific subset of the tremendous data.T. rubrum genome. From the current data, 1,520 (14.9%) unisequences occupy detectable transmembrane regions, including transporters, receptors, channels, sensory transducer and pumps as targets for drugs. But 67.2% of them are hypothetical proteins or orphan sequences, their real biological functions and medical significance require further investigations.Fungal factors contributing to virulence include antigenic variability, the presence of fungal adhesions, effective iron acquisition systems, etc. Most of these molecules are associated with the cell surface. So it's valuable to estimate the fraction of integral membrane proteins in the T. rubrum is classified to related peptidase family by nomenclatures from the MEROPS database [T. rubrum belong to metallo and serine protease family and 83.9% have homologs in A. fumigatus. Further investigations on protease substrate specificities will improve our understanding of their functions and contributions to virulence. Furthermore, the CGH results of these proteases could help to explain the relative specificities of different dermatophytes in causing different types of dermatophytosis.A biological characteristic of dermatophytes is their ability to invade keratinized tissues, so it is evident that secreted proteases are suspected pivotal virulence determinants. Each putative protease found in database . Most ofT. rubrum unisequences for functional domains and other known sequence motifs. 3,448 (33.7%) had one or more Pfam hits with an hmmer E-value cutoff of 1. T. rubrum shares three of its top ten Pfam domains  with C. immitis [A. fumigatus [We used Pfam database to search  immitis  and A. fumigatus .T. rubrum, certain aspects of the results will change. We will update the results described here on a regular basis and contribute to the community effort.Future TrED development will be dedicated to offer a platform for comparative genomics studies of dermatophytes. It will integrate sequence, expression and other data associated with dermatophytes. As with the ongoing efforts for genome sequencing of T. rubrum, and the integrated database TrED, significant progress shall be made in unraveling the intriguing biology of this medically important fungal pathogen of humans and its mechanisms of virulence. Gaps in our knowledge will be filled by a combination of comparative and functional genomics, including techniques such as transcriptomics, bioinformatics, and proteomics.The enormous influx of information from genome sequencing projects is revolutionizing the science of fungal pathogenesis. This ranges from understanding the most basic aspects of gene content and genome organization, to elucidating the mechanisms of host-pathogen interaction and the development of new diagnostic techniques and vaccines. With the release of the large set of expression data of  suitable for most graphical web browser. The users' browser need to enable cookie  for some database utilities, such as customized similarity setting and batch query process.The database is available at LC designed the website, participated in data evaluation and drafted the manuscript. JY constructed the database, performed the computational analysis and revised the manuscript. LW, WZ and TL contributed data to the database. QJ conceived of the study and supervised the work. All authors read and approved the final manuscript.


Expressed Sequence Tags (ESTs) are short and error-prone DNA sequences generated from the 5' and 3' ends of randomly selected cDNA clones. They provide an important resource for comparative and functional genomic studies and, moreover, represent a reliable information for the annotation of genomic sequences. Because of the advances in biotechnologies, ESTs are daily determined in the form of large datasets. Therefore, suitable and efficient bioinformatic approaches are necessary to organize data related information content for further investigations.Parallel Processing of ESTs), a pipeline based on parallel computing for EST analysis. The results are organized in a suitable data warehouse to provide a starting point to mine expressed sequence datasets. The collected information is useful for investigations on data quality and on data information content, enriched also by a preliminary functional annotation.We implemented ParPEST (The pipeline presented here has been developed to perform an exhaustive and reliable analysis on EST data and to provide a curated set of information based on a relational database. Moreover, it is designed to reduce execution time of the specific steps required for a complete analysis using distributed processes and parallelized software. It is conceived to run on low requiring hardware components, to fulfill increasing demand, typical of the data used, and scalability at affordable costs. The role of bioinformatics to support the Life Sciences has become fundamental for the collection, the management and the interpretation of large amount of biological data. The data are in most cases derived from experimental methodologies with large scale approaches, the so-called "omics" projects. International projects aimed to sequence the whole genomes of model organisms are often paralleled by initiatives for the expressed data sequencing to support gene identification and functional characterizations. Moreover, because of advances in biotechnologies, ESTs are daily determined in the form of large datasets from many different laboratories. Therefore, the analyses of expressed sequence data involve the necessity of suitable and efficient methodologies to provide high quality information for further investigations. Furthermore, suitable models for the organization of information related to EST data collections are fundamental to provide a preliminary environment for analyses of structural features of the data, as well as of expression maps and of functional relationships useful for the interpretation of mechanisms and of rules of gene expression processes.There are many software available for EST processing, with the purpose to clean the datasets from contaminations -4 and toExpressed sequence curated databanks are worldwide available. They consist of collections built starting from dbEST, using selected computational tools to solve the complex series of consecutive analyses. Some of the well known efforts are the Unigene database ,19, the Parallel Processing of ESTs), for the pre-processing, clustering, assembling and preliminary annotation of ESTs, based on parallel computing and on automatic information storage. Useful information resulting from each single step of the pipeline is integrated into a relational database and can be analysed by Structured Query Language (SQL) calls for a "ad hoc" data-mining. We also provide a web interface with suitable pre-defined queries to the database for interactive browsing of the results that is supported by graphical views.Our contribution to this research is a pipeline, named ParPEST  as default operating system and the OSCAR 4.0 distribution  that proThe main process of the pipeline is designed to serialize and to control the parallel execution of the different steps required for the analysis and to parse into reports the collected results.Input datasets are parsed by a specific routine so that information from the GenBank format or included in the FASTA format could be upload into a MySQL database.Sequence data are pre-processed in two steps, to clean the data and to avoid mis-clustering and/or mis-assembling. The first step requires RepeatMasker  and the PaCE  is the sThe functional annotation is performed using the MPI-Blast package . Raw ESTAll the results obtained from the single steps of the pipeline are recorded in a relational database and are managed through SQL calls implemented in a suitable PHP-based web interface to allow interactive browsing of all the structural features of each EST, their organization in the assembled contigs, the BLAST-derived annotations as well as the GO classifications.The pipeline performs a parallel analysis on large amount of EST data. Because of distributed computing there is no execution limit for the processes, that are allocated according to available resources. The free release of PaCE,  that we The pipeline has been tested on a cluster of 8 nodes single processor. In Table As expected, large datasets (>1000 ESTs) give the widest reduction of execution time increasing the number of nodes Figure . The exeWe implemented the software to make it independent of the resource manager server. Therefore, though we based the system on a PBS resource manager, it can be easily ported to other environment such as Globus Toolkit  or SUN GdbEST data are organized in GenBank format where organism, cloning library, development stage, tissue specificity and other information are usually available. While parsing the input file, a complete set of basic information useful to described the sequence are collected in the 'est' table of the relational database we designed Figure .Another table is used to describe vector contaminations according to the report the main process of the pipeline automatically produces during the pre-processing step Figure . TherefoClusters obtained from PaCE resulting in single EST sequence or in contigs, are collected in the database too. A specific routine included in the main process of the pipeline performs a deep analyses on the clustered sequence to derive information on how many ESTs belong to a single contig, and how many contigs are produced once the sequences are clustered.CAP3 assemblies sequences building a multiple alignment and deriving a consensus to obtain a contig. To use only high-quality reads during assembly, CAP3 removes automatically 5' and 3' low-quality regions (clipping step). Therefore, to keep information about the whole assembly process, both the complete alignment and the EST trimmed regions are recorded into the database.The table designed to organize the BLAST report from raw EST data as well as from contig sequence analyses, can include the five most similar subject sequences and their related information. Gene Ontology terms related to each BLAST hit are recorded into the GO table included in the database.The information obtained from the execution of the pipeline is stored in a MySQL database that provides a datawarehouse useful for further investigations. Indeed, all the information collected in the database can support biologically interesting analyses both to check the quality of the experimental results and to define structural and functional features of the data. For this purpose the database can be queried through SQL calls implemented in a suitable PHP-based interface. We provide a pre-defined web based query system to support also non expert users. Different views are possible. In particular, EST Browser Figure  allows uUsers can further select data based on the preliminary functional annotation, specifying a biological function as well as a GO term or a GO accession Figure . MoreoveCluster Browser Figure  is speciWe designed the presented pipeline to perform an exhaustive analysis on EST datasets. Moreover, we implemented ParPEST to reduce execution time of the different steps required for a complete analysis by means of distributed processing and of parallelized software. Though some efforts are reported in the literature where all the steps included in a EST comprehensive analyses are integrated in a pipelined approach -13, to oThe pipeline is conceived to run on low requiring hardware components, to fulfill increasing demand, typical of the data used, and scalability at affordable costs.Our efforts has been focused to fulfill all the possible automatic analyses useful to highlight structural features of the data and to link the resulting data to biological processes with standardized annotation such as Gene Ontology and KEGG. This is fundamental to contribute to the comprehension of transcriptional and post-transcriptional mechanisms and to derive patterns of expression, to characterize properties and relationships and uncover still unknown biological functionalities.Our goal was to set up an integrated computational platform, exploiting efficient computing, including a comprehensive informative system and ensuring flexible queries on varied fundamental aspects, also based on suitable graphical views of the results, to support exhaustive and faster investigations on challenging biological data collections.The design of the platform is conceived to provide the pipeline and its results using a user friendly web interface. Upon request, users can upload GenBank or Fasta formatted files..We offer free support for processing sequence collections to the academic community under specific agreements. We would welcome you to find contacts and to visit a demo version of the web interface at


Many attempts are being made to understand biological subjects at a systems level. A major resource for these approaches are biological databases, storing manifold information about DNA, RNA and protein sequences including their functional and structural motifs, molecular markers, mRNA expression levels, metabolite concentrations, protein-protein interactions, phenotypic traits or taxonomic relationships. The use of these databases is often hampered by the fact that they are designed for special application areas and thus lack universality. Databases on metabolic pathways, which provide an increasingly important foundation for many analyses of biochemical processes at a systems level, are no exception from the rule. Data stored in central databases such as KEGG, BRENDA or SABIO-RK is often limited to read-only access. If experimentalists want to store their own data, possibly still under investigation, there are two possibilities. They can either develop their own information system for managing that own data, which is very time-consuming and costly, or they can try to store their data in existing systems, which is often restricted. Hence, an out-of-the-box information system for managing metabolic pathway data is needed.We have designed META-ALL, an information system that allows the management of metabolic pathways, including reaction kinetics, detailed locations, environmental factors and taxonomic information. Data can be stored together with quality tags and in different parallel versions. META-ALL uses Oracle DBMS and Oracle Application Express. We provide the META-ALL information system for download and use. In this paper, we describe the database structure and give information about the tools for submitting and accessing the data. As a first application of META-ALL, we show how the information contained in a detailed kinetic model can be stored and accessed. and can be downloaded free of charge and installed locally.META-ALL is a system for managing information about metabolic pathways. It facilitates the handling of pathway-related data and is designed to help biochemists and molecular biologists in their daily research. It is available on the Web at  Modern molecular biological research produces large amounts of data. One main problem remains the elucidation and representation of relationships between the compounds of biological systems. A large number of information systems holding data from molecular biology has been developed by public and private research projects . A rapidAnother important problem biochemists and molecular biologists are faced with is the management of their own data. Currently, experimentalists can develop an own information system for managing that own data, but this is very time-consuming and costly. An alternative would be to try to get their data into existing systems. Often, this is not possible. In case that scientists have different opinions about certain items, pathway data needs to be stored in different parallel versions. However, this is not possible with most of the existing systems.In an attempt to overcome these problems and to provide the scientific community with a software system that meets their requirements, we initiated the META-ALL project. META-ALL is designed for management of detailed information about metabolic pathways, including reactions, translocations, substances, pathways, locations and kinetic parameters. The system contains a versioning system and a Web interface for entering and querying of data.Km value for enzyme x in compartment y of organ z of organism w?" or "If a value for organism x is missing, what value was measured in the closest relative  of that organism?"Meta-All is a software package that is initially distributed with an example data-set only. It can be installed locally to help biologists and biochemists in their daily research. The user can enter own experimental data as well as data sets from other sources such as publications or databases. Once the user's instance of Meta-All is populated with sufficient amounts of data, a variety of complex queries will be possible due to the detailed structure of the database schema. Examples for such queries are dependent on the filling of the system, and could be: "What is the In this paper, at first we give an overview about the technical background of the system. In the second part we describe the database schema, before we provide details about the user-interface we developed for managing the contents of the database. Finally, we discuss our system in relation to other systems.META-ALL uses the Oracle database management system, which is extensively used in academia and industry. Furthermore, Oracle provides several useful tools for easy creation of user interfaces and a security concept for protecting selected data. For the user-frontend, we use Oracle Application Express . It alloguest" and the password "meta-all" allows the user to see a pathway from sucrose breakdown in potato as a test data set [META-ALL can be accessed in two ways. First, we provide a demo instance of META-ALL on our server which is accessible via our project Web page . This indata set . The demdata set , which iThe database schema is divided into several parts. The main parts are conversions, substances, pathways, locations , references and versioning. A simplified version reflecting all objects storable in the database schema is shown in Fig. conversions and substances. A conversion is a reaction or a translocation, both of them either actively  or passively . All necessary information can be stored with every conversion: name and synonym(s), formula, type  and subtype . Conversions are categorised into pathways and pathways into super-pathways. Furthermore, some specific information can be included, e.g. for the layout of cycles . The substance is an umbrella term for transporters, enzymes, metabolites and macromolecules, the latter of which consists of one or more types of metabolite units. Each substance plays a certain role in a conversion, which can be catalyst in case the substance is an enzyme, or substrate (= input substance) in case the substance is a metabolite. Furthermore, a metabolite can be a modulator (activator or inhibitor) of a reaction, the product (= output substance) or act as a coenzyme. Each of the role information can be enriched by detailed information such as Vmax values, affinity constants, etc. Each piece of information is assigned to the location information and publications.The central parts of the schema are Conversions take place at different locations inside an organism depending on the developmental state and environmental effects, and the database schema reflects as many of these parameters as possible. We focused on the use of existing ontologies in order to have a controlled vocabulary allowing the comparison of data from different sources. For our instance of META-ALL we decided to use PlantOntology  terms toAs publications represent probably the most important knowledge source, it is crucial to store reference information with as many data points as possible. We decided to use the widely accepted standard of PubMed IDs instead of developing an integrated publication management. Hence, META-ALL stores the PubMed ID for each publication and a text field is provided to store necessary information for yet unpublished data. The record has to be updated manually once a PubMed ID is available. If using the META-ALL Web-Interface described below, the user is shown the PubMed ID and, if existing, the remark field. The NCBI Web page with the abstract of the publication can be opened in a new window using a provided link.The data for which META-ALL is intended usually has different quality levels. Hence, the storage of additional quality information and the possibility to store curated data, as it was pointed out in , is requThe META-ALL Web interface is the central user-interface to the META-ALL database Fig. . We usedMETA-ALL is an information system for managing data about metabolic pathways, which is available for download and local installation, in order to help experimentalists in their daily research. We provide both the database schema  and the user interface. It is available from the META-ALL Web page. Additionally, we provide a demo instance of the META-ALL system on our server.The pathway by which sucrose is transformed to starch in developing potato tubers has been subject of numerous studies over several decades (for a review see ). A deta• The enzyme name, EC number and stoichiometric formula.• The kinetic type of the reaction, together with the organism this was determined in.• The name of each metabolite, the role it plays in which reaction, and the binding- and dissociation order in these reactions.• The kinetic parameters of each reaction such as binding or inhibitory constants, maximal velocity and equilibrium constant; their value and unit; the organism, tissue, cell, and compartment the constant was measured in.• For all data, the reference and the according PubMed ID (if available) is given, together with a tag if this kinetic type was measured or estimated.The structure of the metabolic network has been exported from META-ALL as an SBML file and imported into the data visualisation system VANTED for a visualisation of the pathway. Figure Although there exist several information systems for storing metabolic pathway data, we decided to develop a new one, because many of the existing systems are not capable of meeting the requirements of experimentalists working on higher organisms. We analysed a variety of existing information systems concerning their advantages and disadvantages and made these information the fundament of our requirement analysis. An exciting study in this field is published in .An information system of high interest is SABIO-RK . It focuMETA-ALL uses an Oracle database management system for persistently storing metabolic pathway data and an Oracle Application Express user interface. An installation instruction containing database schema, user interface and initial data set can be obtained from the META-ALL project Web page. It can be used with either a commercial Oracle license or the Oracle Database 10 g Express Edition, which is available free of charge from Oracle Corporation.We prepared several reports for the META-ALL user interface and used our system in conjunction with VANTED for pathway visualisation. We plan to connect META-ALL to the Systems Biology Modelling Environment (SYBME)  to furthFor the future, we plan to facilitate the following application using META-ALL: the semi-automatic generation of kinetic models of a certain pathway. The procedure for this task will be as follows: the user enters the pathway that should be modelled and defines the boundaries of the model . An application of META-ALL then queries the database for all information about the enzymes present in this pathway at a specific location in a given organism. If a certain kinetic parameter is not available for an enzyme, it will be possible to take the data from the taxonomically nearest neighbour species for which the data is available with the help of the genetic distance matrix included in META-ALL. This is a common approach for constructing kinetic models, which is normally done through tedious literature searches. The data can then be presented to the user who can decide which data to keep and which to change. Subsequently, the model could be exported in standard formats such as SBML. We are aware that this is a future application implying that the database is filled with a sufficient amount of data, which is in the responsibility of the user. The user could, for example, import large datasets from existing sources and improve the data step by step. This procedure is supported by the quality and versioning tagging described in subsection Database schema.For the daily work, the existing Web-Interface should be appropriate. In the future, we plan to expand META-ALL by an SBML importer allowing the import of larger amounts of data at once.In this paper, we presented META-ALL, a metabolic pathway information system, which can be downloaded and installed locally. It is intended to support biochemists and molecular biologists in their daily research by providing a platform for the management of detailed information about metabolic pathways, including reactions, translocations, substances, pathways, locations and kinetic parameters. META-ALL contains a versioning system, quality tags and a Web interface for entering and querying of data. Pathways can be exported into SBML files for use in visualisation or simulation tools.Project name: META-ALL• Project home page: • Operating system(s): Client: only a Web browser required; Server: OS depending on the Oracle installation, e. g. Microsoft Windows, Linux• Programming language: User-interface using the Oracle Application Express technology and SQL, PL/SQL• Other requirements: Oracle DBMS 9i/10g (version 9.2.0.3 and higher) and Oracle Application Express 2.0 license if available, or Oracle Database 10g Express Edition • License: Apache License, Version 2.0• Any restrictions to use by non-academics: According to license• All authors participated in the design of the system. SW implemented the system. SW and BHJ designed the database schema and drafted the manuscript. All authors read and approved the final version.


Although biomedical information is growing rapidly, it is difficult to find and retrieve validated data especially for rare hereditary diseases. There is an increased need for services capable of integrating and validating information as well as proving it in a logically organized structure. A XML-based language enables creation of open source databases for storage, maintenance and delivery for different platforms.Here we present a new data model called fact file and an XML-based specification Inherited Disease Markup Language (IDML), that were developed to facilitate disease information integration, storage and exchange. The data model was applied to primary immunodeficiencies, but it can be used for any hereditary disease. Fact files integrate biomedical, genetic and clinical information related to hereditary diseases.. A fact file is a user oriented user interface, which serves as a starting point to explore information on hereditary diseases.IDML and fact files were used to build a comprehensive Web and WAP accessible knowledge base ImmunoDeficiency Resource (IDR) available at .The IDML enables the seamless integration and presentation of genetic and disease information resources in the Internet. IDML can be used to build information services for all kinds of inherited diseases. The open source specification and related programs are available at  Biomedical information is often very complex. Deciphering the roles of genes in human health and disease is a grand challenge for many reasons, including impediments to defining phenotypes, difficulties in identifying and quantifying environmental effects, technical problems in generating genotypic information, and the difficulties of studying humans . The comMost hereditary diseases are rare and the diagnosed patients for a condition are often randomly spread out in the world. One doctor usually has only a few patients with a disease. It is often difficult to find comprehensive and validated biomedical information related to rare diseases. In addition, it is more and more difficult to publish results in scientific journals only from a few cases even when they are interesting . Still, There is an increasing need for tools and services capable of integrating information from a variety of sources. Clinicians and researchers could benefit from a more consolidated and unified view of the available biomedical data. Systems biology researchers need to integrate disparate information from multiple public sources to merge with their own experimental data to generate models of processes. Biomedical data mining attempts to extract information from biomedical databases by using e.g. automated natural language processing (NLP) techniques . ProcessThe Extensible Markup Language (XML) is a standard created by the World Wide Web Consortium (W3C) for characterizing the content and structure of documents . It is dBeyond XML there are a number of additional specifications such as Document Object Model (DOM) , XML SchA unified data format of resources is required for comparison between similar diseases and reutilization of information. Here we present a new data model called fact file, which integrates biomedical information related to hereditary diseases into a Web and WAP accessible knowledge base. Our scope is wider than e.g. in gene oriented knowledge bases such as GeneCards , UniGeneThe fact files were applied to build a comprehensive, validated knowledge base for primary immunodeficiencies (PIDs) called ImmunoDeficiency Resource (IDR) ,24. It iFactFile> element is a root  were developed to facilitate disease information integration, storage and exchange in the first place for immunodeficiencies, but in principle for any hereditary disease. The IDML is an XML specification and container for bioinformatical data on hereditary diseases. The fact file data model schema was defined according to W3C XML specification  > (XML) > [XSLT] > (HTML) > Browser, where processors are in square brackets and products in round brackets. The output of XSP pages is structured XML content, which can pipe through XSLT to produce HTML. The XSP feature is not currently in use in the IDR. practically anywhere, anytime.The information on fact files can be easily transformed and presented in any platform. It is easy to write platform or even browser and screen specific pages. We have implemented a transformation from IDML to WML for portable devices (such as mobile phones) with WAP compliance (Wireless Application Protocol). The fact files are available via bioinformatics related WAP service, BioWAP ,48 practNew web techniques are developed continuously. During this project a number of new specifications and software appeared, requiring upgrading of the system many times. The separation of content and presentation enables to share the project for people who are responsible for information content and people who develop the knowledge management techniques. Once the data model was created, we have not had to touch it hardly at all in spite of technical improvements, content additions and deletions.As far as we know there are no other efforts to develop a markup language to describe connections between disease and genetic information. The IDML was designed with following purposes in mind. First, we wanted a markup that is able to present disease, clinical, diagnostic and genetic information and relations between them. Secondly, the data model structure had to be intuitive, hierarchical, flexible, but still machine and human readable. Sometimes the relatively large XML files can appear verbose for human readers, but hierarchically and logically organized structure in addition to semantic markup facilitate the interpretation of documents. Thirdly, an application and platform independent data format was needed. Its portability, extensibility and robustness are primary advantages for interoperating heterogeneous systems. The availability of open source and free tools for processing files in all major programming languages is important. The openness of source code as well as data formats and data itself allows better integration and interoperation between data resources. The IDML enables the seamless integration of genetic and disease information resources in the Internet. The data model is appropriate for the implementation of automated decision support systems such as diagnostic consultations. Fourthly, the data have to be unambiguous and validated.et al. lists some issues related to database searching and accessibility that can cause difficulties [A fact file is a user oriented user interface, which serves as a good starting point to explore information on hereditary diseases. For some time now, there has been many advanced search facilities in the Internet such as Google, that are able to find very fast web pages that contain given keywords. However, the web searches typically turn up innumerable completetely irrelevant "hits", requiring much manual filtering by the user. Navarro iculties  includinThere is an increasing number of biomedical data sources in the Internet. The Human Genome Initiative  and otheAlmost all pages in the Internet have been written in HyperText Markup Language (HTML) where it is used for style description. It provides some possibilities for simple description about a document. It is able to use special metatags that contain simple keywords or more advanced descriptions like Dublin Core Languages, but they are very little utilised and only the most sophisticated search engines can exploit them.There are some efforts to integrate heterogeneous biomedical databases ,54,55. SThe XML-based language IDML and fact file data model were developed for integrating, storing and exchanging information on inherited diseases. The IDML language and fact file model are implemented in the IDR knowledge base. The fact files can be easily transformed from IDML to any format such as HTML or WML using either standard W3C techniques or flexible custom code. The content management as well as the exchange of presentation are facilitated by separating document content and presentation. The IDML-based information system was proved to be a viable and applicable specification for inherited diseases. Numerous downloads  from the IDR knowledge base during the last two years have proved the applicability and adaptability of the fact file model.AxKit An XML Delivery Toolkit for ApacheBioWAP Bioinformatics service for portable devicesDOM Document Object ModelDTD Document Type DefinitionebXML Electronic Business XMLEDDNAL European Directory of DNA Diagnostic LaboratoriesEMBL Genetic sequence database by European Molecular Biology LaboratoryGDB Genome DatabaseGenBank Genetic sequence database by National Center for Biotechnology InformationHTML Hypertext Markup LanguageIDML Inherited Disease Markup LanguageIDR ImmunoDeficiency ResourceIE Information extractionIFNγ Interferon gammaNLP Natural language processingOMIM Online Mendelian Inheritance in ManPID Primary ImmunodeficiencyPDB Protein DataBankPDF Portable Document FormatPfam Protein Families DatabaseProDom Protein Domain DatabasePROSITE Database of Protein Families and DomainsRDF Resource Description FrameworkSGML Standard Generalized Markup LanguageSMART Simple Modular Architecture Research ToolSNP Single nucleotide polymorphismSOAP Simple Object Access ProtocolSOURCE Genomic resource in the InternetSwiss-Prot Protein knowledgebaseUDDI Universal Description, Discovery, and IntegrationURL Unified Resource LocatorW3C World Wide Web ConsortiumWAP Wireless Application ProtocolWML Wireless Markup LanguageWSDL Web Services Definition/Description LanguageXML Extensible Markup LanguageXPS XPathScriptXSL Extensible Style LanguageXSLT XSL TransformationsXSP eXtensible Server PagesThe author(s) declare that they have no competing interests.The pre-publication history for this paper can be accessed here:An XML schema for IDMLClick here for fileA DTD file for IDMLClick here for fileAn example file using IDML. A fact file for X-linked agammaglobulinemia.Click here for file


Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required.We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use.cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling. The complete sequencing of the genomes of numerous organisms provides a genetic "parts list" for human and many model organisms ,2. Howevin silico models of complex biological processes according to specific hypotheses about how the process works. Such hypotheses can then be verified or falsified by experiment and the models further refined [Having interaction and pathway data available in computable form enables the construction of  refined . In the  refined . Computa refined , better  refined , facilit refined , and acc refined .Central to any of these endeavors is the creation and maintenance of interaction and pathway databases. Because of the need to store and organize growing amounts of pathway information and the lack of a common point of organization, the number of interaction databases is growing rapidly, and over 220 exist in the year 2006 . UnfortuTo make heterogeneous data sources easier to use, several standard XML exchange formats for sharing interaction and pathway data have been developed. The Systems Biology Markup Language (SBML)  and CellAs more pathway databases make their data available in standard formats, it is easier to aggregate pathway information from multiple sources. A convenient single point of access for pathway information would provide researchers with a more complete and powerful view of biological networks and cellular machinery. Database and software systems that work towards this goal include pathway databases with original content, such as those listed in Table cPath is built using a traditional three-tier web architecture. The first tier consists of the open source MySQL database , and fulThe core cPath data model consists of just three elements: biological entities, links between biological entities, and links to external databases. For example, when storing a PSI-MI interaction, such as "p53 binds to TP73", cPath splits the PSI-MI record into three biological entity records: one for the interaction, one for p53 and one for TP73. For each record, cPath stores the complete PSI-MI XML content associated with that entity. cPath then stores two internal links, one from the interaction record to p53, and a second from the interaction record to TP73. Finally, cPath stores external link records defined in the PSI-MI record for each of the biological entities; for example, cPath will store a link from p53 to its UniProt identifier, as well as links from p53 to any matching Gene Ontology (GO) terms if those references are present in the PSI-MI file. External links are supplemented by any unification links available in the identifier mapping system.By maintaining a simple data model, and storing XML within a relational database structure, cPath is able to store data in multiple XML formats, including PSI-MI and BioPAX and the cPath table structure need not be updated when these formats change. cPath does not define any new XML formats, but rather adopts the data models of PSI-MI and BioPAX. Thus cPath can store any biological information represented by these standard exchange formats, from protein sequence, to experimental description, to thermodynamics information for biochemical reactions. Not all information will be displayed via the web interface, but all will be available in the XML returned by the web service API. Additional tables within cPath are used to support the three core entity tables. For example, cPath maintains an identity and reference table for mapping between identifiers, an external database table for creating hyperlinks to other biological databases, an organism table for storing basic organism data, an XML cache table for storing pre-computed XML assembly documents to speed loading of commonly accessed pages, and a log table for storing fatal errors in production mode.Using cPath, researchers can import interaction and pathway data from multiple sources, access such data via a standard web interface, and export data to third-party applications via a standards-based web service Figure . BiologiA recurring problem in bioinformatics is linking related or identical data described by different databases when multiple database identifiers (primary keys) are used to refer to the same biological entity ,41. InteImportantly, cPath also provides a similar service for storing relationships between non-equivalent, but related biological entities. For example, a researcher can import a UniProt to Affymetrix mapping file, then when a new protein with a matching UniProt identifier is subsequently imported into cPath, it is annotated with all known Affymetrix probe set identifiers. This is useful for tools that link gene expression data to pathways.To support data aggregation from multiple databases, such as to create custom integrated sets of pathways for local use, cPath supports the PSI-MI  and BioPcPath supports PSI-MI format Level 1 and BioPAX format Levels 1 and 2. Level 1 of PSI-MI represents protein-protein interactions. Level 1 of BioPAX represents metabolic pathways, Level 2 adds support for molecular interactions and post-translational protein modifications, such as those supported by PSI-MI, and future levels will add support for signaling pathways, gene regulatory networks and genetic interactions by the end of 2006.Once pathway data is stored in cPath, it is available for browsing via a standard web browser. For example, the Cancer Cell Map  currentlData stored in cPath can be made available for query and export via a standards-based web service interface. For example, a third-party application can retrieve a list of all pathways stored in cPath, and then retrieve the full details of each pathway in subsequent calls back to cPath. The result of each query is a BioPAX or PSI-MI formatted XML data file that can be parsed and used by the application. By exposing all data via a standards-based web service interface, cPath enables interoperable communication with other software modules, and enables third-party applications to more easily build and expand tools for visualization, analysis and model simulation. For example, the cPath plugin for Cytoscape  enables In addition to our own software engineering requirements, cPath meets specific interoperability and testing requirements of the National Cancer Institute (NCI) Cancer Biomedical Informatics Grid, or caBIG™. The goal of caBIG™ is to create a common infrastructure of interoperable tools and data specifically focused on cancer research ,51, and cpath@googlegroups.com).cPath is freely available under the GNU Lesser General Public License (LGPL) for academic and commercial use. cPath can be used to distribute pathway data on the Internet, or can be used to share private data locally within an individual lab, department or company. Stable releases of the cPath software are available for download, as are nightly snapshots of the latest code, which is not guaranteed to be stable, but may have new features compared to the last stable release. A complete administrator guide (available in PDF format) describes the step-by-step process for installing a new instance of cPath. Installation requires some computer system administration skill and ability to work with the command line, thus is geared towards computational biologists and software developers. Once installed, cPath can be administered via the command line using a Perl script wrapper around a Java administration program or via a web-based administration interface. After installation, typical set-up includes loading selecting identifier mapping and PSI-MI or BioPAX files and running the text indexer via the command line administration script or the web-based interface. Once these are done, cPath is fully functional and is ready for use. Any problems with import, such as errors in the input files, are detected by a validator and reported. The web-based administration provides several options for customizing the look and feel of cPath, including the ability to set global headers, footers, and home page content. Additionally, we maintain a public mailing list where new users can post questions related to the administration and use of cPath  level by simply recognizing identifiers referencing the same protein, but it is incapable of detecting duplicate interaction or pathway records, resolving conflicting information and detecting semantic inconsistencies between different sources, all of which are required for true integration. Furthermore, while cPath is capable of importing both PSI-MI and BioPAX data, the web interface and web service must be set to support one of these exchange formats at a time. To support both exchange formats simultaneously, we will focus on creating translators between PSI-MI and BioPAX, and expand the roster of available web services queries to support both formats. cPath will also support PSI-MI Level 2.5, which expands the format scope to include other interactors, such as small molecules, DNA, and RNA and will support BioPAX Level 3.0 and future levels as they become available. We also plan to integrate BioPAX and PSI-MI validators, currently under development by others, into the cPath import pipeline to verify that all incoming BioPAX and PSI-MI records conform to their respective format definitions and to community best practices.With the availability of tools for converting SBML and CellML to BioPAX developed by BioModels.net , we will• Project Name: cPath• Project Home Page: • Operating System(s): Platform independent; tested on Windows, Linux and Mac OS X• Programming Languages: Java• Other Requirements: MySQL 4.0 or higher; Apache Tomcat Server 4.1 or higher; Apache Ant 1.6 or higher, Perl 5.0 or higher. All required software is open source and freely available.• License: Free for academic and commercial users under the GNU Lesser General Public License (LGPL)..• Example site running cPath software: Memorial Sloan-Kettering Cancer Cell Map, EC managed the overall software development process for cPath, wrote most of the code for cPath, and wrote the first draft of this manuscript. GB conceived of the cPath project, managed the scientific and technical goals, and helped draft the manuscript. BG contributed to the web interface for displaying BioPAX records, built the administrative interface for customizing cPath web pages, and built the cPath plugin for overlaying expression data onto biological pathways. CS conceived of the cPath project, and provided overall scientific and technical guidance. All authors read and approved the final manuscript.


Cellular processes depend on the function of intracellular molecular networks. The curation of the literature relevant to specific biological pathways is important for many theoretical and experimental research teams and communities. No current tool supports web publication or hosting of user-developed large scale annotated pathway diagrams. Sharing via web publication is needed to allow real-time access to the current literature pathway knowledgebase, both privately within a research team or publicly among the outside research community. Web publication also facilitates team and/or community input into the curation process while allowing centralized control of the curation and validation process. We have developed new tool to address these needs. Biological Pathway Publisher (BioPP) is a software suite for converting CellDesigner Systems Biology Markup Language (CD-SBML) formatted pathways into a web viewable format. The BioPP suite is available for private use and for depositing knowledgebases into a newly created public repository.BioPP suite is a web-based application that allows pathway knowledgebases stored in CD-SBML to be web published with an easily navigated user interface. The BioPP suite consists of four interrelated elements: a pathway publisher, an upload web-interface, a pathway repository for user-deposited knowledgebases and a pathway navigator. Users have the option to convert their CD-SBML files to HTML for restricted use or to allow their knowledgebase to be web-accessible to the scientific community. All entities in all knowledgebases in the repository are linked to public database entries as well as to a newly created public wiki which provides a discussion forum.BioPP tools and the public repository facilitate sharing of pathway knowledgebases and interactive curation for research teams and scientific communities. BioPP suite is accessible at  Understanding cellular function requires detailed cell-type specific insight into the structure and operation of the molecular networks formed by a cell's genes and proteins . The comBiological network curation involves the construction, verification and refinement of an annotated pathway map. Maps may be generic or cell-type specific. Accurate assembly of an annotated pathway map is a difficult, slow, iterative and labor-intensive project Fig . CuratorMost pathway maps are created using standalone software tool and stored in modifications of the Systems Biology Markup Language (SBML) -9. The cThere are several software tools that provide good drawing platforms to build, annotate and visualize biological pathways. Tools that provide automated layout of SBML files have been developed as part of the Systems Biology Workbench ,14. CytoIn order to facilitate the incorporation of new information and the correction of errors in a detailed pathway map, web-based dissemination of the map and knowledgebase is required. Current web-based pathway maps, such as PANTHER  and ReacThe BioPP suite consists of four interrelated elements: a pathway publisher, an upload web-interface, a pathway repository and a pathway navigator, each of which is described separately below. The pathway publisher uses information provided through the pathway upload web-interface to web-publish the pathway and/or to populate the pathway repository. The networks in the pathway repository are publicly viewable via the pathway navigator. The BioPP suite is community accessible (see Availability and requirements).publisher application is launched on a dedicated server that hosts the BioPP suite through the pathway upload web-interface. The pathway publisher receives the user-uploaded information about the pathway, and the associated CD-SBML file and the png image exported from CellDesigner 4.0. The publisher parses the uploaded files to create a flat html file library describing the underlying pathway content and mapping the entity coordinates in the pathway. The hyperlinks on the entities point to the corresponding annotation information. The library contains various types of information such as sorted lists of different types of entities (Protein/Gene/RNA), sorted list of interactions, annotation for each interaction and for each entity, and the co-ordinates of entities.This Perl implemented The user can also choose to have the network published, using an automated procedure, into the pathway repository. After publication, the user will be returned (via browser) the converted files, in standard compressed zip format. Should the user prefer not to publish the converted pathway in the public pathway repository, the converted files are returned back to the user, and all user-uploaded information and converted files are deleted from the server. Thus, if desired, users can utilize the publisher to convert pathways for their own use without compromising confidential information.The upload web-interface is based on CGI and is implemented in Perl. Following a simple registration process, a user receives an e-mail with a username and corresponding password, which will enable the user to upload the biological network related information, such as pathway name, CD-SBML file, .png image file, and other identifying information onto the server via the web-interface Fig. . The uplrepository  into a HTML library used for web-publication of the pathways. As CellDesigner permits import and conversion of the standard SBML format into CD-SBML, BioPP can be used to convert any pathway stored in the standard SBML format. Therefore, BioPP permits a very easy, flexible and quick access to large amounts of biological information. The pathways are presented in several HTML frames Fig. , each wiame Fig. .The network frame contains a click-enabled image of the biological network. The entities and interactions with supporting annotation, pathway graphics legend and other pertinent information assist exploration of the network. The network is displayed through a JAVA applet which enables panning of the network via the index window, containing a smaller image of the map. Dragging a zoom rectangle inside the index window will result in displaying the underlying map location in the main frame.All entities in the main frame are hyperlinked to the corresponding annotations, if available. All annotations are presented in the annotation frame. These annotations display a list of interactions in which the chosen entity is involved and the annotation corresponding that entity, if any. Each interaction in this list is hyperlinked to the corresponding interaction annotation, if available. In the introduction and legend frames, we present a brief user-specified description of the network and the CellDesigner legend  of the rExperience suggests that the proteins, genes, and interactions involved in an existing pathway are valuable information for the curators and accelerates construction of a pathway. The interactions, proteins, genes, and RNA frames are populated with the sorted list of interactions, proteins, genes and RNAs, respectively. All the interactions that contain relevant annotation are hyperlinked, which when clicked will display the corresponding annotations in the annotation frame. Note that several interactions may be annotated with the same information, e.g. citations. In order to avoid redundancy, the tool ensures that all the annotation HTML files are unique. As a result, should need arise, several interactions are hyperlinked to the same annotation information. In addition, a list of interactions that is annotated by the currently displayed information is presented in the annotation frame. Similarly, all the proteins, genes, and RNAs in the respective frames are hyperlinked.An entity specific link to NCBI's Gene Entrez page which opens a new window is provided for all the proteins, genes, and RNAs in all the locations wherever cited. In addition, a link to a master, public entity-based wiki page is provided as a forum for community input and discussion.A completely automated database of biological pathways (with hyperlinks to pertinent annotations) is publicly available (see Availability and requirements). The current interface supports (a) conversion of the original pathway into a web-publishable form and (b) web publication of the pathway in our database. In both cases, the software requires the user to upload the source CD-SBML file and the associated image. Unless the user specifically directs BioPP to publish the pathway to the repository, the server retains no information from the user about the pathway or the HTML pages that are delivered.The BioPP suite provides easy to use tools for web-publication and viewing of pathways as well establishes a new public annotated pathway repository. These tools and repository should be useful to pathway curators, to research teams constructing specialized and cell-type specific pathways and to the general research community.Project Name: Biological Pathway PublisherProject home page: Operating systems: platform independent)Other requirements: Browser supporting Java Virtual Machine (JVM). For viewing large pathways, additional memory allocation to JVM may be required (for instructions on memory allocation see Restrictions: noneGAV and GN designed and implemented the software and drafted the manuscript. GAV and SCS conceived of the project. SP tested pathways and helped specify the suite. SCS supervised the project and revised the manuscript. All authors read and approved the final manuscript.


We, for example, have used them for quantitation of proteins on "reverse-phase" protein lysate arrays. For those studies, we quality-controlled > 600 available monoclonal antibodies and also needed to develop precise information on the genes that encode their antigens. Translation among the various protein and gene identifier types proved non-trivial because of one-to-many and many-to-one relationships. To organize the antibody, protein, and gene information, we initially developed a relational database in Filemaker for our own use. When it became apparent that the information would be useful to many other researchers faced with the need to choose or characterize antibodies, we developed it further as AbMiner, a fully relational web-based database under MySQL, programmed in Java.Monoclonal antibodies are used extensively throughout the biomedical sciences for detection of antigens, either iAbMiner is a user-friendly, web-based relational database of information on > 600 commercially available antibodies that we validated by Western blot for protein microarray studies. It includes many types of information on the antibody, the immunogen, the vendor, the antigen, and the antigen's gene. Multiple gene and protein identifier types provide links to corresponding entries in a variety of other public databases, including resources for phosphorylation-specific antibodies. AbMiner also includes our quality-control data against a pool of 60 diverse cancer cell types (the NCI-60) and also protein expression levels for the NCI-60 cells measured using our high-density "reverse-phase" protein lysate microarrays for a selection of the listed antibodies. Some other available database resources give information on antibody specificity for one or a couple of cell types. In contrast, the data in AbMiner indicate specificity with respect to the antigens in a pool of 60 diverse cell types from nine different tissues of origin.AbMiner is a relational database that provides extensive information from our own laboratory and other sources on more than 600 available antibodies and the genes that encode the antibodies' antigens. The data will be made freely available at  Antibodies are used as tools throughout biomedical science, and they are, increasingly, being incorporated into clinical practice in such specialties as rheumatology, oncology, and infectious diseases . They arThat was the case when our laboratory began the project that motivated us to develop AbMiner: 'reverse-phase' protein lysate microarray profiling of the 60 human cancer cell lines (the NCI-60) used since 1990 by the U.S. National Cancer Institute's Developmental Therapeutics Program to screen > 100,000 chemical compounds  for anticancer activity  introducTo record and monitor the validation process, we programmed a relational database that included the results as well as correlative meta-data on each antibody reagent. It became apparent, however, that the database  would be valuable to a much broader community of antibody users. We therefore decided to develop it further as a public resource. For the 635 antibodies included to date, the user can browse the information or search by antibody name or by any of 18 other features , with no particular selection criteria except that antibodies directed against very small ~ 10 kDa) or very large (> 350 kDa) proteins were excluded. Species recognized included human, mouse, rat, dog, chicken, frog, and others. Each vial of antibody was assigned a unique AbMiner identification code number so that screening of the particular vial could be tracked. Pertinent information indicated in the schema in Table 0 kDa or To validate antigen-specificity by Western blot against a wide range of human antigens, we harvested cell lysates from each of the NCI-60 cell lines as described previously ,24 and gsingle band: one predominant band, at the expected molecular weight; (b) multiple bands: extra bands remaining with a 5-second or longer film exposure; (c) wrong molecular weight: predominant band or bands at unexpected molecular weight(s); and (d) no band. Antibodies were Western-blotted up to three or four times if required to obtain clear results.The Western blot results were classified as follows: (a) We focused on Western blot analysis and designed the screening process as we did because of the specific requirements for application of antibodies on reverse-phase lysate arrays. Antibodies that recognize an epitope from more than one protein (or isoform) can be used for detection and quantitation of proteins on a Western blot as long as any extraneous bands have different effective molecular weights and would show up as separate bands. The lysate array, in contrast, is effectively a multiplexed dot blot; the signal from each spot on the array is the summation of specific and non-specific binding of the antibody. Therefore, for the lysate arrays we used only screened antibodies that produced a single predominant band by Western at the expected molecular weight. However, antibodies that produced multiple bands may still be useful for other applications, so information on them is retained in AbMiner. Other types of quality control data  may be most pertinent to other types of applications. AbMiner is extensible in that data fields can be added to accommodate and present such information.th of the pool's protein, we cannot rule out the possibility that a relatively faint extraneous band on the western would reflect a grossly incorrect target protein value for one or two cell types on the array. To check that possibility would require doing 60 times as many Westerns . In principle, that would be desirable, but the uncertainties of the present quality control procedure are still considerably less than those inherent in many other transcriptomic and proteomic profiling technologies. As noted previously, the user will presumably reassess candidate antibodies on his or her particular experimental system in any case.As indicated in Fig. AbMiner's Gene Information database provides translation among different data platforms and makes it possible for the user to search by proteomic, transcriptomic, or genomic identifiers. To find the intersection between data sets from different platforms – such as cDNA ,26 and ovice versa. That feature of AbMiner  a data entry module constructed using FileMaker Pro5.0™  and used by our team for data entry as well as for detailed tracking of the antibody validation process.(ii) a web application for sharing the various types of information on antibodies, antigens, and genes with the research community. The web application, written principally in Java, leverages a variety of available resources: MySQL as the database engine; Hibernate to map the objects into the database; JSP, Struts, and Tiles to render the user interface; and JUnit and HTTPUnit for testing individual programming units and the overall system. In addition to providing the web user interface, we have defined a simple HTTP specification that facilitates linkage of other applications into AbMiner. The web application was constructed under the "Agile Development" paradigm, which encourages close, iterative interaction between user/tester/motivators of the package (biologists) and software engineers . That inBoth components of AbMiner have the same underlying model, which includes three main modules: Antibody, Screening, and Gene. AbMiner uses a relational database approach to manage the complex relationships among those elements. The relationships are generally not one-to-one. For example, a given gene often codes for different splice variants, which may or may not be recognized by the same antibody. Conversely, multiple antibodies from different vendors or hybridoma clones may target a protein encoded by a single gene. An additional complication is that, because of the continuing re-annotation of the human genome, some identifiers are not unique or constant. That dynamic process is exemplified by retired or relocated UniGene clusters that can sometimes result in more than one UniGene Cluster ID or LocusID entry for the same gene. By constructing AbMiner as a relational database, we have been able to organize and update those one-to-many, many-to-one, and many-to-many relationships.Currently, AbMiner is populated with screening data generated by our own laboratory, but we plan to incorporate data from other studies and repositories when available. We also plan to put data entry pages on the web component for input of screening information from other investigators in the research community who wish to contribute (with appropriate attribution)The identifiers described in the last section perform an additional function by serving as link-outs to their respective entries in LocusLink, UniGene, GeneCards, Entrez's RefSeq, and our MedMiner program. AbMiner also provides links to the Mammalian Phosphorylation Resource (MPR) , a web sAs already noted, development of AbMiner was motivated by our need to organize information on antibodies for lysate array studies, and it has proved itself an almost indispensable tool in that respect. Particularly important is the information on the correspondence between antibody names, antigen names, and the variety of gene identifier types. We were able, for example, to address the question of similarities and differences between mRNA and protein expression profiles across the NCI-60 . IdentifWe have also applied the resources of AbMiner to the identification of molecular biomarkers at the protein level. For one such study, we developed a multi-step "integromic" protocol ,30, whicThe protein-mRNA expression studies are continuing, with promise. More than 500 of the 635 antibodies currently in AbMiner have UniGene Cluster IDs that match those from four different NCI-60 microarray platforms (see Table AbMiner is certainly not the most comprehensive in terms of numbers of antigens or antibodies covered. In that regard, the classic source is Linscott's Directory . The AdvOverall, to the best of our knowledge, none of the other sources have the range of information types on the antibodies, the antigens, the vendors, and the antigen's genes that AbMiner does, and none of them give the type of multiple-tissue Western blot specificity data or protein microarray data that are compiled in AbMiner. Insofar as we have found, any Western blot results given in the other databases had been obtained against single cell types. The quality control criteria represented in AbMiner are stricter and more comprehensive in that we have validated the antibodies by Western blot against a pool representing a wide range of cancer cell lines from nine different organs of origin and from different cell lineages. Non-specificities showed up in that more rigorous testing when they didn't in testing against individual cell types.AbMiner has unique relational characteristics for dealing with the one-to-many, many-to-one, and many-to-many relationships among antibody reagents, their antigens, and the genes of those antigens. Through the use of MatchMiner , supplemWe developed AbMiner as we did to provide the type of information needed for "integromic" ,44 studiAbMiner Fig.  is a bro. Also available there for batch downloading are the quality control results and lysate array data for screened antibodies. They will be updated as new antibodies are tested [AbMiner is freely accessible to both public and private sector users at e tested . Also avS.M. Major spearheaded the antibody quality control studies and software development and wrote the manuscript. S. Nishizuka designed the project and led the team in the lysate array experiments. D. Morita, F. Washburn, D. Asin, and H. Kouros-Mehr provided extensive experimental results. R. Rowland coded a prototype of the package in File Maker Pro. M. Sunshine and D. Kane developed the MySQL web interface and refactored the program package as a Java implementation. U. Shankavaram analyzed and organized the protein array data. J.N. Weinstein led the overall team and played roles in experimental and computational aspects of the project as well as in the writing.


With the advent of high throughput genotyping technology and the information available via projects such as the human genome sequencing and the HapMap project, more and more data relevant to the study of genetics and disease risk will be produced. Systematic reviews and meta-analyses of human genome epidemiology studies rely on the ability to identify relevant studies and to obtain suitable data from these studies. A first port of call for most such reviews is a search of MEDLINE. We examined whether this could be usefully supplemented by identifying databases on the World Wide Web that contain genetic epidemiological information.We conducted a systematic search for online databases containing genetic epidemiological information on gene prevalence or gene-disease association. In those containing information on genetic association studies, we examined what additional information could be obtained to supplement a MEDLINE literature search.We identified 111 databases containing prevalence data, 67 databases specific to a single gene and only 13 that contained information on gene-disease associations. Most of the latter 13 databases were linked to MEDLINE, although five contained information that may not be available from other sources.There is no single resource of structured data from genetic association studies covering multiple diseases, and in relation to the number of studies being conducted there is very little information specific to gene-disease association studies currently available on the World Wide Web. Until comprehensive data repositories are created and utilized regularly, new data will remain largely inaccessible to many systematic review authors and meta-analysts. Following the human genome project  and withInterpretation of the findings of genetic association studies is problematic, not only due to the selective reporting of findings, but also due to limitations of design, conduct, sample size, suboptimal analysis, and inconsistent findings across studies ,5. Systeepidemiology data was limited. This was in part blamed on the relative youth of the field of genetic epidemiology at the time [One means of making genetic association information available is through online databases. A discussion paper published in 2000 recommended that, although resources for the provision of genomic information on the web were adequate, the availability of genetic the time .Here we present findings from a systematic search for genetic epidemiology data available on the World Wide Web. Our primary motivation was to seek resources that would facilitate thorough systematic reviews or meta-analyses of gene prevalence or genetic association. We were interested both in identification of relevant studies and in availability of data that might not be published in journal articles. For genetic association information we further sought to evaluate the role of online databases as a supplement to information contained in MEDLINE, from the point of view of either a literature-based meta-analysis or in the preliminary stages of a collaborative combined analysis.th October 2005. Links from all databases identified were followed to identify further databases. We excluded general purpose reference databases (such as MEDLINE and EMBASE), databases primarily presenting information on genomics or proteomics without information on epidemiological studies, databases providing a resource for families and health care practitioners, and reported databases whose websites were found to be non-functional.We sought databases containing epidemiological information on gene prevalence or genetic association. Prevalence databases were determined as those with information on population prevalence of genetic variants without information on the evidence that such variants are involved in disease susceptibility or progression. Association databases were determined as those containing epidemiological information relating specific genetic variants to specific health or disease outcomes. To identify these we investigated the databases listed in the 2005 issue of the Nucleic Acids Research Database issue  and usedWe produced a list of prevalence databases, and a list of databases addressing variants of a single gene. Databases including association information on more than one gene were the subject of detailed investigation. We extracted information from these on content, source of data, regularity of update, size of the database, accessibility, search functions, connections to other databases, administration and funding, using a pre-piloted pro forma. We developed a system of grading the database according to its potential utility within systematic reviews and meta-analyses, as a supplement to a standard search of MEDLINE. This 'Beyond-MEDLINE utility grade' runs from grade 1 for a database that includes only material available in MEDLINE  to grade 5 for a database making unpublished data available to the user.The grade definitions are as follows:Database entries are equivalent to/links to MEDLINE records;Database entries are based on MEDLINE records, but with additional qualitative information, or otherwise available data ;Database entries are based on MEDLINE records, but with additional quantitative information otherwise unavailable (e.g. updated results or unpublished association data);Database enables identification of association studies not mentioned in MEDLINE records (e.g. non-MEDLINE-indexed report of an association study);Database enables identification of association studies not mentioned in MEDLINE records AND includes association data from such studies .A total of 448 websites were investigated, excluding duplicates. Of these, 257 were excluded, 111 were classed as containing prevalence data, 67 were classed a specific to a single gene and the remaining 13 databases were classed as containing information from genetic association studies and contained information on more than one gene. These were examined in more detail. Lists of all databases, by category, are available on our website .The prevalence databases contained information on the frequency of genetic variation in multiple genes, often in more than one population. If a database only contained information relevant to a single gene, then this was placed in the gene-specific subcategory. The majority of databases in the gene-specific subcategory contained only prevalence data but some contained information about gene- disease associations, though these were often limited to the rather older field of single gene disorders. Databases containing information on only a single gene were excluded from the utility grade analysis.Thirteen databases contained information on genetic association studies in more than a single gene Table . The majdata (i.e. a utility grade of ∁3) clearly include only a small minority of the genetic association studies that exist  have announced a new database called dbGaP specifically to host genotype-phenotype studies . This daAs a result of our systematic search for online repositories of genetic epidemiology data, we found 13 databases containing information on genetic association on more than one gene. On grading each of these with respect to the amount and type of extra data contained compared with a search of MEDLINE, we found seven that contained completely novel data that was previously unavailable (i.e. utility grade ≥ 3). This suggests that systematic reviews and meta-analyses based on published reports could be usefully supplemented with searches of some of these resources. However, the yield of information on the world wide web was still disappointingly low, and neither published literature nor online databases appear adequate to find all relevant evidence for inclusion in a comprehensive meta-analysis. We encourage study investigators to make their published and unpublished data available in suitable online repositories. A single resource providing structured data from genetic association studies covering multiple diseases would be an invaluable resource.CDC Center for Disease ControlHuGENet Human Genome Epidemiology NetworkThe author(s) declare that they have no competing interests.AJF participated in the design of the study, carried it out, and drafted the manuscript. JPTH conceived of the study, participated in its design and coordination and helped draft the manuscript. Both authors approved the final manuscript.The pre-publication history for this paper can be accessed here:This file list all of the 13 extracted databases and gives a more detailed description of eachClick here for file