Relational Database Schema Research Articles

Artemis and ACT: viewing, annotating and comparing sequences stored in a relational database

Motivation: Artemis and Artemis Comparison Tool (ACT) have become mainstream tools for viewing and annotating sequence data, particularly for microbial genomes. Since its first release, Artemis has been continuously developed and supported with additional functionality for editing and analysing sequences based on feedback from an active user community of laboratory biologists and professional annotators. Nevertheless, its utility has been somewhat restricted by its limitation to reading and writing from flat files. Therefore, a new version of Artemis has been developed, which reads from and writes to a relational database schema, and allows users to annotate more complex, often large and fragmented, genome sequences.Results: Artemis and ACT have now been extended to read and write directly to the Generic Model Organism Database (GMOD, http://www.gmod.org) Chado relational database schema. In addition, a Gene Builder tool has been developed to provide structured forms and tables to edit coordinates of gene models and edit functional annotation, based on standard ontologies, controlled vocabularies and free text.Availability: Artemis and ACT are freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at the Wellcome Trust Sanger Institute web sites: http://www.sanger.ac.uk/Software/Artemis/ http://www.sanger.ac.uk/Software/ACT/Contact: artemis@sanger.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Computer-Aided Model Generation for Nonlinear Structural Analysis Using a Structural Component Model Database

This paper presents an approach for efficiently building analytical models for nonlinear analysis. The objective has been achieved by establishing structural component model database by collecting various structural component models addressing various structural details. A common data structure and a relational database schema for storing structural component models were proposed in this study. The proposed structural component model database can serve as a decision supporting system for building nonlinear analytical models manually. In addition, the modeling information stored in this database can be presented in XML document format to be parsed and manipulated by computer system for generating nonlinear analytical model in file automatically. A school building database is used as a case study to show the feasibility of automatic modeling for nonlinear analysis using the proposed structural component database. A semiautomatic model generation system was developed to provide an efficient modeling process, which is in the manner of form filling and option selecting on web-based user interfaces, so that the model builder can focus on making engineering decisions. The modeling details are handled automatically by the proposed system based on user selection and setting.

HIP2: An online database of human plasma proteins from healthy individuals

BackgroundWith the introduction of increasingly powerful mass spectrometry (MS) techniques for clinical research, several recent large-scale MS proteomics studies have sought to characterize the entire human plasma proteome with a general objective for identifying thousands of proteins leaked from tissues in the circulating blood. Understanding the basic constituents, diversity, and variability of the human plasma proteome is essential to the development of sensitive molecular diagnosis and treatment monitoring solutions for future biomedical applications. Biomedical researchers today, however, do not have an integrated online resource in which they can search for plasma proteins collected from different mass spectrometry platforms, experimental protocols, and search software for healthy individuals. The lack of such a resource for comparisons has made it difficult to interpret proteomics profile changes in patients' plasma and to design protein biomarker discovery experiments.DescriptionTo aid future protein biomarker studies of disease and health from human plasma, we developed an online database, HIP2 (Healthy Human Individual's Integrated Plasma Proteome). The current version contains 12,787 protein entries linked to 86,831 peptide entries identified using different MS platforms.ConclusionThis web-based database will be useful to biomedical researchers involved in biomarker discovery research. This database has been developed to be the comprehensive collection of healthy human plasma proteins, and has protein data captured in a relational database schema built to contain mappings of supporting peptide evidence from several high-quality and high-throughput mass-spectrometry (MS) experimental data sets. Users can search for plasma protein/peptide annotations, peptide/protein alignments, and experimental/sample conditions with options for filter-based retrieval to achieve greater analytical power for discovery and validation.

A perspective for biomedical data integration: Design of databases for flow cytometry

BackgroundThe integration of biomedical information is essential for tackling medical problems. We describe a data model in the domain of flow cytometry (FC) allowing for massive management, analysis and integration with other laboratory and clinical information. The paper is concerned with the proper translation of the Flow Cytometry Standard (FCS) into a relational database schema, in a way that facilitates end users at either doing research on FC or studying specific cases of patients undergone FC analysisResultsThe proposed database schema provides integration of data originating from diverse acquisition settings, organized in a way that allows syntactically simple queries that provide results significantly faster than the conventional implementations of the FCS standard. The proposed schema can potentially achieve up to 8 orders of magnitude reduction in query complexity and up to 2 orders of magnitude reduction in response time for data originating from flow cytometers that record 256 colours. This is mainly achieved by managing to maintain an almost constant number of data-mining procedures regardless of the size and complexity of the stored information.ConclusionIt is evident that using single-file data storage standards for the design of databases without any structural transformations significantly limits the flexibility of databases. Analysis of the requirements of a specific domain for integration and massive data processing can provide the necessary schema modifications that will unlock the additional functionality of a relational database.

The SGN comparative map viewer

With the rapid accumulation of genetic data for a multitude of different species, the availability of intuitive comparative genomic tools becomes an important requirement for the research community. Here we describe a web-based comparative viewer for mapping data, including genetic, physical and cytological maps, that is part of the SGN website (http://sgn.cornell.edu/) but that can also be installed and adapted for other websites. In addition to viewing and comparing different maps stored in the SGN database, the viewer allows users to upload their own maps and compare them to other maps in the system. The viewer is implemented in object oriented Perl, with a simple extensible interface to write data adapters for other relational database schemas and flat file formats.

Extending a Natural Language Interface with Geospatial Queries

In this installation of architectural perspectives, we describe an extension of a menu-based natural language interface (MBNLI) to support geospatial queries. Our extension makes it easier for application analysts and even inexperienced users to phrase complex queries without knowing the relational database query language SQL, database schemas (table structures), spatial operators, or spatial indexes.

A Chado case study: an ontology-based modular schema for representing genome-associated biological information

A few years ago, FlyBase undertook to design a new database schema to store Drosophila data. It would fully integrate genomic sequence and annotation data with bibliographic, genetic, phenotypic and molecular data from the literature representing a distillation of the first 100 years of research on this major animal model system. In developing this new integrated schema, FlyBase also made a commitment to ensure that its design was generic, extensible and available as open source, so that it could be employed as the core schema of any model organism data repository, thereby avoiding redundant software development and potentially increasing interoperability. Our question was whether we could create a relational database schema that would be successfully reused. Chado is a relational database schema now being used to manage biological knowledge for a wide variety of organisms, from human to pathogens, especially the classes of information that directly or indirectly can be associated with genome sequences or the primary RNA and protein products encoded by a genome. Biological databases that conform to this schema can interoperate with one another, and with application software from the Generic Model Organism Database (GMOD) toolkit. Chado is distinctive because its design is driven by ontologies. The use of ontologies (or controlled vocabularies) is ubiquitous across the schema, as they are used as a means of typing entities. The Chado schema is partitioned into integrated subschemas (modules), each encapsulating a different biological domain, and each described using representations in appropriate ontologies. To illustrate this methodology, we describe here the Chado modules used for describing genomic sequences. GMOD is a collaboration of several model organism database groups, including FlyBase, to develop a set of open-source software for managing model organism data. The Chado schema is freely distributed under the terms of the Artistic License (http://www.opensource.org/licenses/artistic-license.php) from GMOD (www.gmod.org).

An Incremental Method for the Lexical Annotation of Domain Ontologies

In this article, we present MELIS (Meaning Elicitation and Lexical Integration System), a method and a software tool for enabling an incremental process of automatic annotation of local schemas (e.g. relational database schemas, directory trees) with lexical information. The distinguishing and original feature of MELIS is the incremental process: the higher the number of schemas which are processed, the more background/ domain knowledge is cumulated in the system (a portion of domain ontology is learned at every step), the better the performance of the systems on annotating new schemas. MELIS has been tested as a component of the MOMIS-Ontology Builder, a framework able to create a domain ontology representing a set of selected data sources, described with a standard W3C language wherein concepts and attributes are annotated according to the lexical reference database. We describe the MELIS component within the MOMIS-Ontology Builder framework and provide some experimental results of MELIS as a standalone tool and as a component integrated in MOMIS.

An approach to developing complex database schemas using form types

AbstractIn this paper we consider an approach to developing complex database schemas. Apart from the theoretical model of the approach, we also developed a CASE tool named Integrated Information Systems*Case, R.6.2 (IIS*Case) that supports the practical application of the approach. In this paper the basis of our approach to the design and integration of database schemas and ways of using IIS*Case is outlined. The main features of a new version of IIS*Case, developed in Java, are described. IIS*Case is based on the concept of ‘form type’ and supports the conceptual modelling of a database schema, generating subschemas and integrating them into a relational database schema in 3NF. IIS*Case provides an intelligent support for complex and highly formalized design and programming tasks. Having an advanced knowledge of information systems and database design is not a compulsory prerequisite for using IIS*Case. IIS*Case is based on a methodology of gradual integration of independently designed subschemas into a database schema. The process of independent subschema design may lead to collisions in expressing real‐world constraints. IIS*Case uses specialized algorithms for checking the consistency of constraints embedded in a database schema and its subschemas. This paper briefly outlines the application of the process of detecting collisions, and actions the designer may take to resolve them. Copyright © 2007 John Wiley & Sons, Ltd.

The MIEL system: Uniform interrogation of structured and weakly-structured imprecise data

We present an information system developed to help assessing the microbiological risk in food. That information system contains experimental results in microbiology, mainly extracted from scientific publications. The increasing amount of the experimental results available and the difficulty to integrate them into a classic relational database schema led us to design a system composed of two distinct subsystems queried through a common interface. The first subsystem is a classic relational database. The second subsystem is a database containing weakly-structured pieces of information expressed in terms of conceptual graphs. The data stored in both bases can be fuzzy ones in order to take into account the specificities of the biological information. The uniform query language used on both relational database and conceptual graph database allows the users to express preferences by using fuzzy sets in their queries. The MIEL system is now operational and used by the microbiologists involved in the Sym'Previus French project.

A generator of SQL schema specifications

IIS*Case is an integrated CASE tool that supports the automation and intelligent support of complex and highly formalized design and programming tasks in the development of an information system. IIS*Case, as a tool from the class of domain oriented design environments, generates relational database schemas in 3rd normal form with all relevant data constraints. SQL Generator is an IIS*Case tool that generates the implementation specification of a database schema according to ANSI SQL:2003 standard. The generator may also produce a database schema specification for Microsoft SQL Server or Oracle DBMSs. The paper describes SQL Generator's traits, considers aspects of its application, and shows its use in the implementation of a complex database constraint using procedural mechanisms of a particular relational DBMS. SQL Generator is implemented in Java and Oracle JDeveloper environment.

Formation of hypercube representation of relational database

In the paper, a technology of constructing hypercube data representation from an original relational database is considered. The construction is based on formal definitions of the intermediate and target data models. Using an example of a particular relational database scheme, a mechanism of automated application generation is discussed.

An Integrated Web Resource for Cotton

“The Cotton Diversity Database” (http://cotton.agtec.uga.edu) is a Web resource for cotton (Gossypium spp.) phenotypic and genomic data. A primary goal for this resource is to provide both a useful management tool for breeders and other applied scientists and a research tool for genetic and genomic scientists. The resource contains four interface suites that include displays for each of the available phenotypic or genomic data types. These display suites are accessible via the genotype portal, a search interface that allows users to begin with a cotton accession and obtain all available data. The phenotypic data displays include graphical views of overall cultivar performance with means and between group standard deviations indicated in an easy‐to‐interpret graphical manner for common trial measures such as lint yield, micronaire, etc. The genomic data displays include interactive graphical views of genetic map and diversity data types. Genetic map data is displayed in both traditional linear and two‐dimensional comparative dot plot formats. Genetic diversity data is displayed in an interactive tree‐based format showing degrees of similarity among genotypes. The data are stored in Oracle relational database (RDBMS) schemas containing tables and views for data storage, auto‐calculated statistics and display parameters. The searchable RDBMS provides flexibility for a wide range of query and search options as well as integration paths amongst the various data types.

Enriching the ER model based on discovered association rules

The entity–relationship (ER) model, a powerful means for business and data modeling, needs to be enriched with new semantics as the real world changes and its understanding improves. This paper attempts at enriching the ER model based on association rules (AR) discovered from large databases by introducing specializations and sub-types into the ER model. The proposed framework is extended to deal with more general, flexible and linguistic knowledge in fuzzy association rules. Moreover, transforming an AR-enriched-ER (AR-EER) schema to a relational database (RDB) schema is also investigated.

BioWarehouse: a bioinformatics database warehouse toolkit.

BackgroundThis article addresses the problem of interoperation of heterogeneous bioinformatics databases.ResultsWe introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research.ConclusionBioWarehouse embodies significant progress on the database integration problem for bioinformatics.

Using Chado to Store Genome Annotation Data

Chado is a relational database schema that can be used to manage a wide variety of biological information, including genome annotation, genetic, phenotypic, and expression data. Its flexibility comes from its use of "ontologies," which are controlled vocabularies that describe data types and the relationships among them. By changing its ontologies, Chado can be customized to suit many different needs. Another aspect that gives Chado its flexibility is its use of a modular design, which means that users can choose to use only those features of Chado that are suitable for their needs. XORT is the main software tool used to move data in and out of Chado databases. XORT uses an XML-based file format for data import and export; this format is called ChadoXML, The protocols described in this chapter show how to use XORT and related software to import genome annotation data into Chado databases, and how to export data stored in Chado databases into different file formats for report and data mining purposes.

MaxdLoad2 and maxdBrowse: standards-compliant tools for microarray experimental annotation, data management and dissemination

BackgroundmaxdLoad2 is a relational database schema and Java® application for microarray experimental annotation and storage. It is compliant with all standards for microarray meta-data capture; including the specification of what data should be recorded, extensive use of standard ontologies and support for data exchange formats. The output from maxdLoad2 is of a form acceptable for submission to the ArrayExpress microarray repository at the European Bioinformatics Institute. maxdBrowse is a PHP web-application that makes contents of maxdLoad2 databases accessible via web-browser, the command-line and web-service environments. It thus acts as both a dissemination and data-mining tool.ResultsmaxdLoad2 presents an easy-to-use interface to an underlying relational database and provides a full complement of facilities for browsing, searching and editing. There is a tree-based visualization of data connectivity and the ability to explore the links between any pair of data elements, irrespective of how many intermediate links lie between them. Its principle novel features are:• the flexibility of the meta-data that can be captured,• the tools provided for importing data from spreadsheets and other tabular representations,• the tools provided for the automatic creation of structured documents,• the ability to browse and access the data via web and web-services interfaces.Within maxdLoad2 it is very straightforward to customise the meta-data that is being captured or change the definitions of the meta-data. These meta-data definitions are stored within the database itself allowing client software to connect properly to a modified database without having to be specially configured. The meta-data definitions (configuration file) can also be centralized allowing changes made in response to revisions of standards or terminologies to be propagated to clients without user intervention.maxdBrowse is hosted on a web-server and presents multiple interfaces to the contents of maxd databases. maxdBrowse emulates many of the browse and search features available in the maxdLoad2 application via a web-browser. This allows users who are not familiar with maxdLoad2 to browse and export microarray data from the database for their own analysis. The same browse and search features are also available via command-line and SOAP server interfaces. This both enables scripting of data export for use embedded in data repositories and analysis environments, and allows access to the maxd databases via web-service architectures.ConclusionmaxdLoad2 and maxdBrowse are portable and compatible with all common operating systems and major database servers. They provide a powerful, flexible package for annotation of microarray experiments and a convenient dissemination environment. They are available for download and open sourced under the Artistic License.

A Simple, Flexible and Scalable Approach for Generating Tailored Questionnaires and Health Education Messages

Tailored health information is important for generating patient-specific recommendations in clinical decision support systems and for crafting health education materials that are specifically customized to a patient. Many previous attempts to generate tailored information require complex representations, lack general applicability, and are inflexible to content alterations. In this article, we describe a simple, yet flexible approach for tailoring health communication. This generalized and scalable approach relies on a flexible state representation of each individual and an expandable rule drafting and processing engine. It utilizes a relational database schema and a simple table structure to maintain each individual's past and current health information. Content for tailored communication is represented in a single table which stores predefined logic describing the rules for selecting content applicable to specific individuals. The flexibility, scalability, and simplicity of this approach are demonstrated by describing two diverse projects. One project has provided patient-tailored decision support for physicians for over 82,000 patient encounters and the other generates tailored health questions and messages for patients through a tool developed in less than 4 months.

Constraint Preserving Transformation from Relational Schema to XML Schema

XML has become the standard for publishing and exchanging data on the Web. However, most business data is managed and will remain to be managed by relational database management systems. As such, there is an increasing need to efficiently and accurately publish relational data as XML documents for Internet-based applications. One way to publish relational data is to provide virtual XML documents for relational data via an XML schema which is transformed from the underlying relational database schema such that users can access the relational database through the XML schema. In this paper, we discuss issues in transforming a relational database schema into the corresponding XML schema. We aim to preserve all integrity constraints defined in a relational database schema, to achieve high level of nesting and to avoid introducing data redundancy in the transformed XML schema. In the paper, we first propose a basic transformation algorithm which introduces no data redundancy, then we improve the algorithm by exploring further nesting of the transformed XML schema.

BatchFlow

In a typical IT division of a large enterprise, there are a significant number of batch jobs that process data in relational database systems and flat files. These jobs may range from those that are run once in a year to those that are run every hour or every few minutes. While there are many notations available to represent a relational database schema including the ER model [1], it appears that there is no good visual notation to represent the jobs themselves. To some extent, this problem can be addressed by using flowcharts, UML sequence diagrams, and operations summary matrix. However, these notations and representations do not leverage the fact that most of the operations in a batch job are usually a relational select/insert/update/delete operation on a relational table, or a read/write operation on a file. In this paper, we introduce a method and notation that we call BatchFlow. This notation can help organizations in visualizing and communicating the batch job designs at a high level.