Integrating biological databases

Improving data quality by source analysis

In many domains, data cleaning is hampered by our limited ability to specify a comprehensive set of integrity constraints to assist in identification of erroneous data. An alternative approach to improve data quality is to exploit different data sources that contain information about the same set of objects. Such overlapping sources highlight hot-spots of poor data quality through conflicting data values and immediately provide alternative values for conflict resolution. In order to derive a dataset of high quality, we can merge the overlapping sources based on a quality assessment of the conflicting values. The quality of the resulting dataset, however, is highly dependent on our ability to asses the quality of conflicting values effectively.

The main objective of this article is to introduce methods that aid the developer of an integrated system over overlapping, but contradicting sources in the task of improving the quality of data. Value conflicts between contradicting sources are often systematic, caused by some characteristic of the different sources. Our goal is to identify such systematic differences and outline data patterns that occur in conjunction with them. Evaluated by an expert user, the regularities discovered provide insights into possible conflict reasons and help to assess the quality of inconsistent values. The contributions of this article are two concepts of systematic conflicts: contradiction patterns and minimal update sequences. Contradiction patterns resemble a special form of association rules that summarize characteristic data properties for conflict occurrence. We adapt existing association rule mining algorithms for mining contradiction patterns. Contradiction patterns, however, view each class of conflicts in isolation, sometimes leading to largely overlapping patterns. Sequences of set-oriented update operations that transform one data source into the other are compact descriptions for all regular differences among the sources. We consider minimal update sequences as the most likely explanation for observed differences between overlapping data sources. Furthermore, the order of operations within the sequences point out potential dependencies between systematic differences. Finding minimal update sequences, however, is beyond reach in practice. We show that the problem already is NP-complete for a restricted set of operations. In the light of this intractability result, we present heuristics that lead to convincing results for all examples we considered.

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

Background

Biology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways.

Results

This paper presents a new approach, scenario driven data modelling (SDDM), that integrates multi-relational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements.

Conclusions

We provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting massive data streams into usable knowledge.

Critical assessment of human metabolic pathway databases: a stepping stone for future integration

Background

Multiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts.

Results

We compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison.

Conclusions

Our results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor.

Knowledge sharing and collaboration in translational research, and the DC-THERA Directory

Biomedical research relies increasingly on large collections of data sets and knowledge whose generation, representation and analysis often require large collaborative and interdisciplinary efforts. This dimension of ‘big data’ research calls for the development of computational tools to manage such a vast amount of data, as well as tools that can improve communication and access to information from collaborating researchers and from the wider community. Whenever research projects have a defined temporal scope, an additional issue of data management arises, namely how the knowledge generated within the project can be made available beyond its boundaries and life-time. DC-THERA is a European ‘Network of Excellence’ (NoE) that spawned a very large collaborative and interdisciplinary research community, focusing on the development of novel immunotherapies derived from fundamental research in dendritic cell immunobiology. In this article we introduce the DC-THERA Directory, which is an information system designed to support knowledge management for this research community and beyond. We present how the use of metadata and Semantic Web technologies can effectively help to organize the knowledge generated by modern collaborative research, how these technologies can enable effective data management solutions during and beyond the project lifecycle, and how resources such as the DC-THERA Directory fit into the larger context of e-science.

BKM-react, an integrated biochemical reaction database

Background

The systematic, complete and correct reconstruction of genome-scale metabolic networks or metabolic pathways is one of the most challenging tasks in systems biology research. An essential requirement is the access to the complete biochemical knowledge - especially on the biochemical reactions. This knowledge is extracted from the scientific literature and collected in biological databases. Since the available databases differ in the number of biochemical reactions and the annotation of the reactions, an integrated knowledge resource would be of great value.

Results

We developed a comprehensive non-redundant reaction database containing known enzyme-catalyzed and spontaneous reactions. Currently, it comprises 18,172 unique biochemical reactions. As source databases the biochemical databases BRENDA, KEGG, and MetaCyc were used. Reactions of these databases were matched and integrated by aligning substrates and products. For the latter a two-step comparison using their structures (via InChIs) and names was performed. Each biochemical reaction given as a reaction equation occurring in at least one of the databases was included.

Conclusions

An integrated non-redundant reaction database has been developed and is made available to users. The database can significantly facilitate and accelerate the construction of accurate biochemical models.

The PlaNet Consortium: a network of European plant databases connecting plant genome data in an integrated biological knowledge resource

The completion of the Arabidopsis genome and the large collections of other plant sequences generated in recent years have sparked extensive functional genomics efforts. However, the utilization of this data is inefficient, as data sources are distributed and heterogeneous and efforts at data integration are lagging behind. PlaNet aims to overcome the limitations of individual efforts as well as the limitations of heterogeneous, independent data collections. PlaNet is a distributed effort among European bioinformatics groups and plant molecular biologists to establish a comprehensive integrated database in a collaborative network. Objectives are the implementation of infrastructure and data sources to capture plant genomic information into a comprehensive, integrated platform. This will facilitate the systematic exploration of Arabidopsis and other plants. New methods for data exchange, database integration and access are being developed to create a highly integrated, federated data resource for research. The connection between the individual resources is realized with BioMOBY. BioMOBY provides an architecture for the discovery and distribution of biological data through web services. While knowledge is centralized, data is maintained at its primary source without a need for warehousing. To standardize nomenclature and data representation, ontologies and generic data models are defined in interaction with the relevant communities.Minimal data models should make it simple to allow broad integration, while inheritance allows detail and depth to be added to more complex data objects without losing integration. To allow expert annotation and keep databases curated, local and remote annotation interfaces are provided. Easy and direct access to all data is key to the project.

S3QL: a distributed domain specific language for controlled semantic integration of life sciences data

Background

The value and usefulness of data increases when it is explicitly interlinked with related data. This is the core principle of Linked Data. For life sciences researchers, harnessing the power of Linked Data to improve biological discovery is still challenged by a need to keep pace with rapidly evolving domains and requirements for collaboration and control as well as with the reference semantic web ontologies and standards. Knowledge organization systems (KOSs) can provide an abstraction for publishing biological discoveries as Linked Data without complicating transactions with contextual minutia such as provenance and access control.

We have previously described the Simple Sloppy Semantic Database (S3DB) as an efficient model for creating knowledge organization systems using Linked Data best practices with explicit distinction between domain and instantiation and support for a permission control mechanism that automatically migrates between the two. In this report we present a domain specific language, the S3DB query language (S3QL), to operate on its underlying core model and facilitate management of Linked Data.

Results

Reflecting the data driven nature of our approach, S3QL has been implemented as an application programming interface for S3DB systems hosting biomedical data, and its syntax was subsequently generalized beyond the S3DB core model. This achievement is illustrated with the assembly of an S3QL query to manage entities from the Simple Knowledge Organization System. The illustrative use cases include gastrointestinal clinical trials, genomic characterization of cancer by The Cancer Genome Atlas (TCGA) and molecular epidemiology of infectious diseases.

Conclusions

S3QL was found to provide a convenient mechanism to represent context for interoperation between public and private datasets hosted at biomedical research institutions and linked data formalisms.

E-nephrology

Diagnosis of renal diseases is often delayed owing to the scarcity of trained physicians, lack of facilities, and shortage of funds limits effective management, particularly when it comes to the red zone of renal replacement therapy. The Internetis expected to open up a myriad resource of knowledge and applications for academicians, researchers and clinicians alike in all health care professions across the globe. Also, the Internet has grown rapidly over the years and will inevitably expand even more. Evolving technologies offer modern applications for information management, communications with multimedia and virtual reality. Now, these innovative technologies have opened up newer possibilities for nephrologists. As Internet is serving as a backbone for these modern technologies, it is an utmost necessity to use and refine Internet applications for future nephrologists. Increasingly easy access to Internet has dramatically reduced barriers in sharing of information among basic and clinical nephrologists. Considering the growing scope for nephrologists in the use of Internet, it is necessary to understand Internet as a source of information and backbone of modern application. This review illustrates expanding roles of the Internet for the nephrologists and provides ready to use compilation of useful academic, research, clinical resources and is expected to introduce, stimulate and guide nephrologists into the realm of the world wide web. It also investigates how Internet is supporting in growth and development of the field of nephrology and present and future scopes of Internet as a tool for professionals involved in this area as well as information about biological sciences, and it also gives information about societies in various continents working in field of nephrology and the links useful for clinicians and research scientists.

Drug repositioning for orphan diseases

The need and opportunity to discover therapeutics for rare or orphan diseases are enormous. Due to limited prevalence and/or commercial potential, of the approximately 6000 orphan diseases (defined by the FDA Orphan Drug Act as <200 000 US prevalence), only a small fraction (5%) is of interest to the biopharmaceutical industry. The fact that drug development is complicated, time-consuming and expensive with extremely low success rates only adds to the low rate of therapeutics available for orphan diseases. An alternative and efficient strategy to boost the discovery of orphan disease therapeutics is to find connections between an existing drug product and orphan disease. Drug Repositioning or Drug Repurposing--finding a new indication for a drug--is one way to maximize the potential of a drug. The advantages of this approach are manifold, but rational drug repositioning for orphan diseases is not trivial and poses several formidable challenges--pharmacologically and computationally. Most of the repositioned drugs currently in the market are the result of serendipity. One reason the connection between drug candidates and their potential new applications are not identified in an earlier or more systematic fashion is that the underlying mechanism 'connecting' them is either very intricate and unknown or indirect or dispersed and buried in an ever-increasing sea of information, much of which is emerging only recently and therefore is not well organized. In this study, we will review some of these issues and the current methodologies adopted or proposed to overcome them and translate chemical and biological discoveries into safe and effective orphan disease therapeutics.

Linking NCBI to Wikipedia: a wiki-based approach

The NCBI Taxonomy underpins many bioinformatics and phyloinformatics databases, but by itself provides limited information on the taxa it contains. One readily available source of information on many taxa is Wikipedia. This paper describes iPhylo Linkout, a Semantic wiki that maps taxa in NCBI's taxonomy database onto corresponding pages in Wikipedia. Storing the mapping in a wiki makes it easy to edit, correct, or otherwise annotate the links between NCBI and Wikipedia. The mapping currently comprises some 53,000 taxa, and is available at http://iphylo.org/linkout. The links between NCBI and Wikipedia are also made available to NCBI users through the NCBI LinkOut service.

LabKey Server: an open source platform for scientific data integration, analysis and collaboration

BACKGROUND

Broad-based collaborations are becoming increasingly common among disease researchers. For example, the Global HIV Enterprise has united cross-disciplinary consortia to speed progress towards HIV vaccines through coordinated research across the boundaries of institutions, continents and specialties. New, end-to-end software tools for data and specimen management are necessary to achieve the ambitious goals of such alliances. These tools must enable researchers to organize and integrate heterogeneous data early in the discovery process, standardize processes, gain new insights into pooled data and collaborate securely.

RESULTS

To meet these needs, we enhanced the LabKey Server platform, formerly known as CPAS. This freely available, open source software is maintained by professional engineers who use commercially proven practices for software development and maintenance. Recent enhancements support: (i) Submitting specimens requests across collaborating organizations (ii) Graphically defining new experimental data types, metadata and wizards for data collection (iii) Transitioning experimental results from a multiplicity of spreadsheets to custom tables in a shared database (iv) Securely organizing, integrating, analyzing, visualizing and sharing diverse data types, from clinical records to specimens to complex assays (v) Interacting dynamically with external data sources (vi) Tracking study participants and cohorts over time (vii) Developing custom interfaces using client libraries (viii) Authoring custom visualizations in a built-in R scripting environment. Diverse research organizations have adopted and adapted LabKey Server, including consortia within the Global HIV Enterprise. Atlas is an installation of LabKey Server that has been tailored to serve these consortia. It is in production use and demonstrates the core capabilities of LabKey Server. Atlas now has over 2,800 active user accounts originating from approximately 36 countries and 350 organizations. It tracks roughly 27,000 assay runs, 860,000 specimen vials and 1,300,000 vial transfers.

CONCLUSIONS

Sharing data, analysis tools and infrastructure can speed the efforts of large research consortia by enhancing efficiency and enabling new insights. The Atlas installation of LabKey Server demonstrates the utility of the LabKey platform for collaborative research. Stable, supported builds of LabKey Server are freely available for download at http://www.labkey.org. Documentation and source code are available under the Apache License 2.0.

TargetMine, an integrated data warehouse for candidate gene prioritisation and target discovery

Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

BACKGROUND

To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.

RESULTS

To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data.

CONCLUSIONS

We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene--disease and gene--compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Network inference from time-dependent Omics data

We provide a commented overview of the available databases for the systematic collection of pathway information and biological models essential for the interpretation of Omics data. Then, we present both the state of the art and the future challenges of network inference, a research area dealing with the deduction of reaction mechanisms from experimental Omics data. This approach represents one of the most challenging instances for making use of the huge amount of information gathered in the Omics era.

Current trends and new challenges of databases and web applications for systems driven biological research

Dynamic and rapidly evolving nature of systems driven research imposes special requirements on the technology, approach, design and architecture of computational infrastructure including database and Web application. Several solutions have been proposed to meet the expectations and novel methods have been developed to address the persisting problems of data integration. It is important for researchers to understand different technologies and approaches. Having familiarized with the pros and cons of the existing technologies, researchers can exploit its capabilities to the maximum potential for integrating data. In this review we discuss the architecture, design and key technologies underlying some of the prominent databases and Web applications. We will mention their roles in integration of biological data and investigate some of the emerging design concepts and computational technologies that are likely to have a key role in the future of systems driven biomedical research.

MetNetAPI: A flexible method to access and manipulate biological network data from MetNet

Background

Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server.

Results

MetNetAPI is a versatile Application Programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates). Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only.

Conclusions

An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, Microsoft.NET and R programming environments and offers flexible query and broad data- retrieval methods. Data retrieval can be customized to client needs and the API offers a framework to construct and manipulate user-defined networks. The design principles can be used as a template to build programmable interfaces for other biological databases. The API software and tutorials are available at http://www.metnetonline.org/api.

Booly: a new data integration platform

Background

Data integration is an escalating problem in bioinformatics. We have developed a web tool and warehousing system, Booly, that features a simple yet flexible data model coupled with the ability to perform powerful comparative analysis, including the use of Boolean logic to merge datasets together, and an integrated aliasing system to decipher differing names of the same gene or protein. Furthermore, Booly features a collaborative sharing system and a public repository so that users can retrieve new datasets while contributors can easily disseminate new content.

Results

We illustrate the uses of Booly with several examples including: the versatile creation of homebrew datasets, the integration of heterogeneous data to identify genes useful for comparing avian and mammalian brain architecture, and generation of a list of Food and Drug Administration (FDA) approved drugs with possible alternative disease targets.

Conclusions

The Booly paradigm for data storage and analysis should facilitate integration between disparate biological and medical fields and result in novel discoveries that can then be validated experimentally. Booly can be accessed at http://booly.ucsd.edu.

Information technology solutions for integration of biomolecular and clinical data in the identification of new cancer biomarkers and targets for therapy

The quest for new cancer biomarkers and targets for therapy requires not only the aggregation and analysis of heterogeneous biomolecular data but also integration of clinical data. In this review we highlight information technology solutions for the integration of biomolecular and clinical data and focus on a solution at the departmental level, i.e., decentralized and medium-scale solution for groups of labs working on a specific topic. Both, hardware and software requirements are described as well as bioinformatics methods and tools for the data analysis. The highlighted IT solutions include storage architecture, high-performance computing, and application servers. Additionally, following computational approaches for data integration are reviewed: data aggregation, integrative data analysis including methodological aspects as well as examples, biomolecular pathways and network reconstruction, and mathematical modelling. Finally, a case study in cancer immunology including the used computational methods is shown, demonstrating how IT solutions for integrating biomolecular and clinical data can help to identify new cancer biomarkers for improving diagnosis and predicting clinical outcome.

Outcome of array CGH analysis for 255 subjects with intellectual disability and search for candidate genes using bioinformatics

Array CGH enables the detection of pathogenic copy number variants (CNVs) in 5-15% of individuals with intellectual disability (ID), making it a promising tool for uncovering ID candidate genes. However, most CNVs encompass multiple genes, making it difficult to identify key disease gene(s) underlying ID etiology. Using array CGH we identified 47 previously unreported unique CNVs in 45/255 probands. We prioritized ID candidate genes using five bioinformatic gene prioritization web tools. Gene priority lists were created by comparing integral genes from each CNV from our ID cohort with sets of training genes specific either to ID or randomly selected. Our findings suggest that different training sets alter gene prioritization only moderately; however, only the ID gene training set resulted in significant enrichment of genes with nervous system function (19%) in prioritized versus non-prioritized genes from the same de novo CNVs (7%, p < 0.05). This enrichment further increased to 31% when the five web tools were used in concert and included genes within mitogen-activated protein kinase (MAPK) and neuroactive ligand-receptor interaction pathways. Gene prioritization web tools enrich for genes with relevant function in ID and more readily facilitate the selection of ID candidate genes for functional studies, particularly for large CNVs.

Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network

Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is approximately 67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in approximately 590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format.

BioExtract server--an integrated workflow-enabling system to access and analyze heterogeneous, distributed biomolecular data

Many in silico investigations in bioinformatics require access to multiple, distributed data sources and analytic tools. The requisite data sources may include large public data repositories, community databases, and project databases for use in domain-specific research. Different data sources frequently utilize distinct query languages and return results in unique formats, and therefore researchers must either rely upon a small number of primary data sources or become familiar with multiple query languages and formats. Similarly, the associated analytic tools often require specific input formats and produce unique outputs which make it difficult to utilize the output from one tool as input to another. The BioExtract Server (http://bioextract.org) is a Web-based data integration application designed to consolidate, analyze, and serve data from heterogeneous biomolecular databases in the form of a mash-up. The basic operations of the BioExtract Server allow researchers, via their Web browsers, to specify data sources, flexibly query data sources, apply analytic tools, download result sets, and store query results for later reuse. As a researcher works with the system, their "steps" are saved in the background. At any time, these steps can be preserved long-term as a workflow simply by providing a workflow name and description.

FlyTED: the Drosophila Testis Gene Expression Database

FlyTED, the Drosophila Testis Gene Expression Database, is a biological research database for gene expression images from the testis of the fruit fly Drosophila melanogaster. It currently contains 2762 mRNA in situ hybridization images and ancillary metadata revealing the patterns of gene expression of 817 Drosophila genes in testes of wild type flies and of seven meiotic arrest mutant strains in which spermatogenesis is defective. This database has been built by adapting a widely used digital library repository software system, EPrints (http://eprints.org/software/), and provides both web-based search and browse interfaces, and programmatic access via an SQL dump, OAI-PMH and SPARQL. FlyTED is available at http://www.fly-ted.org/.

Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits

A number of statistical methods have been developed to test for associations between pathways (collections of genes related biologically) and complex genetic traits. Pathway analysis methods were originally developed for analyzing gene expression data, but recently methods have been developed to perform pathway analysis on genome-wide association study (GWAS) data. The purpose of this review is to give an overview of these methods, enabling the reader to gain an understanding of what pathway analysis involves, and to select the method most suited to their purposes. This review describes the various types of statistical methods for pathway analysis, detailing the strengths and weaknesses of each. Factors influencing the power of pathway analyses, such as gene coverage and choice of pathways to analyze, are discussed, as well as various unresolved statistical issues. Finally, a list of computer programs for performing pathway analysis on genome-wide association data is provided.

Proteomics: challenges, techniques and possibilities to overcome biological sample complexity

Proteomics is the large-scale study of the structure and function of proteins in complex biological sample. Such an approach has the potential value to understand the complex nature of the organism. Current proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. Advances in protein fractionation and labeling techniques have improved protein identification to include the least abundant proteins. In addition, proteomics has been complemented by the analysis of posttranslational modifications and techniques for the quantitative comparison of different proteomes. However, the major limitation of proteomic investigations remains the complexity of biological structures and physiological processes, rendering the path of exploration paved with various difficulties and pitfalls. The quantity of data that is acquired with new techniques places new challenges on data processing and analysis. This article provides a brief overview of currently available proteomic techniques and their applications, followed by detailed description of advantages and technical challenges. Some solutions to circumvent technical difficulties are proposed.

A journey to Semantic Web query federation in the life sciences

BACKGROUND

As interest in adopting the Semantic Web in the biomedical domain continues to grow, Semantic Web technology has been evolving and maturing. A variety of technological approaches including triplestore technologies, SPARQL endpoints, Linked Data, and Vocabulary of Interlinked Datasets have emerged in recent years. In addition to the data warehouse construction, these technological approaches can be used to support dynamic query federation. As a community effort, the BioRDF task force, within the Semantic Web for Health Care and Life Sciences Interest Group, is exploring how these emerging approaches can be utilized to execute distributed queries across different neuroscience data sources.

METHODS AND RESULTS

We have created two health care and life science knowledge bases. We have explored a variety of Semantic Web approaches to describe, map, and dynamically query multiple datasets. We have demonstrated several federation approaches that integrate diverse types of information about neurons and receptors that play an important role in basic, clinical, and translational neuroscience research. Particularly, we have created a prototype receptor explorer which uses OWL mappings to provide an integrated list of receptors and executes individual queries against different SPARQL endpoints. We have also employed the AIDA Toolkit, which is directed at groups of knowledge workers who cooperatively search, annotate, interpret, and enrich large collections of heterogeneous documents from diverse locations. We have explored a tool called "FeDeRate", which enables a global SPARQL query to be decomposed into subqueries against the remote databases offering either SPARQL or SQL query interfaces. Finally, we have explored how to use the vocabulary of interlinked Datasets (voiD) to create metadata for describing datasets exposed as Linked Data URIs or SPARQL endpoints.

CONCLUSION

We have demonstrated the use of a set of novel and state-of-the-art Semantic Web technologies in support of a neuroscience query federation scenario. We have identified both the strengths and weaknesses of these technologies. While Semantic Web offers a global data model including the use of Uniform Resource Identifiers (URI's), the proliferation of semantically-equivalent URI's hinders large scale data integration. Our work helps direct research and tool development, which will be of benefit to this community.

Ultra-Structure database design methodology for managing systems biology data and analyses

Background

Modern, high-throughput biological experiments generate copious, heterogeneous, interconnected data sets. Research is dynamic, with frequently changing protocols, techniques, instruments, and file formats. Because of these factors, systems designed to manage and integrate modern biological data sets often end up as large, unwieldy databases that become difficult to maintain or evolve. The novel rule-based approach of the Ultra-Structure design methodology presents a potential solution to this problem. By representing both data and processes as formal rules within a database, an Ultra-Structure system constitutes a flexible framework that enables users to explicitly store domain knowledge in both a machine- and human-readable form. End users themselves can change the system's capabilities without programmer intervention, simply by altering database contents; no computer code or schemas need be modified. This provides flexibility in adapting to change, and allows integration of disparate, heterogenous data sets within a small core set of database tables, facilitating joint analysis and visualization without becoming unwieldy. Here, we examine the application of Ultra-Structure to our ongoing research program for the integration of large proteomic and genomic data sets (proteogenomic mapping).

Results

We transitioned our proteogenomic mapping information system from a traditional entity-relationship design to one based on Ultra-Structure. Our system integrates tandem mass spectrum data, genomic annotation sets, and spectrum/peptide mappings, all within a small, general framework implemented within a standard relational database system. General software procedures driven by user-modifiable rules can perform tasks such as logical deduction and location-based computations. The system is not tied specifically to proteogenomic research, but is rather designed to accommodate virtually any kind of biological research.

Conclusion

We find Ultra-Structure offers substantial benefits for biological information systems, the largest being the integration of diverse information sources into a common framework. This facilitates systems biology research by integrating data from disparate high-throughput techniques. It also enables us to readily incorporate new data types, sources, and domain knowledge with no change to the database structure or associated computer code. Ultra-Structure may be a significant step towards solving the hard problem of data management and integration in the systems biology era.

An XML transfer schema for exchange of genomic and genetic mapping data: implementation as a web service in a Taverna workflow

Background

Genomic analysis, particularly for less well-characterized organisms, is greatly assisted by performing comparative analyses between different types of genome maps and across species boundaries. Various providers publish a plethora of on-line resources collating genome mapping data from a multitude of species. Datasources range in scale and scope from small bespoke resources for particular organisms, through larger web-resources containing data from multiple species, to large-scale bioinformatics resources providing access to data derived from genome projects for model and non-model organisms. The heterogeneity of information held in these resources reflects both the technologies used to generate the data and the target users of each resource. Currently there is no common information exchange standard or protocol to enable access and integration of these disparate resources. Consequently data integration and comparison must be performed in an ad hoc manner.

Results

We have developed a simple generic XML schema (GenomicMappingData.xsd – GMD) to allow export and exchange of mapping data in a common lightweight XML document format. This schema represents the various types of data objects commonly described across mapping datasources and provides a mechanism for recording relationships between data objects. The schema is sufficiently generic to allow representation of any map type (for example genetic linkage maps, radiation hybrid maps, sequence maps and physical maps). It also provides mechanisms for recording data provenance and for cross referencing external datasources (including for example ENSEMBL, PubMed and Genbank.). The schema is extensible via the inclusion of additional datatypes, which can be achieved by importing further schemas, e.g. a schema defining relationship types. We have built demonstration web services that export data from our ArkDB database according to the GMD schema, facilitating the integration of data retrieval into Taverna workflows.

Conclusion

The data exchange standard we present here provides a useful generic format for transfer and integration of genomic and genetic mapping data. The extensibility of our schema allows for inclusion of additional data and provides a mechanism for typing mapping objects via third party standards. Web services retrieving GMD-compliant mapping data demonstrate that use of this exchange standard provides a practical mechanism for achieving data integration, by facilitating syntactically and semantically-controlled access to the data.

Towards barcode markers in Fungi: an intron map of Ascomycota mitochondria

Background

A standardized and cost-effective molecular identification system is now an urgent need for Fungi owing to their wide involvement in human life quality. In particular the potential use of mitochondrial DNA species markers has been taken in account. Unfortunately, a serious difficulty in the PCR and bioinformatic surveys is due to the presence of mobile introns in almost all the fungal mitochondrial genes. The aim of this work is to verify the incidence of this phenomenon in Ascomycota, testing, at the same time, a new bioinformatic tool for extracting and managing sequence databases annotations, in order to identify the mitochondrial gene regions where introns are missing so as to propose them as species markers.

Methods

The general trend towards a large occurrence of introns in the mitochondrial genome of Fungi has been confirmed in Ascomycota by an extensive bioinformatic analysis, performed on all the entries concerning 11 mitochondrial protein coding genes and 2 mitochondrial rRNA (ribosomal RNA) specifying genes, belonging to this phylum, available in public nucleotide sequence databases. A new query approach has been developed to retrieve effectively introns information included in these entries.

Results

After comparing the new query-based approach with a blast-based procedure, with the aim of designing a faithful Ascomycota mitochondrial intron map, the first method appeared clearly the most accurate. Within this map, despite the large pervasiveness of introns, it is possible to distinguish specific regions comprised in several genes, including the full NADH dehydrogenase subunit 6 (ND6) gene, which could be considered as barcode candidates for Ascomycota due to their paucity of introns and to their length, above 400 bp, comparable to the lower end size of the length range of barcodes successfully used in animals.

Conclusion

The development of the new query system described here would answer the pressing requirement to improve drastically the bioinformatics support to the DNA Barcode Initiative. The large scale investigation of Ascomycota mitochondrial introns performed through this tool, allowing to exclude the introns-rich sequences from the barcode candidates exploration, could be the first step towards a mitochondrial barcoding strategy for these organisms, similar to the standard approach employed in metazoans.

Genotype-phenotype databases: challenges and solutions for the post-genomic era

The flow of research data concerning the genetic basis of health and disease is rapidly increasing in speed and complexity. In response, many projects are seeking to ensure that there are appropriate informatics tools, systems and databases available to manage and exploit this flood of information. Previous solutions, such as central databases, journal-based publication and manually intensive data curation, are now being enhanced with new systems for federated databases, database publication, and more automated management of data flows and quality control. Along with emerging technologies that enhance connectivity and data retrieval, these advances should help to create a powerful knowledge environment for genotype-phenotype information.

Iron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseases

BACKGROUND

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular 'reactive oxygen species' (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation.

REVIEW

We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation).The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible.This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, since in some circumstances (especially the presence of poorly liganded iron) molecules that are nominally antioxidants can actually act as pro-oxidants. The reduction of redox stress thus requires suitable levels of both antioxidants and effective iron chelators. Some polyphenolic antioxidants may serve both roles.Understanding the exact speciation and liganding of iron in all its states is thus crucial to separating its various pro- and anti-inflammatory activities. Redox stress, innate immunity and pro- (and some anti-)inflammatory cytokines are linked in particular via signalling pathways involving NF-kappaB and p38, with the oxidative roles of iron here seemingly involved upstream of the IkappaB kinase (IKK) reaction. In a number of cases it is possible to identify mechanisms by which ROSs and poorly liganded iron act synergistically and autocatalytically, leading to 'runaway' reactions that are hard to control unless one tackles multiple sites of action simultaneously. Some molecules such as statins and erythropoietin, not traditionally associated with anti-inflammatory activity, do indeed have 'pleiotropic' anti-inflammatory effects that may be of benefit here.

CONCLUSION

Overall we argue, by synthesising a widely dispersed literature, that the role of poorly liganded iron has been rather underappreciated in the past, and that in combination with peroxide and superoxide its activity underpins the behaviour of a great many physiological processes that degrade over time. Understanding these requires an integrative, systems-level approach that may lead to novel therapeutic targets.

A systematic strategy for the discovery of candidate genes responsible for phenotypic variation

It is increasingly common to combine genome-wide expression data with quantitative trait mapping data to aid in the search for sequence polymorphisms responsible for phenotypic variation. By joining these complex but different data types at the level of the biological pathway, we can take advantage of existing biological knowledge to systematically identify possible mechanisms of genotype-phenotype interaction. With the development of web services and workflows, this process can be made rapid and systematic. Our methodology was applied to a use case of resistance to African trypanosomiasis in mice. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://www.myexperiment.org/users/43/workflows .

An integrated Korean biodiversity and genetic information retrieval system

Background

On-line biodiversity information databases are growing quickly and being integrated into general bioinformatics systems due to the advances of fast gene sequencing technologies and the Internet. These can reduce the cost and effort of performing biodiversity surveys and genetic searches, which allows scientists to spend more time researching and less time collecting and maintaining data. This will cause an increased rate of knowledge build-up and improve conservations. The biodiversity databases in Korea have been scattered among several institutes and local natural history museums with incompatible data types. Therefore, a comprehensive database and a nation wide web portal for biodiversity information is necessary in order to integrate diverse information resources, including molecular and genomic databases.

Results

The Korean Natural History Research Information System (NARIS) was built and serviced as the central biodiversity information system to collect and integrate the biodiversity data of various institutes and natural history museums in Korea. This database aims to be an integrated resource that contains additional biological information, such as genome sequences and molecular level diversity. Currently, twelve institutes and museums in Korea are integrated by the DiGIR (Distributed Generic Information Retrieval) protocol, with Darwin Core2.0 format as its metadata standard for data exchange. Data quality control and statistical analysis functions have been implemented. In particular, integrating molecular and genetic information from the National Center for Biotechnology Information (NCBI) databases with NARIS was recently accomplished. NARIS can also be extended to accommodate other institutes abroad, and the whole system can be exported to establish local biodiversity management servers.

Conclusion

A Korean data portal, NARIS, has been developed to efficiently manage and utilize biodiversity data, which includes genetic resources. NARIS aims to be integral in maximizing bio-resource utilization for conservation, management, research, education, industrial applications, and integration with other bioinformation data resources. It can be found at .

Bringing Web 2.0 to bioinformatics

Enabling deft data integration from numerous, voluminous and heterogeneous data sources is a major bioinformatic challenge. Several approaches have been proposed to address this challenge, including data warehousing and federated databasing. Yet despite the rise of these approaches, integration of data from multiple sources remains problematic and toilsome. These two approaches follow a user-to-computer communication model for data exchange, and do not facilitate a broader concept of data sharing or collaboration among users. In this report, we discuss the potential of Web 2.0 technologies to transcend this model and enhance bioinformatics research. We propose a Web 2.0-based Scientific Social Community (SSC) model for the implementation of these technologies. By establishing a social, collective and collaborative platform for data creation, sharing and integration, we promote a web services-based pipeline featuring web services for computer-to-computer data exchange as users add value. This pipeline aims to simplify data integration and creation, to realize automatic analysis, and to facilitate reuse and sharing of data. SSC can foster collaboration and harness collective intelligence to create and discover new knowledge. In addition to its research potential, we also describe its potential role as an e-learning platform in education. We discuss lessons from information technology, predict the next generation of Web (Web 3.0), and describe its potential impact on the future of bioinformatics studies.

State of the nation in data integration for bioinformatics

Data integration is a perennial issue in bioinformatics, with many systems being developed and many technologies offered as a panacea for its resolution. The fact that it is still a problem indicates a persistence of underlying issues. Progress has been made, but we should ask "what lessons have been learnt?", and "what still needs to be done?" Semantic Web and Web 2.0 technologies are the latest to find traction within bioinformatics data integration. Now we can ask whether the Semantic Web, mashups, or their combination, have the potential to help. This paper is based on the opening invited talk by Carole Goble given at the Health Care and Life Sciences Data Integration for the Semantic Web Workshop collocated with WWW2007. The paper expands on that talk. We attempt to place some perspective on past efforts, highlight the reasons for success and failure, and indicate some pointers to the future.

The PhyLoTA Browser: processing GenBank for molecular phylogenetics research

As an archive of sequence data for over 165,000 species, GenBank is an indispensable resource for phylogenetic inference. Here we describe an informatics processing pipeline and online database, the PhyLoTA Browser (http://loco.biosci.arizona.edu/pb), which offers a view of GenBank tailored for molecular phylogenetics. The first release of the Browser is computed from 2.6 million sequences representing the taxonomically enriched subset of GenBank sequences for eukaryotes (excluding most genome survey sequences, ESTs, and other high-throughput data). In addition to summarizing sequence diversity and species diversity across nodes in the NCBI taxonomy, it reports 87,000 potentially phylogenetically informative clusters of homologous sequences, which can be viewed or downloaded, along with provisional alignments and coarse phylogenetic trees. At each node in the NCBI hierarchy, the user can display a "data availability matrix" of all available sequences for entries in a subtaxa-by-clusters matrix. This matrix provides a guidepost for subsequent assembly of multigene data sets or supertrees. The database allows for comparison of results from previous GenBank releases, highlighting recent additions of either sequences or taxa to GenBank and letting investigators track progress on data availability worldwide. Although the reported alignments and trees are extremely approximate, the database reports several statistics correlated with alignment quality to help users choose from alternative data sources.

Laying the foundation for a Genomic Rosetta Stone: creating information hubs through the use of consensus identifiers

Given the growing wealth of downstream information, the integration of molecular and non-molecular data on a given organism has become a major challenge. For micro-organisms, this information now includes a growing collection of sequenced genes and complete genomes, and for communities of organisms it includes metagenomes. Integration of the data is facilitated by the existence of authoritative, community-recognized, consensus identifiers that may form the heart of so-called information knuckles. The Genomic Standards Consortium (GSC) is building a mapping of identifiers across a group of federated databases with the aim to improve navigation across these resources and to enable the integration of their information in the near future. In particular, this is possible because of the existence of INSDC Genome Project Identifiers (GPIDs) and accession numbers, and the ability of the community to define new consensus identifiers such as the culture identifiers used in the StrainInfo.net bioportal. Here we outline (1) the general design of the Genomic Rosetta Stone project, (2) introduce example linkages between key databases (that cover information about genomes, 16S rRNA gene sequences, and microbial biological resource centers), and (3) make an open call for participation in this project providing a vision for its future use.

Biodiversity informatics: the challenge of linking data and the role of shared identifiers

A major challenge facing biodiversity informatics is integrating data stored in widely distributed databases. Initial efforts have relied on taxonomic names as the shared identifier linking records in different databases. However, taxonomic names have limitations as identifiers, being neither stable nor globally unique, and the pace of molecular taxonomic and phylogenetic research means that a lot of information in public sequence databases is not linked to formal taxonomic names. This review explores the use of other identifiers, such as specimen codes and GenBank accession numbers, to link otherwise disconnected facts in different databases. The structure of these links can also be exploited using the PageRank algorithm to rank the results of searches on biodiversity databases. The key to rich integration is a commitment to deploy and reuse globally unique, shared identifiers [such as Digital Object Identifiers (DOIs) and Life Science Identifiers (LSIDs)], and the implementation of services that link those identifiers.

GORouter: an RDF model for providing semantic query and inference services for Gene Ontology and its associations

Background

The most renowned biological ontology, Gene Ontology (GO) is widely used for annotations of genes and gene products of different organisms. However, there are shortcomings in the Resource Description Framework (RDF) data file provided by the GO consortium: 1) Lack of sufficient semantic relationships between pairs of terms coming from the three independent GO sub-ontologies, that limit the power to provide complex semantic queries and inference services based on it. 2) The term-centric view of GO annotation data and the fact that all information is stored in a single file. This makes attempts to retrieve GO annotations based on big volume datasets unmanageable. 3) No support of GOSlim.

Results

We propose a RDF model, GORouter, which encodes heterogeneous original data in a uniform RDF format, creates additional ontology mappings between GO terms, and introduces a set of inference rulebases. Furthermore, we use the Oracle Network Data Model (NDM) as the native RDF data repository and the table function RDF_MATCH to seamlessly combine the result of RDF queries with traditional relational data. As a result, the scale of GORouter is minimized; information not directly involved in semantic inference is put into relational tables.

Conclusion

Our work demonstrates how to use multiple semantic web tools and techniques to provide a mixture of semantic query and inference solutions of GO and its associations. GORouter is licensed under Apache License Version 2.0, and is accessible via the website: .

Semi-automatic web service composition for the life sciences using the BioMoby semantic web framework

Researchers in the life-sciences are currently limited to small-scale informatics experiments and analyses because of the lack of interoperability among life-sciences web services. This limitation can be addressed by annotating services and their interfaces with semantic information, so that interoperability problems can be reasoned about programmatically. The Moby semantic web framework is a popular and mature platform that is used for this purpose. However, the number of services that are available to select from when building a workflow is becoming unmanageable for users. As such, attempts have been made to assist with service selection and composition. These tasks fall under the general label of automated service composition. We present a prototype workflow assembly client that reduces the number of choices that users have to make by (1) restricting the overall set of services presented to them and (2) ranking services so that the the most desirable ones are presented first. We demonstrate via an evaluation of this prototype that a unification of relatively simple techniques can rank desirable services highly while maintaining interactive response times.