Integrating biological databases

A system for exploring and visualizing biological pathways from large scale datasets

The growing accumulation of biological pathway datasets is making it possible to study pathways at genomic scale by exploration and visualization, facilitating the shift of biomedical research to systems-level analyses. Presented in this paper is our ongoing work on developing a system for exploring and visualizing biological pathways for large scale pathway analyses. A pathway repository with a standard data format was developed for storing various types of pathways, mechanisms for navigating among different pathway datasets and to external data sources were also designed, and a rendering engine is being developed to allow user for visualization and exploration.

GenoList: an integrated environment for comparative analysis of microbial genomes

The multitude of bacterial genome sequences being determined has generated new requirements regarding the development of databases and graphical interfaces: these are needed to organize and retrieve biological information from the comparison of large sets of genomes. GenoList (http://genolist.pasteur.fr/GenoList) is an integrated environment dedicated to querying and analyzing genome data from bacterial species. GenoList inherits from the SubtiList database and web server, the reference data resource for the Bacillus subtilis genome. The data model was extended to hold information about relationships between genomes (e.g. protein families). The web user interface was designed to primarily take into account biologists’ needs and modes of operation. Along with standard query and browsing capabilities, comparative genomics facilities are available, including subtractive proteome analysis. One key feature is the integration of the many tools accessible in the environment. As an example, it is straightforward to identify the genes that are specific to a group of bacteria, export them as a tab-separated list, get their protein sequences and run a multiple alignment on a subset of these sequences.

The Firegoose: two-way integration of diverse data from different bioinformatics web resources with desktop applications

BACKGROUND

Information resources on the World Wide Web play an indispensable role in modern biology. But integrating data from multiple sources is often encumbered by the need to reformat data files, convert between naming systems, or perform ongoing maintenance of local copies of public databases. Opportunities for new ways of combining and re-using data are arising as a result of the increasing use of web protocols to transmit structured data.

RESULTS

The Firegoose, an extension to the Mozilla Firefox web browser, enables data transfer between web sites and desktop tools. As a component of the Gaggle integration framework, Firegoose can also exchange data with Cytoscape, the R statistical package, Multiexperiment Viewer (MeV), and several other popular desktop software tools. Firegoose adds the capability to easily use local data to query KEGG, EMBL STRING, DAVID, and other widely-used bioinformatics web sites. Query results from these web sites can be transferred to desktop tools for further analysis with a few clicks. Firegoose acquires data from the web by screen scraping, microformats, embedded XML, or web services. We define a microformat, which allows structured information compatible with the Gaggle to be embedded in HTML documents. We demonstrate the capabilities of this software by performing an analysis of the genes activated in the microbe Halobacterium salinarum NRC-1 in response to anaerobic environments. Starting with microarray data, we explore functions of differentially expressed genes by combining data from several public web resources and construct an integrated view of the cellular processes involved.

CONCLUSION

The Firegoose incorporates Mozilla Firefox into the Gaggle environment and enables interactive sharing of data between diverse web resources and desktop software tools without maintaining local copies. Additional web sites can be incorporated easily into the framework using the scripting platform of the Firefox browser. Performing data integration in the browser allows the excellent search and navigation capabilities of the browser to be used in combination with powerful desktop tools.

DoD2007: 1082 molecular biology databases

Molecular biology databases are an integral part of biological research. To date, many databases were established with varied options to access associated biological data. Depending on the data being annotated, some are architecturally similar while others are specialized. In order to provide a partial solution to data integration, we report Database of Databases (DoD2007), constructed using html and javascript. The database has a web-based user interface with simple global search, specific database search, keyword help as well as links to abstracts, full-text and database home pages. Majority of data were derived form Nucleic Acids Research database issue and other published resources. The current release includes 15 categories with updated descriptions and links to 1082 databases, of which, 209 are new entries. New databases included in this issue are represented with ‘+’ sign before the name and a ‘*’ symbol provided for those that remained silent.

A semantic web approach applied to integrative bioinformatics experimentation: a biological use case with genomics data

MOTIVATION

The numerous public data resources make integrative bioinformatics experimentation increasingly important in life sciences research. However, it is severely hampered by the way the data and information are made available. The semantic web approach enhances data exchange and integration by providing standardized formats such as RDF, RDF Schema (RDFS) and OWL, to achieve a formalized computational environment. Our semantic web-enabled data integration (SWEDI) approach aims to formalize biological domains by capturing the knowledge in semantic models using ontologies as controlled vocabularies. The strategy is to build a collection of relatively small but specific knowledge and data models, which together form a 'personal semantic framework'. This can be linked to external large, general knowledge and data models. In this way, the involved scientists are familiar with the concepts and associated relationships in their models and can create semantic queries using their own terms. We studied the applicability of our SWEDI approach in the context of a biological use case by integrating genomics data sets for histone modification and transcription factor binding sites.

RESULTS

We constructed four OWL knowledge models, two RDFS data models, transformed and mapped relevant data to the data models, linked the data models to knowledge models using linkage statements, and ran semantic queries. Our biological use case demonstrates the relevance of these kinds of integrative bioinformatics experiments. Our findings show high startup costs for the SWEDI approach, but straightforward extension with similar data.

A systematic strategy for large-scale analysis of genotype phenotype correlations: identification of candidate genes involved in African trypanosomiasis

It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. In this article, we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype-phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region. Subsequent re-sequencing in Daxx identified a deletion of an amino acid, identified in susceptible mouse strains, in the Daxx-p53 protein-binding region. This supports recent experimental evidence that apoptosis could be playing a role in the trypanosomiasis resistance phenotype. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://workflows.mygrid.org.uk/repository/myGrid/PaulFisher/.

A data integration approach for cell cycle analysis oriented to model simulation in systems biology

Background

The cell cycle is one of the biological processes most frequently investigated in systems biology studies and it involves the knowledge of a large number of genes and networks of protein interactions. A deep knowledge of the molecular aspect of this biological process can contribute to making cancer research more accurate and innovative. In this context the mathematical modelling of the cell cycle has a relevant role to quantify the behaviour of each component of the systems. The mathematical modelling of a biological process such as the cell cycle allows a systemic description that helps to highlight some features such as emergent properties which could be hidden when the analysis is performed only from a reductionism point of view. Moreover, in modelling complex systems, a complete annotation of all the components is equally important to understand the interaction mechanism inside the network: for this reason data integration of the model components has high relevance in systems biology studies.

Description

In this work, we present a resource, the Cell Cycle Database, intended to support systems biology analysis on the Cell Cycle process, based on two organisms, yeast and mammalian. The database integrates information about genes and proteins involved in the cell cycle process, stores complete models of the interaction networks and allows the mathematical simulation over time of the quantitative behaviour of each component. To accomplish this task, we developed, a web interface for browsing information related to cell cycle genes, proteins and mathematical models. In this framework, we have implemented a pipeline which allows users to deal with the mathematical part of the models, in order to solve, using different variables, the ordinary differential equation systems that describe the biological process.

Conclusion

This integrated system is freely available in order to support systems biology research on the cell cycle and it aims to become a useful resource for collecting all the information related to actual and future models of this network. The flexibility of the database allows the addition of mathematical data which are used for simulating the behavior of the cell cycle components in the different models. The resource deals with two relevant problems in systems biology: data integration and mathematical simulation of a crucial biological process related to cancer, such as the cell cycle. In this way the resource is useful both to retrieve information about cell cycle model components and to analyze their dynamical properties. The Cell Cycle Database can be used to find system-level properties, such as stable steady states and oscillations, by coupling structure and dynamical information about models.

Microbial genome data resources

Studies of the genomes of individual microbial organisms as well as aggregate genomes (metagenomes) of microbial communities are expected to lead to advances in various areas, such as healthcare, environmental cleanup, and alternative energy production. A variety of specialized data resources manage the results of different microbial genome data processing and interpretation stages, and represent different degrees of microbial genome characterization. Scientists studying microbial genomes and metagenomes often need one or several of these resources. Given their diversity, these resources cannot be used effectively without determining the scope and type of individual resources as well as the relationship between their data.

DroSpeGe: rapid access database for new Drosophila species genomes

The Drosophila species comparative genome database DroSpeGe () provides genome researchers with rapid, usable access to 12 new and old Drosophila genomes, since its inception in 2004. Scientists can use, with minimal computing expertise, the wealth of new genome information for developing new insights into insect evolution. New genome assemblies provided by several sequencing centers have been annotated with known model organism gene homologies and gene predictions to provided basic comparative data. TeraGrid supplies the shared cyberinfrastructure for the primary computations. This genome database includes homologies to Drosophila melanogaster and eight other eukaryote model genomes, and gene predictions from several groups. BLAST searches of the newest assemblies are integrated with genome maps. GBrowse maps provide detailed views of cross-species aligned genomes. BioMart provides for data mining of annotations and sequences. Common chromosome maps identify major synteny among species. Potential gain and loss of genes is suggested by Gene Ontology groupings for genes of the new species. Summaries of essential genome statistics include sizes, genes found and predicted, homology among genomes, phylogenetic trees of species and comparisons of several gene predictions for sensitivity and specificity in finding new and known genes.

SYSTOMONAS--an integrated database for systems biology analysis of Pseudomonas

To provide an integrated bioinformatics platform for a systems biology approach to the biology of pseudomonads in infection and biotechnology the database SYSTOMONAS (SYSTems biology of pseudOMONAS) was established. Besides our own experimental metabolome, proteome and transcriptome data, various additional predictions of cellular processes, such as gene-regulatory networks were stored. Reconstruction of metabolic networks in SYSTOMONAS was achieved via comparative genomics. Broad data integration is realized using SOAP interfaces for the well established databases BRENDA, KEGG and PRODORIC. Several tools for the analysis of stored data and for the visualization of the corresponding results are provided, enabling a quick understanding of metabolic pathways, genomic arrangements or promoter structures of interest. The focus of SYSTOMONAS is on pseudomonads and in particular Pseudomonas aeruginosa, an opportunistic human pathogen. With this database we would like to encourage the Pseudomonas community to elucidate cellular processes of interest using an integrated systems biology strategy. The database is accessible at .

EuSplice: a unified resource for the analysis of splice signals and alternative splicing in eukaryotic genes

MOTIVATION

Despite increased availability of genome annotation data, a comprehensive resource for in-depth analysis of splice signal distributions and alternative splicing (AS) patterns in eukaryote genomes is still lacking. To meet this need, we have developed EuSplice--a unique splice-centric database which provides reliable splice signal and AS information for 23 eukaryotes.

RESULTS

The EuSplice database contains 95,822 AS events and 2.1 million splice signals associated with over 270,000 protein-coding genes. The intuitive, user-friendly EuSplice web interface has powerful data mining and graphics capabilities for inter-genomic comparative analysis of splice signals, putative cryptic splice sites and AS events. Moreover, the seamless integration of splicing data to extensive gene-specific annotations, such as homolog annotations, functional information, mutations and sequence details makes EuSplice a powerful one-stop information resource for investigating the molecular mechanisms of complex splicing events, disease associations and the evolution of splicing in eukaryotes.

AVAILABILITY

http://66.170.16.154/EuSplice.

SUPPLEMENTARY INFORMATION

Supplementary tables and figures at Bioinfo online.

FlyMine: an integrated database for Drosophila and Anopheles genomics

FlyMine is a data warehouse that addresses one of the important challenges of modern biology: how to integrate and make use of the diversity and volume of current biological data. Its main focus is genomic and proteomics data for Drosophila and other insects. It provides web access to integrated data at a number of different levels, from simple browsing to construction of complex queries, which can be executed on either single items or lists.

DiscoverySpace: an interactive data analysis application

DiscoverySpace is a graphical application for bioinformatics data analysis. Users can seamlessly traverse references between biological databases and draw together annotations in an intuitive tabular interface. Datasets can be compared using a suite of novel tools to aid in the identification of significant patterns. DiscoverySpace is of broad utility and its particular strength is in the analysis of serial analysis of gene expression (SAGE) data. The application is freely available online.

BioDIFF: An Effective Fast Change Detection Algorithm for Biological Annotations

Warehousing heterogeneous, dynamic biological data is a key technique for biological data integration as it greatly improves performance. However, it requires complex maintenance procedures to update the warehouse in light of the changes to the sources. Consequently, a key issue to address is how to detect changes to the underlying biological data sources. In this paper, we present an algorithm called BioDiff for detecting exact changes to biological annotations. In our approach we transform heterogeneous biological data to XML format and then detect changes between two versions of XML representation of biological data. Our algorithm extends X-Diff, a published XML change detection algorithm. X-Diff, being designed for any type of XML data, does not exploit the semantics of biological data to reduce the data set of bipartite mapping. We have implemented BioDiff in Java. We have conducted an extensive performance study using data from EMBL, GenBank, SwissProt and PDB. Our experimental results show that BioDiff runs 1.5 to 6 times faster than X-Diff.

Accessing integrated Brassica genetic and genomic data using the BASC Server

The BASC system provides tools for integrated mining and browsing of genetic, genomic and phenotypic data. The BASC demonstration server provides access to raw and analysed information for Brassica species and comparative information with Arabidopsis. We can use the tools within the Brassica BASC server to identify candidate genes for traits, conduct genome comparisons with Arabidopsis, identify syntenic regions and view gene expression profiles. The integration of BASC modules allows researchers to gain a comprehensive view of diverse Brassica genetic, genomic and phenotypic information. This chapter demonstrates the application of this resource, through the identification of candidate genes for an observed seed oil quality trait in Brassica napus.

Information integration in molecular bioscience

Integrating information in the molecular biosciences involves more than the cross-referencing of sequences or structures. Experimental protocols, results of computational analyses, annotations and links to relevant literature form integral parts of this information, and impart meaning to sequence or structure. In this review, we examine some existing approaches to integrating information in the molecular biosciences. We consider not only technical issues concerning the integration of heterogeneous data sources and the corresponding semantic implications, but also the integration of analytical results. Within the broad range of strategies for integration of data and information, we distinguish between platforms and developments. We discuss two current platforms and six current developments, and identify what we believe to be their strengths and limitations. We identify key unsolved problems in integrating information in the molecular biosciences, and discuss possible strategies for addressing them including semantic integration using ontologies, XML as a data model, and graphical user interfaces as integrative environments.

cPath: open source software for collecting, storing, and querying biological pathways

Background

Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required.

Results

We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use.

Conclusion

cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling.

ApiDB: integrated resources for the apicomplexan bioinformatics resource center

ApiDB () represents a unified entry point for the NIH-funded Apicomplexan Bioinformatics Resource Center (BRC) that integrates numerous database resources and multiple data types. The phylum Apicomplexa comprises numerous veterinary and medically important parasitic protozoa including human pathogenic species of the genera Cryptosporidium, Plasmodium and Toxoplasma. ApiDB serves not only as a database in its own right, but as a single web-based point of entry that unifies access to three major existing individual organism databases (, and CryptoDB.org), and integrates these databases with data available from additional sources. Through the ApiDB site, users may pose queries and search all available apicomplexan data and tools, or they may visit individual component organism databases.

Resources for integrative systems biology: from data through databases to networks and dynamic system models

In systems biology, biologically relevant quantitative modelling of physiological processes requires the integration of experimental data from diverse sources. Recent developments in high-throughput methodologies enable the analysis of the transcriptome, proteome, interactome, metabolome and phenome on a previously unprecedented scale, thus contributing to the deluge of experimental data held in numerous public databases. In this review, we describe some of the databases and simulation tools that are relevant to systems biology and discuss a number of key issues affecting data integration and the challenges these pose to systems-level research.

Automated structure extraction and XML conversion of life science database flat files

In the light of the increasing number of biological databases, their integration is a fundamental prerequisite for answering complex biological questions. Database integration, therefore, is an important area of research in bioinformatics. Since most of the publicly available life science databases are still exclusively exchanged by means of proprietary flat files, database integration requires parsers for very different flat file formats. Unfortunately, the development and maintenance of database specific flat file parsers is a nontrivial and time-consuming task, which takes considerable effort in large-scale integration scenarios. This paper introduces heuristically based concepts for automatic structure extraction from life science database flat files. On the basis of these concepts the FlatEx prototype is developed for the automatic conversion of flat files into XML representations.

National and ethnic mutation databases: recording populations' genography

Genetic databases are gradually assuming an increasing importance in all areas of health care. The national and ethnic mutation databases (NEMDBs) are continuously updated mutation depositories, recording extensive information over the described genetic heterogeneity of an ethnic group or population. Together with the central and locus-specific databases, those resources not only enhance awareness of the various genetic disorders but also facilitate the provision of genetic services and provide useful insights into the genographic history of human populations. Fifteen independent NEMDBs devoted to the documentation of the extant genetic heterogeneity in various population groups within 57 different countries were assessed; 13 of the NEMDBs were fully functional. The contents of the 13 fully functional NEMDBs were thoroughly analyzed for the presence or absence of 39 criteria, pertaining database general information, operating platform, data source and submission, and querying capacity. This study provides a strong case for uniformity of data to make the NEMDBs content maximally useful. In this direction, a hypothetical content for the ideal NEMDB is derived, which is currently being incorporated in an upgraded version of the ETHNOS NEMDB development and curation software, as well as a community structure that would enhance the chances of mutation frequency capture and documentation in human populations. The ultimate goal is that interested parties and granting bodies will assist in achieving the vision of a comprehensive resource that collects and displays all population-specific genetic information discovered.

FASTSNP: an always up-to-date and extendable service for SNP function analysis and prioritization

Single nucleotide polymorphism (SNP) prioritization based on the phenotypic risk is essential for association studies. Assessment of the risk requires access to a variety of heterogeneous biological databases and analytical tools. FASTSNP (function analysis and selection tool for single nucleotide polymorphisms) is a web server that allows users to efficiently identify and prioritize high-risk SNPs according to their phenotypic risks and putative functional effects. A unique feature of FASTSNP is that the functional effect information used for SNP prioritization is always up-to-date, because FASTSNP extracts the information from 11 external web servers at query time using a team of web wrapper agents. Moreover, FASTSNP is extendable by simply deploying more Web wrapper agents. To validate the results of our prioritization, we analyzed 1569 SNPs from the SNP500Cancer database. The results show that SNPs with a high predicted risk exhibit low allele frequencies for the minor alleles, consistent with a well-known finding that a strong selective pressure exists for functional polymorphisms. We have been using FASTSNP for 2 years and FASTSNP enables us to discover a novel promoter polymorphism. FASTSNP is available at .

The ABC of demographic behaviour: how the interplays of alleles, brains, and contexts over the life course should shape research aimed at understanding population processes

This paper proposes core innovations in the strategy of research on demographic behaviour. One aim is a shift of attention away from events and towards a focus on dynamic processes and their interplay: away from a preoccupation with marriage and divorce, births, deaths, migrations, and household structure towards a broader perspective that takes account of partnership and intimacy, parenthood, potential and well-being, position in society and space, and personal ties. Another aim is a much closer engagement with genetics, neuroscience, psychology, and behavioural economics. A third aim is a strategy that pays more attention to pathways within the individual, to the processes entailed when the individual interacts with various contexts, and to progressions that involve the interplay of the pathways and processes through the life course. These shifts of emphasis, which have already begun to occur, require a systematic reassessment of priorities for research on demographic behaviour.

An information-driven approach to pharmacogenomics

Effective information management of the pharmacogenomics discipline presents many unique challenges. Genetic and genomic data generated via high-throughput methods need to be integrated with phenotypic data which are defined at multiscale levels, ranging from the molecular to the clinical level. Repositories storing these data are distributed and vary in terms of syntax and semantics which result in issues concerning data exchange and integration. The application of the emerging semantic web offers a promising solution to these interoperability issues.

Addressing the problems with life-science databases for traditional uses and systems biology

A prerequisite to systems biology is the integration of heterogeneous experimental data, which are stored in numerous life-science databases. However, a wide range of obstacles that relate to access, handling and integration impede the efficient use of the contents of these databases. Addressing these issues will not only be essential for progress in systems biology, it will also be crucial for sustaining the more traditional uses of life-science databases.

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.

Ontologies for data and knowledge sharing in biology: plant ROS signaling as a case study

Modern technologies have rapidly transformed biology into a data-intensive discipline. In addition to the enormous amounts of existing experimental data in the literature, every new study can produce a large amount of new data, resulting in novel ideas and more publications. In order to understand a biological process as completely as possible, scientists should be able to combine and analyze all such information. Not only molecular biology and bioinformatics, but all the other domains of biology including plant biology, require tools and technologies that enable experts to capture knowledge within distributed and heterogeneous sources of information. Ontologies have proven to be one of the most-useful means of constructing and formalizing expert knowledge. The key feature of an ontology is that it represents a computer-interpretable model of a particular subject area. This article outlines the importance of ontologies for systems biology, data integration and information analyses, as illustrated through the example of reactive oxygen species (ROS) signaling networks in plants.

INTEGRATOR: interactive graphical search of large protein interactomes over the Web

Background

The rapid growth of protein interactome data has elevated the necessity and importance of network analysis tools. However, unlike pure text data, network search spaces are of exponential complexity. This poses special challenges for storing, searching, and navigating this data efficiently. Moreover, development of effective web interfaces has been difficult.

Results

We present Integrator, a web-integrated graphical search tool for protein-protein interaction networks across 50+ genomes.

Conclusion

Integrator provides single and multiple protein searches of the Bioverse database containing experimentally-derived and predicted protein-protein interactions. The interface provides animated local network views, rapid subgraph manipulation, and cross-referencing of functional annotations. Integrator is available at .

Graph-based analysis and visualization of experimental results with ONDEX

MOTIVATION

Assembling the relevant information needed to interpret the output from high-throughput, genome scale, experiments such as gene expression microarrays is challenging. Analysis reveals genes that show statistically significant changes in expression levels, but more information is needed to determine their biological relevance. The challenge is to bring these genes together with biological information distributed across hundreds of databases or buried in the scientific literature (millions of articles). Software tools are needed to automate this task which at present is labor-intensive and requires considerable informatics and biological expertise.

RESULTS

This article describes ONDEX and how it can be applied to the task of interpreting gene expression results. ONDEX is a database system that combines the features of semantic database integration and text mining with methods for graph-based analysis. An overview of the ONDEX system is presented, concentrating on recently developed features for graph-based analysis and visualization. A case study is used to show how ONDEX can help to identify causal relationships between stress response genes and metabolic pathways from gene expression data. ONDEX also discovered functional annotations for most of the genes that emerged as significant in the microarray experiment, but were previously of unknown function.

Data integration and genomic medicine

Genomic medicine aims to revolutionize health care by applying our growing understanding of the molecular basis of disease. Research in this arena is data intensive, which means data sets are large and highly heterogeneous. To create knowledge from data, researchers must integrate these large and diverse data sets. This presents daunting informatic challenges such as representation of data that is suitable for computational inference (knowledge representation), and linking heterogeneous data sets (data integration). Fortunately, many of these challenges can be classified as data integration problems, and technologies exist in the area of data integration that may be applied to these challenges. In this paper, we discuss the opportunities of genomic medicine as well as identify the informatics challenges in this domain. We also review concepts and methodologies in the field of data integration. These data integration concepts and methodologies are then aligned with informatics challenges in genomic medicine and presented as potential solutions. We conclude this paper with challenges still not addressed in genomic medicine and gaps that remain in data integration research to facilitate genomic medicine.

Data standards for proteomics: mitochondrial two-dimensional polyacrylamide gel electrophoresis data as a model system

Proteomics has emerged as a major discipline that led to a re-examination of the need for consensus and a nationally sanctioned set of proteomics technology standards. Such standards for databases and data reporting may be applied to two-dimensional polyacrylamide gel electrophoresis (2D PAGE) technology as a pilot project for assessing global and national needs in proteomics, and the role of the National Institute of Standards and Technology (NIST) and other similar standards and measurement organizations. The experience of harmonizing the heterogeneous data included in the Protein Data Bank (PDB) provides a paradigm for technology in an area where significant heterogeneity in technical detail and data storage has evolved. Here we propose an approach toward standardizing mitochondrial 2D PAGE data in support of a globally relevant proteomics consensus.

Automating genomic data mining via a sequence-based matrix format and associative rule set

There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands.

SOP3: a web-based tool for selection of oligonucleotide primers for single nucleotide polymorphism analysis by Pyrosequencing

SOP3 is a web-based software tool for designing oligonucleotide primers for use in the analysis of single nucleotide polymorphisms (SNPs). Accessible via the Internet, the application is optimized for developing the PCR and sequencing primers that are necessary for Pyrosequencing. The application accepts as input gene name, SNP reference sequence number, or chromosomal nucleotide location. Output can be parsed by gene name, SNP reference number, heterozygosity value, location, chromosome, or function. The location of an individual polymorphism, such as an intron, exon, or 5' or 3' untranslated region is indicated, as are whether nucleotide changes in an exon are associated with a change in an amino acid sequence. SOP3 presents for each entry a set of forward and biotinylated reverse PCR primers as well as a sequencing primer for use during the analysis of SNPs by Pyrosequencing. Theoretical pyrograms for each allele are calculated and presented graphically. The method has been tested in the development of Pyrosequencing assays for determining SNPs and for deletion/insertion polymorphisms in the human genome. Of the SOP3-designed primer sets that were tested, a large majority of the primer sets have successfully produced PCR products and Pyrosequencing data.

BioMOBY successfully integrates distributed heterogeneous bioinformatics Web Services. The PlaNet exemplar case

The burden of non-interoperability between on-line genomic resources is increasingly the rate-limiting step in large-scale genomic analysis. BioMOBY is a biological Web Service interoperability initiative that began as a retreat of representatives from the model organism database community in September, 2001. Its long-term goal is to provide a simple, extensible platform through which the myriad of on-line biological databases and analytical tools can offer their information and analytical services in a fully automated and interoperable way. Of the two branches of the larger BioMOBY project, the Web Services branch (MOBY-S) has now been deployed over several dozen data sources worldwide, revealing some significant observations about the nature of the integrative biology problem; in particular, that Web Service interoperability in the domain of bioinformatics is, unexpectedly, largely a syntactic rather than a semantic problem. That is to say, interoperability between bioinformatics Web Services can be largely achieved simply by specifying the data structures being passed between the services (syntax) even without rich specification of what those data structures mean (semantics). Thus, one barrier of the integrative problem has been overcome with a surprisingly simple solution. Here, we present a non-technical overview of the critical components that give rise to the interoperable behaviors seen in MOBY-S and discuss an exemplar case, the PlaNet consortium, where MOBY-S has been deployed to integrate the on-line plant genome databases and analytical services provided by a European consortium of databases and data service providers.

Pathology data integration with eXtensible Markup Language

It is impossible to overstate the importance of XML (eXtensible Markup Language) as a data organization tool. With XML, pathologists can annotate all of their data (clinical and anatomic) in a format that can transform every pathology report into a database, without compromising narrative structure. The purpose of this manuscript is to provide an overview of XML for pathologists. Examples will demonstrate how pathologists can use XML to annotate individual data elements and to structure reports in a common format that can be merged with other XML files or queried using standard XML tools. This manuscript gives pathologists a glimpse into how XML allows pathology data to be linked to other types of biomedical data and reduces our dependence on centralized proprietary databases.

Statistical Viewer: a tool to upload and integrate linkage and association data as plots displayed within the Ensembl genome browser

Background

To facilitate efficient selection and the prioritization of candidate complex disease susceptibility genes for association analysis, increasingly comprehensive annotation tools are essential to integrate, visualize and analyze vast quantities of disparate data generated by genomic screens, public human genome sequence annotation and ancillary biological databases. We have developed a plug-in package for Ensembl called "Statistical Viewer" that facilitates the analysis of genomic features and annotation in the regions of interest defined by linkage analysis.

Results

Statistical Viewer is an add-on package to the open-source Ensembl Genome Browser and Annotation System that displays disease study-specific linkage and/or association data as 2 dimensional plots in new panels in the context of Ensembl's Contig View and Cyto View pages. An enhanced upload server facilitates the upload of statistical data, as well as additional feature annotation to be displayed in DAS tracts, in the form of Excel Files. The Statistical View panel, drawn directly under the ideogram, illustrates lod score values for markers from a study of interest that are plotted against their position in base pairs. A module called "Get Map" easily converts the genetic locations of markers to genomic coordinates. The graph is placed under the corresponding ideogram features a synchronized vertical sliding selection box that is seamlessly integrated into Ensembl's Contig- and Cyto- View pages to choose the region to be displayed in Ensembl's "Overview" and "Detailed View" panels. To resolve Association and Fine mapping data plots, a "Detailed Statistic View" plot corresponding to the "Detailed View" may be displayed underneath.

Conclusion

Features mapping to regions of linkage are accentuated when Statistic View is used in conjunction with the Distributed Annotation System (DAS) to display supplemental laboratory information such as differentially expressed disease genes in private data tracks. Statistic View is a novel and powerful visual feature that enhances Ensembl's utility as valuable resource for integrative genomic-based approaches to the identification of candidate disease susceptibility genes. At present there are no other tools that provide for the visualization of 2-dimensional plots of quantitative data scores against genomic coordinates in the context of a primary public genome annotation browser.

Pathway information for systems biology

Pathway information is vital for successful quantitative modeling of biological systems. The almost 170 online pathway databases vary widely in coverage and representation of biological processes, making their use extremely difficult. Future pathway information systems for querying, visualization and analysis must support standard exchange formats to successfully integrate data on a large scale. Such integrated systems will greatly facilitate the constructive cycle of computational model building and experimental verification that lies at the heart of systems biology.

A Taxonomic Search Engine: federating taxonomic databases using web services

Background

The taxonomic name of an organism is a key link between different databases that store information on that organism. However, in the absence of a single, comprehensive database of organism names, individual databases lack an easy means of checking the correctness of a name. Furthermore, the same organism may have more than one name, and the same name may apply to more than one organism.

Results

The Taxonomic Search Engine (TSE) is a web application written in PHP that queries multiple taxonomic databases (ITIS, Index Fungorum, IPNI, NCBI, and uBIO) and summarises the results in a consistent format. It supports "drill-down" queries to retrieve a specific record. The TSE can optionally suggest alternative spellings the user can try. It also acts as a Life Science Identifier (LSID) authority for the source taxonomic databases, providing globally unique identifiers (and associated metadata) for each name.

Conclusion

The Taxonomic Search Engine is available at and provides a simple demonstration of the potential of the federated approach to providing access to taxonomic names.

wFleaBase: the Daphnia genome database

BACKGROUND

wFleaBase is a database with the necessary infrastructure to curate, archive and share genetic, molecular and functional genomic data and protocols for an emerging model organism, the microcrustacean Daphnia. Commonly known as the water-flea, Daphnia's ecological merit is unequaled among metazoans, largely because of its sentinel role within freshwater ecosystems and over 200 years of biological investigations. By consequence, the Daphnia Genomics Consortium (DGC) has launched an interdisciplinary research program to create the resources needed to study genes that affect ecological and evolutionary success in natural environments.

DISCUSSION

These tools include the genome database wFleaBase, which currently contains functions to search and extract information from expressed sequenced tags, genome survey sequences and full genome sequencing projects. This new database is built primarily from core components of the Generic Model Organism Database project, and related bioinformatics tools.

SUMMARY

Over the coming year, preliminary genetic maps and the nearly complete genomic sequence of Daphnia pulex will be integrated into wFleaBase, including gene predictions and ortholog assignments based on sequence similarities with eukaryote genes of known function. wFleaBase aims to serve a large ecological and evolutionary research community. Our challenge is to rapidly expand its content and to ultimately integrate genetic and functional genomic information with population-level responses to environmental challenges. URL: http://wfleabase.org/.

Data integration: challenges for drug discovery

The effective integration of data and knowledge from many disparate sources will be crucial to future drug discovery. Data integration is a key element of conducting scientific investigations with modern platform technologies, managing increasingly complex discovery portfolios and processes, and fully realizing economies of scale in large enterprises. However, viewing data integration as simply an 'IT problem' underestimates the novel and serious scientific and management challenges it embodies - challenges that could require significant methodological and even cultural changes in our approach to data.

Integrating global proteomic and genomic expression profiles generated from islet alpha cells: opportunities and challenges to deriving reliable biological inferences

Systematic profiling of expressed gene products represents a promising research strategy for elucidating the molecular phenotypes of islet cells. To this end, we have combined complementary genomic and proteomic methods to better assess the molecular composition of murine pancreatic islet glucagon-producing alphaTC-1 cells as a model system, with the expectation of bypassing limitations inherent to either technology alone. Gene expression was measured with an Affymetrix MG_U74Av2 oligonucleotide array, while protein expression was examined by performing high-resolution gel-free shotgun MS/MS on a nuclear-enriched cell extract. Both analyses were carried out in triplicate to control for experimental variability. Using a stringent detection p value cutoff of 0.04, 48% of all potential mRNA transcripts were predicted to be expressed (probes classified as present in at least two of three replicates), while 1,651 proteins were identified with high-confidence using rigorous database searching. Although 762 of 888 cross-referenced cognate mRNA-protein pairs were jointly detected by both platforms, a sizeable number (126) of gene products was detected exclusively by MS alone. Conversely, marginal protein identifications often had convincing microarray support. Based on these findings, we present an operational framework for both interpreting and integrating dual genomic and proteomic datasets so as to obtain a more reliable perspective into islet alpha cell function.