Analysis and comparison of metabolic pathway databases

Enormous amounts of data result from genome sequencing projects and new experimental methods. Within this tremendous amount of genomic data 30-40 per cent of the genes being identified in an organism remain unknown in terms of their biological function. As a consequence of this lack of information the overall schema of all the biological functions occurring in a specific organism cannot be properly represented. To understand the functional properties of the genomic data more experimental data must be collected. A pathway database is an effort to handle the current knowledge of biochemical pathways and in addition can be used for interpretation of sequence data. Some of the existing pathway databases can be interpreted as detailed functional annotations of genomes because they are tightly integrated with genomic information. However, experimental data are often lacking in these databases. This paper summarises a list of pathway databases and some of their corresponding biological databases, and also focuses on information about the content and the structure of these databases, the organisation of the data and the reliability of stored information from a biological point of view. Moreover, information about the representation of the pathway data and tools to work with the data are given. Advantages and disadvantages of the analysed databases are pointed out, and an overview to biological scientists on how to use these pathway databases is given.     

KaBOB: ontology-based semantic integration of biomedical databases

Background

The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources.

Results

We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license.

Conclusions

KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0559-3) contains supplementary material, which is available to authorized users.     

Bio-Ontology and text: bridging the modeling gap

MOTIVATION: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text. RESULTS: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90% of the information could be represented (95% confidence interval: 86-93%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes). AVAILABILITY: http://zellig.cpmc.columbia.edu/PGschema     

A database system for the analysis of biochemical pathways

To provide support for the analysis of biochemical pathways a database system based on a model that represents the characteristics of the domain is needed. This domain has proven to be difficult to model by using conventional data modelling techniques. We are building an ontology for biochemical pathways, which acts as the basis for the generation of a database on the same domain, allowing the definition of complex queries and complex data representation. The ontology is used as a modelling and analysis tool which allows the expression of complex semantics based on a first-order logic representation language. The induction capabilities of the system can help the scientist in formulating and testing research hypotheses that are difficult to express with the standard relational database mechanisms. An ontology representing the shared formalisation of the knowledge in a scientific domain can also be used as data integration tool clarifying the mapping of concepts to the developers of different databases. In this paper we describe the general structure of our system, concentrating on the ontology-based database as the key component of the system.     

SWISS-PROT: connecting biomolecular knowledge via a protein database

With the explosive growth of biological data, the development of new means of data storage was needed. More and more often biological information is no longer published in the conventional way via a publication in a scientific journal, but only deposited into a database. In the last two decades these databases have become essential tools for researchers in biological sciences. Biological databases can be classified according to the type of information they contain. There are basically three types of sequence-related databases (nucleic acid sequences, protein sequences and protein tertiary structures) as well as various specialized data collections. It is important to provide the users of biomolecular databases with a degree of integration between these databases as by nature all of these databases are connected in a scientific sense and each one of them is an important piece to biological complexity. In this review we will highlight our effort in connecting biological information as demonstrated in the SWISS-PROT protein database.     

Unlocking the potential of survival data for model organisms through a new database and online analysis platform: SurvCurv

Lifespan measurements, also called survival records, are a key phenotype in research on aging. If external hazards are excluded, aging alone determines the mortality in a population of model organisms. Understanding the biology of aging is highly desirable because of the benefits for the wide range of aging‐related diseases. However, it is also extremely challenging because of the underlying complexity. Here, we describe SurvCurv, a new database and online resource focused on model organisms collating survival data for storage and analysis. All data in SurvCurv are manually curated and annotated. The database, available at www.ebi.ac.uk/thornton-srv/databases/SurvCurv/ , offers various functions including plotting, Cox proportional hazards analysis, mathematical mortality models and statistical tests. It facilitates reanalysis and allows users to analyse their own data and compare it with the largest repository of model‐organism data from published experiments, thus unlocking the potential of survival data and demographics in model organisms.     

Mapping sequenced E.coli genes by computer: software, strategies and examples.

Methods are presented for organizing and integrating DNA sequence data, restriction maps, and genetic maps for the same organism but from a variety of sources (databases, publications, personal communications). Proper software tools are essential for successful organization of such diverse data into an ordered, cohesive body of information, and a suite of novel software to support this endeavor is described. Though these tools automate much of the task, a variety of strategies is needed to cope with recalcitrant cases. We describe such strategies and illustrate their application with numerous examples. These strategies have allowed us to order, analyze, and display over one megabase of E. coli DNA sequence information. The integration task often exposes inconsistencies in the available data, perhaps caused by strain polymorphisms or human oversight, necessitating the application of sound biological judgment. The examples illustrate both the level of expertise required of the database curator and the knowledge gained as apparent inconsistencies are resolved. The software and mapping methods are applicable to the study of any genome for which a high resolution restriction map is available. They were developed to support a weakly coordinated sequencing effort involving many laboratories, but would also be useful for highly orchestrated sequencing projects.     

Data warehousing in molecular biology

In the business and healthcare sectors data warehousing has provided effective solutions for information usage and knowledge discovery from databases. However, data warehousing applications in the biological research and development (R&D) sector are lagging far behind. The fuzziness and complexity of biological data represent a major challenge in data warehousing for molecular biology. By combining experiences in other domains with our findings from building a model database, we have defined the requirements for data warehousing in molecular biology.     

The Princeton Protein Orthology Database (P-POD): a comparative genomics analysis tool for biologists

Many biological databases that provide comparative genomics information and tools are now available on the internet. While certainly quite useful, to our knowledge none of the existing databases combine results from multiple comparative genomics methods with manually curated information from the literature. Here we describe the Princeton Protein Orthology Database (P-POD, http://ortholog.princeton.edu), a user-friendly database system that allows users to find and visualize the phylogenetic relationships among predicted orthologs (based on the OrthoMCL method) to a query gene from any of eight eukaryotic organisms, and to see the orthologs in a wider evolutionary context (based on the Jaccard clustering method). In addition to the phylogenetic information, the database contains experimental results manually collected from the literature that can be compared to the computational analyses, as well as links to relevant human disease and gene information via the OMIM, model organism, and sequence databases. Our aim is for the P-POD resource to be extremely useful to typical experimental biologists wanting to learn more about the evolutionary context of their favorite genes. P-POD is based on the commonly used Generic Model Organism Database (GMOD) schema and can be downloaded in its entirety for installation on one's own system. Thus, bioinformaticians and software developers may also find P-POD useful because they can use the P-POD database infrastructure when developing their own comparative genomics resources and database tools.     

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

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

Re-thinking organisms: The impact of databases on model organism biology

Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the curators of these databases for what counts as reliable evidence, acceptable terminology, appropriate experimental set-ups and adequate materials (e.g., specimens). On the one hand, these choices are informed by the collaborative research ethos characterising most model organism communities. On the other hand, the deployment of these standards in databases reinforces this ethos and gives it concrete and precise instantiations by shaping the skills, practices, values and background knowledge required of the database users. We conclude that the increasing reliance on community databases as vehicles to circulate data is having a major impact on how researchers conduct and communicate their research, which affects how they understand the biology of model organisms and its relation to the biology of other species.     

MGD: the Mouse Genome Database

The Mouse Genome Database (MGD) (http://www.informatics.jax.org) one component of a community database resource for the laboratory mouse, a key model organism for interpreting the human genome and for understanding human biology. MGD strives to provide an extensively integrated information resource with experimental details annotated from both literature and on-line genomic data sources. MGD curates and presents the consensus representation of genotype (sequence) to phenotype information including highly detailed information about genes and gene products. Primary foci of integration are through representations of relationships between genes, sequences and phenotypes. MGD collaborates with other bioinformatics groups to curate a definitive set of information about the laboratory mouse. Recent developments include a general implementation of database structures for controlled vocabularies and the integration of a phenotype classification system.     

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.     

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.     

The semantic metadatabase (SEMEDA): ontology based integration of federated molecular biological data sources

A system for "intelligent" semantic integration and querying of federated databases is being implemented by using three main components: A component which enables SQL access to integrated databases by database federation (MARGBench), an ontology based semantic metadatabase (SEMEDA) and an ontology based query interface (SEMEDA-query). In this publication we explain and demonstrate the principles, architecture and the use of SEMEDA. Since SEMEDA is implemented as 3 tiered web application database providers can enter all relevant semantic and technical information about their databases by themselves via a web browser. SEMEDA' s collaborative ontology editing feature is not restricted to database integration, and might also be useful for ongoing ontology developments, such as the "Gene Ontology" [2]. SEMEDA can be found at http://www-bm.cs.uni-magdeburg.de/semeda/. We explain how this ontologically structured information can be used for semantic database integration. In addition, requirements to ontologies for molecular biological database integration are discussed and relevant existing ontologies are evaluated. We further discuss how ontologies and structured knowledge sources can be used in SEMEDA and whether they can be merged supplemented or updated to meet the requirements for semantic database integration.     

ISYS: a decentralized, component-based approach to the integration of heterogeneous bioinformatics resources

MOTIVATION: Heterogeneity of databases and software resources continues to hamper the integration of biological information. Top-down solutions are not feasible for the full-scale problem of integration across biological species and data types. Bottom-up solutions so far have not integrated, in a maximally flexible way, dynamic and interactive graphical-user-interface components with data repositories and analysis tools. RESULTS: We present a component-based approach that relies on a generalized platform for component integration. The platform enables independently-developed components to synchronize their behavior and exchange services, without direct knowledge of one another. An interface-based data model allows the exchange of information with minimal component interdependency. From these interactions an integrated system results, which we call ISYSf1.gif" BORDER="0">. By allowing services to be discovered dynamically based on selected objects, ISYS encourages a kind of exploratory navigation that we believe to be well-suited for applications in genomic research.     

Theoretical biology in the third millennium

During the 20th century our understanding of genetics and the processes of gene expression have undergone revolutionary change. Improved technology has identified the components of the living cell, and knowledge of the genetic code allows us to visualize the pathway from genotype to phenotype. We can now sequence entire genes, and improved cloning techniques enable us to transfer genes between organisms, giving a better understanding of their function. Due to the improved power of analytical tools databases of sequence information are growing at an exponential rate. Soon complete sequences of genomes and the three-dimensional structure of all proteins may be known. The question we face in the new millennium is how to apply this data in a meaningful way. Since the genes carry the specification of an organism, and because they also record evolutionary changes, we need to design a theoretical framework that can take account of the flow of information through biological systems.