Bibliography of Journal: Bioinformatics

  1. Baker, P.G., Goble, C.A., Bechhofer, S., Paton, N.W., Stevens, R., and Brass, A.. "An ontology for bioinformatics applications." Bioinformatics. 15 (6). 1999. pp. 510-20.
    [ .pdf ] [ .ps ] [ PubMed ] [ WebSite ]

    MOTIVATION: An ontology of biological terminology provides a model of biological concepts that can be used to form a semantic framework for many data storage, retrieval and analysis tasks. Such a semantic framework could be used to underpin a range of important bioinformatics tasks, such as the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results. RESULTS: This paper provides an overview of an ontology [the Transparent Access to Multiple Biological Information Sources (TAMBIS) ontology or TaO] that describes a wide range of bioinformatics concepts. The present paper describes the mechanisms used for delivering the ontology and discusses the ontology's design and organization, which are crucial for maintaining the coherence of a large collection of concepts and their relationships. AVAILABILITY: The TAMBIS system, which uses a subset of the TaO described here, is accessible over the Web via http://img.cs.man.ac.uk/tambis (although in the first instance, we will use a password mechanism to limit the load on our server). The complete model is also available on the Web at the above URL.

    Keywords: Classification ; *Computational Biology ; Databases Factual ; Expert Systems ; Models Biological


  2. Karp, P.D., Paley, S.M., and Romero, P.. "The Pathway Tools software." Bioinformatics. vol. 18 Suppl 1. 2002. pp. S225-32.
    [ .pdf ] [ PubMed ] [ WebSite ]

    Motivation: Bioinformatics requires reusable software tools for creating model-organism databases (MODs). Results: The Pathway Tools is a reusable production-quality software environment for creating a type of MOD called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc (see http://ecocyc.org) integrates our evolving understanding of the genes, proteins, metabolic network, and genetic network of an organism. This paper provides an overview of the four main components of the Pathway Tools: The PathoLogic component supports creation of new PGDBs from the annotated genome of an organism. The Pathway/Genome Navigator provides query, visualization, and Web-publishing services for PGDBs. The Pathway/Genome Editors support interactive updating of PGDBs. The Pathway Tools ontology defines the schema of PGDBs. The Pathway Tools makes use of the Ocelot object database system for data management services for PGDBs. The Pathway Tools has been used to build PGDBs for 13 organisms within SRI and by external users. Availability: The software is freely available to academics and is available for a fee to commercial institutions. Contact ptools-support


  3. Kuffner, R., Zimmer, R., and Lengauer, T.. "Pathway analysis in metabolic databases via differential metabolic display (DMD)." Bioinformatics. 16 (9). 2000. pp. 825-36.
    [ .pdf ] [ PubMed ] [ WebSite ]

    MOTIVATION: A number of metabolic databases are available electronically, some with features for querying and visualizing metabolic pathways and regulatory networks. We present a unifying, systematic approach based on PETRI nets for storing, displaying, comparing, searching and simulating such nets from a number of different sources. RESULTS: Information from each data source is extracted and compiled into a PETRI net. Such PETRI nets then allow to investigate the (differential) content in metabolic databases, to map and integrate genomic information and functional annotations, to compare sequence and metabolic databases with respect to their functional annotations, and to define, generate and search paths and pathways in nets. We present an algorithm to systematically generate all pathways satisfying additional constraints in such PETRI nets. Finally, based on the set of valid pathways, so-called differential metabolic displays (DMDs) are introduced to exhibit specific differences between biological systems, i.e. different developmental states, disease states, or different organisms, on the level of paths and pathways. DMDs will be useful for target finding and function prediction, especially in the context of the interpretation of expression data.

    Keywords: *Algorithms ; Catalysis ; Computational Biology_*methods ; Computer Simulation ; *Data Display ; *Databases Factual ; Enzymes_genetics ; Enzymes_metabolism ; Glycolysis ; Metabolism_*physiology ; Mycoplasma_metabolism ; Yeasts_metabolism


  4. Peleg, M., Yeh, I., and Altman, R.B.. "Modelling biological processes using workflow and Petri Net models." Bioinformatics. 18 (6). 2002. pp. 825-37.
    [ .pdf ] [ PubMed ] [ WebSite ]

    MOTIVATION: Biological processes can be considered at many levels of detail, ranging from atomic mechanism to general processes such as cell division, cell adhesion or cell invasion. The experimental study of protein function and gene regulation typically provides information at many levels. The representation of hierarchical process knowledge in biology is therefore a major challenge for bioinformatics. To represent high-level processes in the context of their component functions, we have developed a graphical knowledge model for biological processes that supports methods for qualitative reasoning. RESULTS: We assessed eleven diverse models that were developed in the fields of software engineering, business, and biology, to evaluate their suitability for representing and simulating biological processes. Based on this assessment, we combined the best aspects of two models: Workflow/Petri Net and a biological concept model. The Workflow model can represent nesting and ordering of processes, the structural components that participate in the processes, and the roles that they play. It also maps to Petri Nets, which allow verification of formal properties and qualitative simulation. The biological concept model, TAMBIS, provides a framework for describing biological entities that can be mapped to the workflow model. We tested our model by representing malaria parasites invading host erythrocytes, and composed queries, in five general classes, to discover relationships among processes and structural components. We used reachability analysis to answer queries about the dynamic aspects of the model. AVAILABILITY: The model is available at http://smi.stanford.edu/projects/helix/pubs/process-model/.


  5. Stevens, R., Baker, P.G., Bechhofer, S., Ng, G., Jacoby, A., Paton, N.W., Goble, C.A., and Brass, A.. "TAMBIS: transparent access to multiple bioinformatics information sources." Bioinformatics. 16 (2). 2000. pp. 184-5.
    [ PubMed ] [ WebSite ]

    SUMMARY: TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) is an application that allows biologists to ask rich and complex questions over a range of bioinformatics resources. It is based on a model of the knowledge of the concepts and their relationships in molecular biology and bioinformatics. AVAILABILITY: TAMBIS is available as an applet from http://img.cs.man.ac.uk/tambis SUPPLEMENTARY: A full manual tutorial and videos can be found at http://img.cs.man.ac.uk/tambis. CONTACT: tambis

    Keywords: Computational Biology ; *Information Storage and Retrieval ; *Software


  6. Schuster, S., Pfeiffer, T., Moldenhauer, F., Koch, I., and Dandekar, T.. "Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae." Bioinformatics. 18 (2). 2002. pp. 351-61.
    [ PubMed ] [ WebSite ]

    MOTIVATION: Reconstructing and analyzing the metabolic map of microorganisms is an important challenge in bioinformatics. Pathway analysis of large metabolic networks meets with the problem of combinatorial explosion of pathways. Therefore, appropriate algorithms for an automated decomposition of these networks into smaller subsystems are needed. RESULTS: A decomposition algorithm for metabolic networks based on the local connectivity of metabolites is presented. Interrelations of this algorithm with alternative methods proposed in the literature and the theory of small world networks are discussed. The applicability of our method is illustrated by an analysis of the metabolism of Mycoplasma pneumoniae, which is an organism of considerable medical interest. The decomposition gives rise to 19 subnetworks. Three of these are here discussed in biochemical terms: arginine degradation, the tetrahydrofolate system, and nucleotide metabolism. The interrelations of pathway analysis of biochemical networks with Petri net theory are outlined.

    Keywords: Algorithms ; Arginine_metabolism ; Computational Biology ; *Metabolism ; Models Biological ; Mycoplasma pneumoniae_*metabolism ; Nucleotides_metabolism ; *Software


  7. Yeh, I., Karp, P.D., Noy, N.F., and Altman, R.B.. "Knowledge acquisition, consistency checking and concurrency control for Gene Ontology (GO)." Bioinformatics. 19 (2). 2003. pp. 241-8.
    [ PubMed ] [ WebSite ]

    Motivation: A critical element of the computational infrastructure required for functional genomics is a shared language for communicating biological data and knowledge. The Gene Ontology (GO; http://www.geneontology.org) provides a taxonomy of concepts and their attributes for annotating gene products. As GO increases in size its ongoing construction and maintenance becomes more challenging. In this paper, we assess the applicability of a Knowledge Base Management System (KBMS), Protege-2000, to the maintenance and development of GO. Results: We transferred GO to Protege-2000 in order to evaluate its suitability for GO. The graphical user interface supported browsing and editing of GO. Tools for consistency checking identified minor inconsistencies in GO and opportunities to reduce redundancy in its representation. The Protege Axiom Language proved useful for checking ontological consistency. The PROMPT tool allowed us to track changes to GO. Using Protege-2000, we tested our ability to make changes and extensions to GO to refine the semantics of attributes and classify more concepts. Availability: Gene Ontology in Protege-2000 and the associated code are located at http://smi.stanford.edu/projects/helix/gokbms/. Protege-2000 is available from http://protege.stanford.edu. Contact: russ.altman