Conference Paper

Algorithms and databases in bioinformaties: Towards a proteomic ontology

Magna Graecia Univ., Catanzaro, Italy
DOI: 10.1109/ITCC.2005.63 Conference: Information Technology: Coding and Computing, 2005. ITCC 2005. International Conference on, Volume: 1
Source: IEEE Xplore


Classification of bioinformatics tools and databases, as well as modelling of biological processes, can help the design of complex in silico experiments. After presenting a survey of bioinformatics algorithms and biological databases, we describe a first design of an ontology for the Proteomics domain. The ontology can be used either to better understand biological processes or to enhance design of distributed bioinformatics applications.

Download full-text


Available from: Tommaso Mazza
  • Source
    • "As an example, BioRegistry [28], a structured repository of metadata related to bioinformatics databases, conformed to Dublin Core Metadata Initiative (DCMI). Cannataro et al. in [7] investigated the issue of classification of databases and algorithms for proteomics. The W3C consortium founded a SIG in Semantic Web for Health Care and Life Science (HCLSIG) to drive the adoption of its SW standards (i.e. "
    [Show abstract] [Hide abstract]
    ABSTRACT: In this work we revise the layered software architecture for the Knowledge Grid by explicitly introducing the concept of Resourceome, i.e. an “alive” ontology of resources. This approach is necessary to tackle the challenges posed by the “ome” status, reached by the world of bioinformatics resources. The resulting software architecture, a Resourceomic Grid, integrates components to support issues like awareness, discovery, integration and abstraction of resources.
    Full-text · Article · Mar 2007 · Future Generation Computer Systems
  • [Show abstract] [Hide abstract]
    ABSTRACT: Semantic Grid applications based on the cooperation of Grid Services need semantic modeling tools and functions to access and manage ontologies stored on the Grid. Although many tools and methodologies exist for ontology editing and management, they often work in a centralized way and are not available on the Grid. The paper proposes a distributed ontology management framework where ontology schema is replicated and instances are distributed among nodes of the Grid. A distributed ontology kernel that offers querying and updating APIs and a graphical query interface for ontology browsing have been fully designed. The kernel leverages distributed data management functions offered by the Grid middleware to store and access ontology fragments. It has been embedded into an ontology-based workflow editor allowing distributed users to design and execute bioinformatics applications. Domain ontologies, partitioned and managed by the ontology kernel, are accessed by the editor enhancing workflow composition. The ontology distribution allows to design a complete application whatever availability of Grid nodes.
    No preview · Article · Jan 2006
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Biological function is to a large extent mediated and controlled by interactions among proteins. The study of interactions about proteins has lead to the accumulation of a large amount of data, also referred to as protein-protein interaction (PPI) data. Such data, stored in publicly available databases, are often queried by using simple keybased query interfaces with little semantic. Current PPI databases enable the retrieval of one or more proteins that interact with a target protein using target protein identifier. Nevertheless, a lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing PPI databases with biological information may result in richer querying interface and successively could enable the development of novel algorithms that may use such biological information. The main contributions of this paper are: (i) a framework able to extend existing PPI databases by using ontologies, and (ii) a semantic based querying interface. The framework merges PPI data with annotations extracted from Gene Ontology and stores annotated data into a database. Then, a semantic-based query interface enables users to query these data by using biological concepts. Finally, a real case study showing the effectiveness of such framework on the analysis of PPI data is also presented.
    Full-text · Conference Paper · Jan 2009