Bio-medical Ontologies Maintenance and Change Management

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Current Trends in Biomedical Data and Applications 5 genome). The types of data contained in macromolecular databanks differ in the extent to which errors can be detected and corrected. For sequences, their general absence from the archive of raw data makes it difficult to detect errors unless there are multiple determinations of the same sequence, or if detailed structural analysis of the protein corresponding to the sequence makes an error appear extremely likely (Bashford et al., 1987), and the correct version of sequence suspected of being erroneous is generally impossible to infer. For protein and nucleic acid structures, in contrast, knowledge that the molecules must obey the general rules of stereochemistry and specialized rules of protein architecture makes it possible to try to evaluate the quality of a structure from the coordinate set alone. Several approaches to evaluating structures from coordinates alone have been proposed (Hooft et al., 1997). It is clear that effective error detection and correction of macromolecular structural data require the deposition and availability of these raw data, and it is very difficult to envisage any argument against requiring their deposition and distribution. Databanks have always been under competing pressures to provide data quickly and completely, but also to aim for optimal data quality. For suspect data (that doesn’t meet data quality control) one possibility is to withhold until data have been corrected (assuming that this is possible) and the other is to release data with a suitable warning. We tend to prefer the latter regime, but it is for the community as a whole to decide. Redundancy in primary molecular biology information arises as the consequence of parallel acquisition of the same or highly similar data from independent sources, inherent redundancy in the data itself, as well as small natural variation in the subjects or errors in the measurements that prevent the identification of essentially identical entries. Additional redundancy is often introduced by insufficiently coordinated annotation efforts. The situation is further complicated by the existence of two or more information resources in many subject areas, exemplified by two parallel protein sequence databanks: PIR-International (Barker et al., 1998) and SWISS-PROT (Bairoch and Apweiler, 1997); and two parallel sequence databanks: GenBank (Benson et al., 2000) and EMBL (Stoesser et al., 2003). Perhaps the most severe case of data redundancy is represented by the EST sequence collections (Adams et al., 1991). Multiple occurrences of nearly identical sequences in EST data collections are due to re-sampling of the same gene and is especially common for highly expressed genes. Several groups proposed methods to collapse clusters of related ESTs into distinct data entries representing individual genes, thereby reducing this element of redundancy by several orders of magnitude (Hide et al., 1997, Schuler et al., 1996). 3 Interoperability of Biological Data Biological data must be described in context rather than in isolation (Karp, 1996). Hence, many databases provide multiple links to other resources, but efficient use of these links requires intelligent retrieval systems. Attempts have been made to create interdatabase links automatically, restricted to few selected data
6 A.S. Sidhu, M. Bellgard, and T.S. Dillon resources, and with limited accuracy (Achard and Dessen, 1998). The user needs to be able to extract responses to a probe query from all possible sources through a transparent and easy-to-use interface. The need for interoperability gave rise to the idea of an autonomous database federation (Robbins, 1994) through partial sharing of underlying database schema permitting cross-database queries, using, for example, SQL-based commercial database implementations. For example, attempts have been made to create a unified federated resource for microbiological information (Wertheim, 1995) suitable for intelligent interrogation. The prototype of such a system is represented by the Ribosome Database Project (Maidak et al., 1996). An alternative approach is the concept of a warehouse, or a centralized data resource that manages a variety of data collections translated into a common format (Ritter, 1994). Linking the community of databases through common semantics is impossible because of their extreme heterogeneity. The recently emerged ‘middleware’ approach affords a chance to uncouple data access from data management and to allow for remote retrieval beyond the simple scripts fetching data from external databases. The most prominent industrial standard for a client-server based middleware is Common Object Request Broker Architecture or CORBA (Ben-Natan, 1995) as defined by the Object Management Group OMG. CORBA is a distributed object architecture that allows objects to communicate across networks through defined interfaces using the syntax of the Interface Definition Language (IDL). The object management architecture of CORBA specifies an application-level communication infrastructure. Several CORBA-based applications have already appeared. (Achard and Barillot, 1997) suggest a set of interface definitions for molecular biology to access a simple but realistic data bank of Sequence Tag Sites. The European Commission supports a project to provide CORBA access to a set of public databases (EMBL, SWISS-PROT, PIR, TRANSFAC, and several others). (Stein et al., 1998) described an alternative approach to database interconnection. Their software system Jade establishes a connection between the database servers and the application programs, and organizes data exchange through standardized relational tables and parameters. Information retrieved on the data server side is transformed into these tables with the help of a specialized application called Jade adapter. Jade currently supports the AceDB, although incorporation of other database systems is anticipated. 4 Summary The existing biomedical databases on the web form a loose federation of autonomous, distributed and heterogeneous data repositories. In order to make all this data really useful, one needs tools that will access and retrieve exactly the information one needs. The online available information needs to be intelligently queried. There is a need within the biomedical community for intelligent and dynamic information retrieval systems for different knowledge domains.
Page 1 and 2: Amandeep S. Sidhu and Tharam S. Dil
Page 3 and 4: Amandeep S. Sidhu and Tharam S. Dil
Page 5 and 6: Preface The molecular biology commu
Page 7 and 8: Preface VII biomedical systems, and
Page 9 and 10: X Contents Mining Clinical, Immunol
Page 11 and 12: 2 A.S. Sidhu, M. Bellgard, and T.S.
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Page 19 and 20: Towards Bioinformatics Resourceomes
Page 25 and 26: 2 The New Waves Towards Bioinformat
Page 27 and 28: 2.4 Semantic Web Towards Bioinforma
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Page 59 and 60: Appendix A Summary of Genomic Datab
Page 61 and 62: Protein Data Integration Problem Am
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Protein Data Integration Problem 59
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Fig. 3. Class Hierarchy of Protein
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72 L. Stanescu, D. Dan Burdescu, an
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Bio-medical Ontologies Maintenance
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TextToOnto (Maedche and Volz 2001)
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Extraction of Constraints from Biol
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Classifying Patterns in Bioinformat
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Mining Clinical, Immunological, and
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Substructure Analysis of Metabolic
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entry compound relation subtype com
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Running Time 4500 4000 3500 3000 25
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6 Conclusion Substructure Analysis
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266 J.Y. Chen, S. Taduri, and F. Ll
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Completing the Total Wellbeing Puzz
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296 M. Fernandez, M. Villasana, and
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Genetic Algorithm in Ab Initio Prot
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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