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Ontologies for Bioinformatics

  • Andrea Splendiani
  • Michele Donato
  • Sorin Drăghici

Abstract

This chapter provides an introduction to ontologies and their application in bioinformatics.

It presents and overview of the range of information artifacts that are denoted as ontologies in this field, from controlled vocabularies to rich axiomatizations. It then focuses on the conceptual nature of ontologies and introduces the role of upper ontologies in the conceptualization process.

Language and technologies that underpin the definition and usage of ontologies are then presented, with a particular focus on the ones derived from the semantic web framework. One objective of this chapter is to provide a concise and effective understanding of how technologies and concepts such as ontologies, RDF, OWL, SKOS, reasoning and Linked-Data relate to each other. The chapter is then complemented by a bioinformatics section (Sect. 27.4), both via an overview of the evolution of ontologies in this discipline, and via a more detailed presentation of a few notable examples such as gene ontologies (and the OBO family), BioPAX and pathway ontologies and UMLS. Finally, the chapter presents examples of a few areas where ontologies have found a significant usage in bioinformatics: data integration, information retrieval and data analysis (Sect. 27.5). This last section briefly lists some tools exploiting the information contained in biomedical ontologies when paired with the output of high-throughput experiments such as cDNA microarrays.

Keywords

Gene Ontology Resource Description Framework Ontological Commitment Unify Medical Language System Biomedical Ontology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

API

application programming interface

BFO

basic formal ontology

BioPAX

biological pathways exchange

CUI

concept unique identifier

CiTO

citation typing ontology

DC

direct current

DE

differential evolution

DNA

deoxyribonucleic acid

DOLCE

descriptive ontology for linguistics and cognitive engineering

GO

gene ontology

GOA

GO annotation

IAO

information artifact ontology

JSON

JavaScript object notation

LSID

life science identifier

MAGE-ML

microarray and gene expression markup language

MAGE-OM

microarray gene expression object model

MIAME

minimum information about a microarray experiment

NCBO

National Center for Biomedical Ontologies

NLM

US National Library of Medicine

NLP

natural-language processing

OBI

ontology of biomedical investigation

OBO

open biology ontology

OBO

open biomedical ontology

OVTK

Ondex Visual Tool Kit

OWL-DL

OWL description logic

OWL

ontology web language

PDSP

Psychoactive Drug Screening Program

RDF

resource description framework

RDFS

resource description framework schema

REST

representational state transfer

RNA

ribonucleic acid

RO

relations ontology

SADI

semantic automated discovery and integration

SKOS

simple knowledge organization system

SNAP

soluble NSF attachment protein

SNOMED

Systematized Nomenclature of Medicine-Clinical Terms

SPARQL

a query system for the Semantic Web

SUMO

suggested upper merged ontology

UML

unified medical language

UMLS

unified medical language system

URI

uniform resource identifier

URL

unified resource locator

XML

extensible markup language

cDNA

complementary DNA

References

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    P. Romano, A. Splendiani: Applications of Semantic Web methodologies and techniques to biology and bioinformatics, LNCS 5224, 200–239 (2008)Google Scholar
  2. 27.2.
    D. Allemang, J. Hendler: Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL (Morgan Kaufmann, Burlington 2009)Google Scholar
  3. 27.3.
    B. Smith, W. Ceusters: Ontological realism as a methodology for coordinated evolution of scientific ontologies, Appl. Ontol. 5, 139–188 (2010)Google Scholar
  4. 27.4.
    A. Splendiani, M. Gündel, J.M. Austyn, D. Cavalieri, C. Scognamiglio, M. Brandizi: Knowledge sharing and collaboration in translational research, and the DC-THERA Directory, Brief. Bioinforma. 12(6), 562–575 (2011)CrossRefGoogle Scholar
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    S. Drăghici: Statistics and Data Analysis for Microarrays Using R and Bioconductor, 2nd edn. (Chapman Hall/CRC, New York 2011)Google Scholar
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    P. Khatri, S. Drăghici: Ontological analysis of gene expression data: Current tools, limitations, and open problems, Bioinformatics 21, 3587–3595 (2005)CrossRefGoogle Scholar
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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Rothamsted ResearchHarpendenUK
  2. 2.Department of Computer ScienceWayne State UniversityDetroitUSA
  3. 3.Department of Computer ScienceWayne State UniversityDetroitUSA

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