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Addressing the problems with life-science databases for traditional uses and systems biology

Nature Reviews Genetics volume 7, pages 482–488 (2006)Cite this article

Abstract

A prerequisite to systems biology is the integration of heterogeneous experimental data, which are stored in numerous life-science databases. However, a wide range of obstacles that relate to access, handling and integration impede the efficient use of the contents of these databases. Addressing these issues will not only be essential for progress in systems biology, it will also be crucial for sustaining the more traditional uses of life-science databases.

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Figure 1: Classical and systems biology roles of life-science databases.
Figure 2: The database integration process: a database warehouse as an example.
Figure 3: Alternative representations of metabolic pathways: alcohol dehydrogenase as an example.

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Acknowledgements

The authors would like to thank C. Rawlings and P. Verrier for commenting on an earlier version of this article. Furthermore we would like to thank the following individuals for exploring with us the pitfalls of life-science databases over the past years: J. Baumbach, J. Butz, E. Kirchem, F. Klingert, S. Knop, B. Kormeier, I. Kupp, A. Neu, A. Rüegg, A. Skusa, B. Steuernagel, J. Taubert, P. Verrier and R. Winnenburg. S.P. gratefully acknowledges funding by the European Science Foundation. Rothamsted Research receives grant-aided support from the UK Biotechnological and Biological Science Research Council.

Author information

Authors and Affiliations

  1. Stephan Philippi is at the Department of Computer Science, University of Koblenz, PO Box 201602, Koblenz, 56016, Germany

    Stephan Philippi

  2. Jacob Köhler is at the Biomathematics and Bioinformatics Division, Rothamsted Research, Harpenden, AL5 2JQ, Hertfordshire, UK

    Jacob Köhler

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Correspondence to Stephan Philippi.

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Related links

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FURTHER INFORMATION

BioJava

BioPAX — Biological Pathways Exchange

BioPerl

BioRuby

CellML

DiscoveryLink

DNA Data Bank of Japan

EC (enzyme class) numbers of the enzyme nomenclature

Ensembl Trace Server

European Bioinformatics Institute SRS server

European Bioinformatics Institute

Extensible Markup Language (XML)

Gene Ontology homepage

Kyoto Encyclopedia of Genes and Genomes

Microarray Gene Expression Data Society

mySQL

NCBI taxonomy

Nucleic Acids Research Database Categories List

ONDEX

Open Biomedical Ontologies

Open Source Initiative License Index

PostgreSQL

Proteomics Standards Initiative — molecular interaction

Systems biology markup language

Universal Protein Resource

Web Services Activity

Glossary

Controlled vocabulary

A standardized set of terms that can be used in a given application domain. A prominent example is the enzyme class nomenclature, which describes classes of biochemical reaction.

Database management system

A system that provides a means of storing, modifying and extracting data from a database.

Evidence code

A controlled vocabulary that is used to track the types of evidence that support a gene annotation.

Flat file

Human readable, non-standardized files that can be used to exchange the contents of life-science databases.

Ontology

A commonly agreed definition of real-world concepts, such as 'protein' and 'enzyme', and their particular relationships, for example, an enzyme 'is a' protein.

Parser

Software that reads a given input, such as a flat file, for further processing.

Web service

A standardized way to allow for interoperable machine-to-machine interaction over a network.

XML

The extensible markup language (XML) is a standard for the creation of application-specific, self-descriptive markup languages, which, for example, can be used for the definition of data-exchange formats.

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Philippi, S., Köhler, J. Addressing the problems with life-science databases for traditional uses and systems biology. Nat Rev Genet 7, 482–488 (2006). https://doi.org/10.1038/nrg1872

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