Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Query Languages for the Life Sciences

  • Zoé LacroixEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1437


Biological data retrieval, integration, and transformation; Biological query Languages; Scientific query Languages


A scientific query language is a query language that expresses the data retrieval, analysis, and transformation tasks involved in the dataflow pertaining to a scientific protocol (or equivalently workflow, dataflow, pipeline). Scientific query languages typically extend traditional database query languages and offer a variety of operators expressing scientific tasks such as ranking, clustering, and comparing in addition to operators specific to a category of scientific objects (e.g., biological sequences).

Historical Background

A scientific query may involve data retrieval tasks from multiple heterogeneous resources and perform a variety of analysis, transformation, and publication tasks. Existing approaches used by scientists include hard coded scripts, data warehouses, link-based federations, database mediation systems, and workflow systems. Hard...

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Recommended Reading

  1. 1.
    Bartlett JC, Toms EG. Developing a protocol for bioinformatics analysis: an integrated information behaviors and task analysis approach. J Am Soc for Inf Sci Technol. 2005;56(5):469–82.CrossRefGoogle Scholar
  2. 2.
    Buneman P, Naqvi SA, Tannen V, Wong L. Principles of Programming with Complex Objects and Collection Types. Theor Comput Sci. 1995;149(1):3–48.MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    I-Min Chen A, Markowitz Victor M. An overview of the object-protocol model (OPM) and OPM data management tools. Inf Syst. 1995;20(5):393–418.CrossRefGoogle Scholar
  4. 4.
    Etzold T, Harris H, Beaulah S. SRS-5 Chapter: an integration platform for databanks and analysis tools in bioinformatics,, In: Lacroix, Critchlow. 2003. p. 109–46. [6].Google Scholar
  5. 5.
    Hunter L, Cohen KB. Biomedical language processing: perspective what’s beyond PubMed? Mol Cell. 2006;21(5):589–94.CrossRefGoogle Scholar
  6. 6.
    Lacroix Z, Critchlow T. Bioinformatics: managing scientific data. San Francisco: Morgan Kaufmann; 2003.Google Scholar
  7. 7.
    Lacroix Z, Ludaescher B, Stevens R. Integrating biological databases. chapter 42. 2007. p. 1525–72. Vol. 3 of Lengauer [8].Google Scholar
  8. 8.
    Lengauer T. Bioinformatics – from genomes to therapies. Weinheim: Wiley-VCH Publishers; 2007.CrossRefGoogle Scholar
  9. 9.
    Seeber I. Patent searches as a complement to literature searches in the life sciences-a ‘how-to’ tutorial. Nat Protoc. 2007;2(10):2418–28.CrossRefGoogle Scholar
  10. 10.
    Shipman DW. The functional data model and the data language DAPLEX. ACM Trans Database Syst. 1981;6(1):140–73.CrossRefGoogle Scholar
  11. 11.
    Stevens R, Goble C, Baker P, Brass A. A classification of tasks in bioinformatics. Bioinformatics. 2001;17(2):180–8.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Arizona State UniversityTempeUSA

Section editors and affiliations

  • Louiqa Raschid
    • 1
  1. 1.Robert H. Smith School of BusinessUniversity of MarylandCollege ParkUSA