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GPI-anchoring is a post-translational, reversible protein modification that is ubiquitous in eukaryotes. Such proteins are primarily present on the exoplasmic leaflet of the plasma membrane.
GPI-anchor structure
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In the plasma membrane, the lipids forming the bilayer can also act as an anchor to tether proteins to the membrane. The three main types of lipid anchors found in eukaryotes are – prenyl groups, fatty acyl groups, and glycosylphosphatidylinositol or GPI groups. Prenyl and fatty acyl groups act as anchors on the cytosolic surface of the membrane, whereas GPI anchors proteins on the extracellular side.
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Related Experiment Video

Updated: Feb 7, 2026

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Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data

Jacob Koehler1, Chris Rawlings, Paul Verrier

  • 1Rothamsted Research, BAB division, Harpenden, UK. jacob.koehler@bbsrc.ac.uk

In Silico Biology
|June 24, 2005
PubMed
Summary
This summary is machine-generated.

A novel data warehouse integrates diverse biological networks and experimental data using graph-based methods and ontologies. This approach enables flexible storage and querying of complex biological information, enhancing bioinformatics applications.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Management

Background:

  • Biological research generates diverse, complex datasets from various sources like gene expression microarrays and protein interaction studies.
  • Integrating these disparate data types (e.g., biological networks, sequence data, experimental results) presents a significant challenge for analysis and interpretation.
  • Existing data management systems often lack the flexibility to accommodate the evolving nature and varied structures of biological information.

Purpose of the Study:

  • To describe a closely integrated data warehouse designed to link diverse biological networks, sequence analysis methods, and experimental results.
  • To present a flexible data schema inspired by graph-based methods and generalized data structures (GDS) that utilizes ontologies and metadata.
  • To achieve semantic compactness and interoperability for a wide range of biological information without requiring database schema modifications.

Main Methods:

  • Biological networks are represented and stored as graphs (e.g., protein interactions, metabolic pathways).
  • Generalized Data Structures (GDS) are employed for storing supplementary information, leveraging ontologies for semantic definition of nodes and edges.
  • The text-mining framework ONDEX is extended to implement the data warehouse, ensuring direct linking to underlying biological sequences for enhanced interoperability.

Main Results:

  • A data warehouse structure capable of integrating heterogeneous biological data, including various network types and experimental outputs.
  • Implementation of a flexible data schema using graph representations and GDS, allowing semantic richness and extensibility.
  • Demonstrated interoperability with sequence analysis methods and support for bioinformatics applications like microarray analysis and systems biology modeling.

Conclusions:

  • The described data warehouse provides a semantically compact and extensible solution for managing complex biological data.
  • The graph-based approach and use of ontologies facilitate the integration and querying of diverse biological information.
  • The ONDEX-based implementation offers a robust platform for advancing bioinformatics research and applications.