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This article is part of the supplement: Semantic e-Science in Biomedicine

Open Access Research

LinkHub: a Semantic Web system that facilitates cross-database queries and information retrieval in proteomics

Andrew K Smith2, Kei-Hoi Cheung2346, Kevin Y Yip2, Martin Schultz2 and Mark B Gerstein125*

Author Affiliations

1 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, 06520, USA

2 Department of Computer Science, Yale University, New Haven, Connecticut, 06520, USA

3 Center for Medical Informatics, Yale University, New Haven, Connecticut, 06520, USA

4 Department of Genetics, Yale University, New Haven, Connecticut, 06520, USA

5 Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, 06511, USA

6 Department of Anesthesiology, Yale University, New Haven, Connecticut, 06520, USA

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BMC Bioinformatics 2007, 8(Suppl 3):S5  doi:10.1186/1471-2105-8-S3-S5

Published: 9 May 2007

Abstract

Background

A key abstraction in representing proteomics knowledge is the notion of unique identifiers for individual entities (e.g. proteins) and the massive graph of relationships among them. These relationships are sometimes simple (e.g. synonyms) but are often more complex (e.g. one-to-many relationships in protein family membership).

Results

We have built a software system called LinkHub using Semantic Web RDF that manages the graph of identifier relationships and allows exploration with a variety of interfaces. For efficiency, we also provide relational-database access and translation between the relational and RDF versions. LinkHub is practically useful in creating small, local hubs on common topics and then connecting these to major portals in a federated architecture; we have used LinkHub to establish such a relationship between UniProt and the North East Structural Genomics Consortium. LinkHub also facilitates queries and access to information and documents related to identifiers spread across multiple databases, acting as "connecting glue" between different identifier spaces. We demonstrate this with example queries discovering "interologs" of yeast protein interactions in the worm and exploring the relationship between gene essentiality and pseudogene content. We also show how "protein family based" retrieval of documents can be achieved. LinkHub is available at hub.gersteinlab.org and hub.nesg.org with supplement, database models and full-source code.

Conclusion

LinkHub leverages Semantic Web standards-based integrated data to provide novel information retrieval to identifier-related documents through relational graph queries, simplifies and manages connections to major hubs such as UniProt, and provides useful interactive and query interfaces for exploring the integrated data.