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Open Access Methodology article

Prototype semantic infrastructure for automated small molecule classification and annotation in lipidomics

Leonid L Chepelev1, Alexandre Riazanov2, Alexandre Kouznetsov2, Hong Sang Low3, Michel Dumontier145* and Christopher JO Baker2*

Author Affiliations

1 Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, Canada

2 Department of Computer Science and Applied Statistics, University of New Brunswick, 100 Tucker Park Road, Saint John, Canada

3 School of Computing, National University of Singapore, 21 Lower Kent Ridge Road, Singapore

4 Institute of Biochemistry, Carleton University, 1125 Colonel By Drive, Ottawa, Canada

5 School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Canada

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BMC Bioinformatics 2011, 12:303  doi:10.1186/1471-2105-12-303

Published: 26 July 2011

Abstract

Background

The development of high-throughput experimentation has led to astronomical growth in biologically relevant lipids and lipid derivatives identified, screened, and deposited in numerous online databases. Unfortunately, efforts to annotate, classify, and analyze these chemical entities have largely remained in the hands of human curators using manual or semi-automated protocols, leaving many novel entities unclassified. Since chemical function is often closely linked to structure, accurate structure-based classification and annotation of chemical entities is imperative to understanding their functionality.

Results

As part of an exploratory study, we have investigated the utility of semantic web technologies in automated chemical classification and annotation of lipids. Our prototype framework consists of two components: an ontology and a set of federated web services that operate upon it. The formal lipid ontology we use here extends a part of the LiPrO ontology and draws on the lipid hierarchy in the LIPID MAPS database, as well as literature-derived knowledge. The federated semantic web services that operate upon this ontology are deployed within the Semantic Annotation, Discovery, and Integration (SADI) framework. Structure-based lipid classification is enacted by two core services. Firstly, a structural annotation service detects and enumerates relevant functional groups for a specified chemical structure. A second service reasons over lipid ontology class descriptions using the attributes obtained from the annotation service and identifies the appropriate lipid classification. We extend the utility of these core services by combining them with additional SADI services that retrieve associations between lipids and proteins and identify publications related to specified lipid types. We analyze the performance of SADI-enabled eicosanoid classification relative to the LIPID MAPS classification and reflect on the contribution of our integrative methodology in the context of high-throughput lipidomics.

Conclusions

Our prototype framework is capable of accurate automated classification of lipids and facile integration of lipid class information with additional data obtained with SADI web services. The potential of programming-free integration of external web services through the SADI framework offers an opportunity for development of powerful novel applications in lipidomics. We conclude that semantic web technologies can provide an accurate and versatile means of classification and annotation of lipids.