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Prototype Semantic Infrastructure for Automated Small Molecule Classification and Annotation in Lipidomics.

From: Chris Baker <denguehost@gmail.com>
Date: Wed, 3 Aug 2011 08:02:19 -0500
Message-ID: <CALDyQEUNSeHMMZr2kEUj9fBeBAg3mi_Cv1W=OEMBgeJic3=MkA@mail.gmail.com>
To: HCLS <public-semweb-lifesci@w3.org>
Hi -

As a follow on to presentations i made on the lipid ontology at
ICBO2009, BioRDF W3C-HCLS 2010 and CSHALS2011,
here is the BMC Bioinfromatics paper detailing how we use lipid
ontology for small molecule classification and deploy
this functionality using SADI Semantic web services.



BMC Bioinformatics. 2011 Jul 26;12(1):303. [Epub ahead of print]
Prototype Semantic Infrastructure for Automated Small Molecule
Classification and Annotation in Lipidomics.
Chepelev LL, Riazanov A, Kouznetsov A, Low HS, Dumontier M, Baker CJ.

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

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.

PMID:21791100[PubMed - as supplied by publisher] Free full text
LinkOut - more resourcesFull Text SourcesBioMed Central

Christopher J. O. Baker Ph. D.
Associate Professor
Dept. Computer Science and Applied Statistics
University of New Brunswick, Canada
Received on Wednesday, 3 August 2011 13:02:46 UTC

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