Abstract
The potential impact of lipid research has been
increasingly realised both in disease treatment and
prevention. Recent advances in soft ionization mass
spectrometry (MS) such as electrospray ionization (ESI)
have permitted parallel monitoring of several hundreds of
lipids in a single experiment and thus facilitated
lipidomics level studies. These advances, however, pose a
greater challenge for bioinformaticians to handle massive
amounts of information-rich MS data from modern
analytical instruments in order to understand complex
functions of lipids. The main aims of this thesis were to
1) develop bioinformatics approaches for lipid
identification based on ultra performance liquid
chromatography coupled to mass spectrometry (UPLC/MS)
data, 2) predict the functional annotations for
unidentified lipids, 3) understand the omics data in the
context of pathways and 4) apply existing chemometric
methods for exploratory data analysis as well as
biomarker discovery.
A bioinformatics strategy for the construction of lipid
database for major classes of lipids is presented using
simplified molecular input line entry system (SMILES)
approach. The database was annotated with relevant
information such as lipid names including short names,
SMILES information, scores, molecular weight,
monoisotopic mass, and isotope distribution. The database
was tailored for UPLC/MS experiments by incorporating the
information such as retention time range, adduct
information and main fragments to screen for the
potential lipids. This database information facilitated
building experimental tandem mass spectrometry libraries
for different biological tissues.
Non-targeted metabolomics screening is often get plagued
by the presence of unknown peaks and thus present an
additional challenge for data interpretation. Multiple
supervised classification methods were employed and
compared for the functional prediction of class labels
for unidentified lipids to facilitate exploratory
analysis further as well as ease the identification
process. As lipidomics goes beyond complete
characterization of lipids, new strategies were developed
to understand lipids in the context of pathways and
thereby providing insights for the phenotype
characterization. Chemometric methods such as principal
component analysis (PCA) and partial least squares and
discriminant analysis (PLS/DA) were utilised for
exploratory analysis as well as biomarker discovery in
the context of different disease phenotypes.
Original language | English |
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Qualification | Doctor Degree |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 11 Jun 2010 |
Place of Publication | Espoo |
Publisher | |
Print ISBNs | 978-951-38-7402-5 |
Electronic ISBNs | 978-951-38-7403-2 |
Publication status | Published - 2010 |
MoE publication type | G5 Doctoral dissertation (article) |
Keywords
- Lipids
- Lipidomics
- Bioinformatics
- Lipid pathways
- High density lipoproteins
- k-nearest neighbours
- Liquid chromatography/mass spectrometry
- Principal component analysis
- Partial least squares and discriminant analysis
- Obesity
- Support vector machines
- LipidDB