New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...

Extended Abstract
English Version
Mass spectrometry (MS) based techniques have emerged as a standard for
large-scale protein analysis. The ongoing progress in terms of more sensitive MS standard for large scale protein
analysis
machines and improved data analysis algorithms led to a constant expansion of
its fields of applications. Recently, MS was introduced into clinical proteomics
with the prospect of early disease detection using proteomic pattern matching. MS for early disease detection
Analyzing biological samples (e.g. blood) by mass spectrometry generates
mass spectra that represent the components (molecules) contained in a sample
as masses and their respective relative concentrations. It is well known that Mass spectra represent relative
an individual’s proteome is highly dynamic and changes quite dramatically
during a day, depending on a variety of factors. However, analyzing a large
enough group of similar individuals (e.g. “healthy” or “suffering from disease
concentrations of molecules in a
sample
X”) allows to identify components in the respective spectra that do not differ An individual’s proteome is highly
much - with respect to concentration - between individuals from the same
group (constant components).
In this work, we are interested in those components that are constant
within a group of individuals but differ much between individuals of two distinct
groups. These distinguishing components that dependent on a particular
medical condition are generally called biomarkers. Since not all biomarkers
dynamic and influenced by e.g.
diseases
found by the algorithms are of equal (discriminating) quality we are only in- In this thesis: search for spectra
terested in a small biomarker subset that - as a combination - can be used
components that reflect particular
diseases
as a fingerprint for a disease. Once a fingerprint for a particular disease (or Best components can be combined to
a disease’s fingerprint
medical condition) is identified, it can be used in clinical diagnostics to classify
unknown spectra.
This mass spectrometry based method appears to be one of the arising key
technologies for biomarker discovery, understanding of biological mechanisms,
and consequently, it might offer new approaches in drug development.
In this thesis we have developed new algorithms for automatic extraction
of disease specific fingerprints from mass spectrometry data. Special empha- New algorithms for automatic
sis has been put on designing highly sensitive methods with respect to signal
extraction of fingerprints have been
developed
detection. This is extremely important in all stages of the pipeline (such as Focus was laid on developing highly
spectra preprocessing, signal detection, signal analysis and identification of
disease specific fingerprints) since many biologically relevant molecules are
found to be very low abundant (such as hormones) thus yielding (comparatively)
small signals. Thanks to our statistically based approach our methods
are able to detect signals even below the noise level inherent in data acquired
by common MS machines.
To provide access to these new classes of algorithms to collaborating groups
we have created a web-based analysis platform that provides all necessary
sensitive algorithms to allow
detection of low abundant molecules
within noise
interfaces for data transfer, data analysis and result inspection. Following A new analysis platform for signal
(pre-)processing and extraction of
disease specific fingerprints was
developed
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