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

1.2. GOALS, OBJECTIVES AND TASKS 7
Figure 1.1.1: A small part of a common spectrum. The x axis reflects the mass
over charge (m/z) value and the y axis the number of times a particle was counted
by the mass spectrometer.
these single biomarkers are called Fingerprints: distinct signal patterns representing
distinguishing peptide signatures (e.g. protein fragments). Several
studies have shown the potential of such patterns for early detection of different
types of cancer (see (Kozak et al., 2005; Becker et al., 2004) and our
studies presented in chapter 4).
Unfortunately, these fingerprints are usually hidden in much larger sets of Fingerprints usually hidden and
small components hard to detect
signals, such as other (non distinguishing) peptide signals or noise (Tibshirani
et al., 2004; Gillette et al., 2005). Especially small signals - which represent
low abundant molecules (such as hormones) - are extremely hard to detect
since they are literally buried in noise. In this thesis we will introduce new New methods for detecting small
signals
algorithms to reliably detect even these small signals to allow for much more
sensitive biomarkers and thus fingerprints.
1.2 Goals, Objectives and Tasks
As pointed out in the previous section the main goal of this thesis is to find
characteristic signals (biomarkers) of a disease in mass spectra of human blood
samples. If such a signal is present in a spectrum this could mean that the
individual this sample stems from suffers from this disease. Special focus is
put on the highly increased sensitivity of detecting the signals in very large
amounts of data. Two properties that current algorithms cannot deliver.
This thesis has three main parts that are briefly described below. The first
part introduces new methods for the reliable detection of proteomics fingerprints
from noisy mass spectra. The second part deals with the application
of the newly developed pipeline in biology and in medical studies and shows
some examples. In the third part we will describe a new distributed computing
framework that allows us to analyze very large amounts of data without the
need to implement complicated computer clusters or supercomputers.
Today’s mass spectrometry (MS) based protein fingerprinting techniques
rely on the analysis of spectra from complex biological protein mixtures (e.g.
serum) obtained from high-throughput platforms in clinical settings. The
general workflow to extract fingerprints from raw data of two patient groups Fingerprint extraction workflow
is: