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

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Acknowledgments Diese zum Schluss verfassten Zeilen meiner Dissertation möchte ich nutzen, um denjenigen zu danken, die mich in den letzten drei Jahren begleitet, unterstützt und gefördert haben. Mein besonderer Dank gilt meinem Betreuer Prof. Christof Schütte für die ständige Unterstützung und Förderung, vor allem aber für den gewährten Freiraum, um eigene Ideen entwickeln zu können. Weiterhin möchte ich mich auch bei meinem Zweitbetreuer Prof. Knut Reinert für wertvolle Hinweise und Anregungen und bei Dr. André Hagehülsmann für die Übernahme des Koreferats und seiner Hilfe bei der Einwerbung der Projektfinanzierung bedanken. Ein herzlicher Dank gilt meinen Eltern, meinen Brüdern Kay und Jens und meinen Freunden und Kollegen innerhalb und außerhalb der BioComputing Arbeitsgruppe - insbesondere Jana -, die alle auf ihre Weise zum Gelingen dieser Arbeit beigetragen haben. Zum Schluss möchte ich noch der Firma Microsoft Research Ltd. in Cambridge (UK) danken, die mich durch ein Promotionsstipendium finanziell unterstützt hat. Tim OF Conrad Freie Universität Berlin May 2008 vi
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 1
Page 1 and 2: New Statistical Algorithms for the
Page 3 and 4: Contents Acknowledgments . . . . .
Page 5: New Statistical Algorithms for the
Page 9 and 10: German Version Das Gebiet der Prote
Page 11 and 12: Chapter 1 Introduction and Survey 1
Page 13 and 14: 1.2. GOALS, OBJECTIVES AND TASKS 7
Page 15 and 16: 1.2. GOALS, OBJECTIVES AND TASKS 9
Page 17 and 18: Chapter 2 Preliminaries 2.1 Topic O
Page 19 and 20: 2.1. TOPIC OVERVIEW 13 Figure 2.1.1
Page 21 and 22: 2.1. TOPIC OVERVIEW 15 completeness
Page 23 and 24: 2.2. AN EXAMPLE 17 (a) Opera A (b)
Page 25 and 26: 2.2. AN EXAMPLE 19 Figure 2.2.6: Tw
Page 27 and 28: 2.2. AN EXAMPLE 21 successes. We ca
Page 29 and 30: Chapter 3 Mathematical Modeling and
Page 31 and 32: 3.2. INTRODUCTION TO MALDI TOF MS 2
Page 37 and 38: 3.3. PREPROCESSING 31 mix (external
Page 39 and 40: 3.3. PREPROCESSING 33 Figure 3.3.5:
Page 41 and 42: 3.3. PREPROCESSING 35 Figure 3.3.7:
Page 43 and 44: 3.4. HIGHLY SENSITIVE PEAK DETECTIO
Page 49 and 50: 3.5. PEAK DETECTION IN 2D MAPS 43
Page 51 and 52: 3.6. PEAK REGISTRATION (ALIGNMENT)
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3.7. IDENTIFYING POTENTIAL FEATURES
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3.8. EXTRACTING FINGERPRINTS 57 Fig
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3.8. EXTRACTING FINGERPRINTS 59 FID
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3.8. EXTRACTING FINGERPRINTS 61 Dim
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3.9. COMPLEXITY ANALYSIS 63 3.9 Com
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Chapter 4 (Bio-)Medical Application
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4.1. DATA USED 67 4.1.2 Serum Data
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4.2. STATISTICAL REMARKS 69 1. Vali
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4.2. STATISTICAL REMARKS 71 Molar v
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4.2. STATISTICAL REMARKS 73 � Fir
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4.2. STATISTICAL REMARKS 75 Let ˆ
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4.2. STATISTICAL REMARKS 77 the boo
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4.3. STUDY RESULTS 79 Figure 4.3.3:
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4.3. STUDY RESULTS 81 Figure 4.3.4:
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4.3. STUDY RESULTS 83 � kNN (gen.
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4.3. STUDY RESULTS 85 d(x, θi) =
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4.3. STUDY RESULTS 87 pairs of obje
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4.3. STUDY RESULTS 89 � “Peptid
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4.4. IDENTIFICATION OF PROTEOMIC FI
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4.6. BIOLOGICAL APPLICATIONS 101 4.
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Chapter 5 Computer Science Grid Str
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5.1. INTRODUCTION 105 � A node is
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5.1. INTRODUCTION 107 of and config
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5.1. INTRODUCTION 109 particular pr
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5.2. THE QUASI AD-HOC (QAD) GRID 11
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5.2. THE QUASI AD-HOC (QAD) GRID 11
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5.3. QAD GRID PLATFORM SERVER 115 F
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5.3. QAD GRID PLATFORM SERVER 117 j
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5.3. QAD GRID PLATFORM SERVER 119 (
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5.3. QAD GRID PLATFORM SERVER 121 p
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5.3. QAD GRID PLATFORM SERVER 123 D
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5.3. QAD GRID PLATFORM SERVER 125 F
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5.3. QAD GRID PLATFORM SERVER 127 t
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5.4. QAD GRID WORKER 129 field. A w
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5.4. QAD GRID WORKER 131 2. This re
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5.4. QAD GRID WORKER 133 Figure 5.4
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5.4. QAD GRID WORKER 135 Checkpoint
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5.4. QAD GRID WORKER 137 database b
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5.5. QAD GRID PLATFORM SERVICES 139
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5.6. QAD GRID WORKFLOWS 141 � Dat
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5.6. QAD GRID WORKFLOWS 143 Service
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5.6. QAD GRID WORKFLOWS 145 Figure
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5.7. RELATED WORK 147 to set-up sys
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5.7. RELATED WORK 149 Table 5.7.1 -
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Chapter 6 proteomics.net - Product-
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6.2. CASE STUDIES 153 6.2 Case Stud
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6.2. CASE STUDIES 155 Figure 6.2.2:
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6.2. CASE STUDIES 157 MASCOT and SE
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6.2. CASE STUDIES 159 The peak pick
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6.2. CASE STUDIES 161 first entry i
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6.2. CASE STUDIES 163 Approach Base
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6.2. CASE STUDIES 165 Figure 6.2.5:
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Chapter 7 Related Work In this chap
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Chapter 8 Conclusion and Future Dir
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8.3. FROM BIOMARKERS TO BIOPRINTS 1
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Appendix A Implementation Details T
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Appendix B Curriculum Vitae Name Ti
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References Aebersold, R. and Mann,
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REFERENCES 179 Breiman, L. (2001).
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REFERENCES 181 Downard, K. M. and M
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REFERENCES 183 Gillette, M. A., Man
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REFERENCES 185 Huyghe, E., Muller,
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REFERENCES 187 Kuijpens, J. L. P.,
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REFERENCES 189 McLachlan, S. M. and
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REFERENCES 191 Platt, J. C. (1999).
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REFERENCES 193 Stone, M. (1974). Cr
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REFERENCES 195 Washburn, M. P., Wol
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