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

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152CHAPTER 6. PROTEOMICS.NET - PRODUCT-ORIENTED CASE STUDIES Organization of data and results: Often a project lacks sufficient organization, documentation and storage of source and resulting data or parameters used in different analyses. The presented platform does solve these problems in the domain of (mass spectrometry based) proteomics research. Once the raw data is integrated into the system each analysis performed is fully documented and each result is stored and can be traced back to the data and parameter-sets used. Comparison of results: During the course of a (data centered) project usually many different algorithms are developed or tested. Additionally, most algorithms can be parameterized in many ways often with large variations in the results. The platform presented here cannot only organize the results depending on algorithms and parameters and thus enable reasonable comparisons but also provides consistent visualization. Exploring parameter space: Many algorithms depend on a lot of input parameters. Determining and optimizing these parameters for a particular dataset or certain purpose is not always possible. The presented platform in combination with the QAD Grid workflow system does not only allow to create different parameter-sets but also starts the desired algorithms with these input parameters on many different machines in parallel and monitors the execution and can also evaluate the results and send reports to the user. Rapid Grid application development: Thanks to the worker concept and available web-services embedding of almost any available program code becomes possible quite quickly - even on specialized hardware. 6.1 Available Services The proteomics.net platform offers the following services: � Web portal for (MS-based) proteome research – Fostering organization, exchange and provision of (raw) data and (intermediate) results) – Provision and easy usage of tools and algorithms (section 6.2.1) – Providing of web services to use available algorithms in local applications (section 6.2.2) � Distributed computing (Grid) services – Enabling processing and analysis of proteomics mass data faster than real time – Allowing integrating of specialized hardware, such as IBM’s Cell/BE processor in Sony’s Playstation 3 for compute intensive tasks (section 6.2.4) – Enabling non-Grid applications to use the power of a distributed computing environment (section 6.2.5) – Easy creation and integration of Grid applications (section 6.2.6)
6.2. CASE STUDIES 153 6.2 Case Studies 6.2.1 Software as a Service: Using the Web Based Platform Services This section describes the main concept of the proteomics.net platform: host, operate and provide algorithms that can be used over the internet with userspecific data. We will give a brief example in the proteomics context that shows the benefits of this concept for an user that does not have the needed IT infrastructure to solve a particular problem. It is generally suited for users that have little interest or capability in software deployment, but do have substantial computing needs. Background Performing an arbitrary data analysis requires of course (1) the data to be analyzed and (2) a suitable analysis algorithm (computer program). Often there arise problems concerning (2): installing, maintaining and running a software package is not always a trivial task. Problems like insufficient hardware (e.g. memory) or an incompatible software platform (e.g. operating system or libraries) can render the use of a particular software impossible. Our Approach: Software as a Service With the proteomics.net platform we have chosen to provide our algorithms using the Software as a Service (SaaS) approach. This means, that the algorithms are hosted on our IT infrastructure and can be used through a web based user interface. To give an example we briefly sketch the steps a user needs to execute to perform a peak picking analysis (as described in section 3.4: 1. Open the platform’s web portal in a web browser 2. Log into the platform (Figure 6.1(a)) 3. Upload the data to be analyzed (e.g. by FTP) 4. Start a new peak picking analysis (figure 6.1(b)) 5. Select the dataset and parameter-set to be used (figure 6.2(a)) 6. Assign a name and description (Figure 6.2(b)) 7. Start the analysis After the peak picking analysis is finished the results will be available for inspection, further usage or export/download. Note that the results are automatically � archived in the system � linked to the raw data the peak picking was performed on � linked to the parameter-set used Note that the data analysis is automatically scheduled to the best matching computing ressources available.
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New Statistical Algorithms for the
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Contents Acknowledgments . . . . .
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New Statistical Algorithms for the
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Extended Abstract English Version M
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German Version Das Gebiet der Prote
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Chapter 1 Introduction and Survey 1
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1.2. GOALS, OBJECTIVES AND TASKS 7
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1.2. GOALS, OBJECTIVES AND TASKS 9
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Chapter 2 Preliminaries 2.1 Topic O
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2.1. TOPIC OVERVIEW 13 Figure 2.1.1
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2.1. TOPIC OVERVIEW 15 completeness
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2.2. AN EXAMPLE 17 (a) Opera A (b)
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2.2. AN EXAMPLE 19 Figure 2.2.6: Tw
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2.2. AN EXAMPLE 21 successes. We ca
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Chapter 3 Mathematical Modeling and
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3.2. INTRODUCTION TO MALDI TOF MS 2
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3.3. PREPROCESSING 31 mix (external
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3.3. PREPROCESSING 33 Figure 3.3.5:
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3.3. PREPROCESSING 35 Figure 3.3.7:
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3.4. HIGHLY SENSITIVE PEAK DETECTIO
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3.5. PEAK DETECTION IN 2D MAPS 43
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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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Page 185 and 186: REFERENCES 179 Breiman, L. (2001).
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Page 191 and 192: REFERENCES 185 Huyghe, E., Muller,
Page 193 and 194: REFERENCES 187 Kuijpens, J. L. P.,
Page 195 and 196: REFERENCES 189 McLachlan, S. M. and
Page 197 and 198: REFERENCES 191 Platt, J. C. (1999).
Page 199 and 200: REFERENCES 193 Stone, M. (1974). Cr
Page 201 and 202: REFERENCES 195 Washburn, M. P., Wol
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