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

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160CHAPTER 6. PROTEOMICS.NET - PRODUCT-ORIENTED CASE STUDIES Approach Matlab Scripts The scripting language Matlab is quite popular for quick coding of mathematical algorithms. A typical algorithm gets some input parameters and data and then computes and returns the results. To allow almost any user-defined matlab script to be run within a QAD Grid two things must be done: (a) a special Matlab worker must be started at the Grid nodes that will then run the user-defined Matlab scripts and (b) the function call of the user-defined Matlab script must be changed to take two more parameters. If the user-defined script is called from the Matlab worker these two additional parameters contain (1) a string “MCP” and (2) an object reference to the underlying QAD Grid worker. Through this the base QAD Grid worker functions (see section 5.4.1) can be used, such as writing log entries or change the worker’s status. If the Matlab worker is started at a QAD Grid node it performs the following steps: 1. Start Matlab at the Grid node 2. Start the Matlab worker script 3. Within the Matlab worker: � Wait until a new Matlab task is available � Request the name of the user-defined Matlab script to be started � Request the parameters for the user-defined Matlab script � Start the user-defined Matlab script The Matlab worker core that gets the parameters from the QAD Grid and starts the user-defined script is implemented as shown in listing 6.2: Listing 6.2: Minimal code for using the a Matlab worker. 1 params = jPROTEOMICSWORKER MATLAB MCP. g e t n e x t t a s k ( ) ; 2 3 i f ( params ˜= [ ] ) 4 % param1 : name o f s c r i p t 5 % param2 − param8 : params f o r c a l l e d s c r i p t 6 m s c r i p t = char ( params ( 1 ) ) ; 7 8 . . . 9 10 c = [ m s c r i p t ’ ( ”MCP” , jPROTEOMICSWORKER MATLAB MCP ’ ] ; 11 for i = 2 : 9 12 i f ( ˜ isempty ( char ( params ( i ) ) ) ) 13 c = [ c ’ , ” ’ char ( params ( i ) ) ’ ” ’ ] ; 14 end 15 end 16 c = [ c ’ ) ’ ] ; 17 18 jPROTEOMICSWORKER MATLAB MCP. c h a n g e s t a t e ( [ ’ P r o c e s s i n g Task ID : ’ 19 id ’ − c a l l i n g : ’ c ] ) ; 20 c = strrep ( c , ’ ” ’ , ’ ’ ’ ’ ) ; 21 22 % e x e c u t e matlab s c r i p t 23 eval ( c ) ; 24 end ; Line 1 requests a new task from the QAD Grid. If a new job is available the 9-dimensional array “params” contains the parameters of this task. The
6.2. CASE STUDIES 161 first entry in “params” contains the filename of the user-defined script, the remaining array entries contain the parameters. Line 10-16 show how the calling string “c” is built and that the first two parameters mentioned above are the “MCP” string and the object reference (line 10). Line 18 shows how the QAD Grid worker object can be used to communicate directly with the QAD Grid: here, the state string of the worker is changed, which will automatically be updated in the QAD Grid database. The Matlab built-in function “eval” is then used with the start string “c” as parameter to start the actual userdefined Matlab script. Binaries The QAD Grid worker concept offers another far more general approach to enable non-Grid applications in the case where no source code is available. Using a wrapper worker almost any binary application can use the power of distributed computing. To achieve this, a standard base worker is available that offers the following: � Authenticate at and integrate into the proteomics.net platform � Request new tasks (and respective parameters) for the configured purpose � Perform any system call or run any binary application � Process results and transmit them to the proteomics.net platform To give a concrete example listing 6.3 shows how we use a wrapper worker to start a binary (“tophat”, line 47-51), and process the results (copy to some destination, line 57). Listing 6.3: Core of wrapper worker to start tophat filter binary. 1 public s t a t i c void main ( S t r i n g [ ] a r g s ) throws Exception { 2 3 . . . 4 5 % i n i t i a l i z e o b j e c t 6 7 PROTEOMICSWORKER apply tophat 8 myPROTEOMICSWORKER apply tophat = 9 new PROTEOMICSWORKER apply tophat( 10 ”PROTEOMICSWORKER apply tophat . java ” , ” a p p l i c a t i o n t o p h a t ” , 11 ”” , false , ”” , ”” , a r g s [ 0 ] ) ; 12 13 % run main method 14 15 myPROTEOMICSWORKER apply tophat . do work ( ) ; 16 } 17 18 . . . 19 20 // −−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− 21 public void do work ( ) { 22 23 . . . 24 25 params = g e t n e x t t a s k ( ) ; 26 27 i f ( ( params != null ) && ( ! params . e q u a l s ( ( ”” ) ) ) ) { 28 t a sk h a n d l e d = true ;
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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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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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Page 157 and 158: Chapter 6 proteomics.net - Product-
Page 159 and 160: 6.2. CASE STUDIES 153 6.2 Case Stud
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Page 163 and 164: 6.2. CASE STUDIES 157 MASCOT and SE
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Page 173 and 174: Chapter 7 Related Work In this chap
Page 175 and 176: Chapter 8 Conclusion and Future Dir
Page 177 and 178: 8.3. FROM BIOMARKERS TO BIOPRINTS 1
Page 179 and 180: Appendix A Implementation Details T
Page 181 and 182: Appendix B Curriculum Vitae Name Ti
Page 183 and 184: References Aebersold, R. and Mann,
Page 185 and 186: REFERENCES 179 Breiman, L. (2001).
Page 187 and 188: REFERENCES 181 Downard, K. M. and M
Page 189 and 190: REFERENCES 183 Gillette, M. A., Man
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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