Ingvar Eidhammer, Harald Barsnes, Geir Egil Eide, Lennart Martens

Computational and Statistical Methods for Protein Quantification by Mass Spectrometry (eBook, ePUB)

• Format: ePub

Produktbeschreibung

The definitive introduction to data analysis in quantitative proteomics This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistical approaches to pursue the planning, design and analysis of quantitative proteomics experiments. The author's carefully constructed approach allows readers to easily make the transition into the field of quantitative proteomics. Through detailed descriptions of wet-lab methods, computational approaches and statistical tools, this book covers the full scope of a quantitative experiment, allowing readers to acquire new knowledge as well as acting as a useful reference work for more advanced readers. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry: * Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs. * Is illustrated by a large number of figures and examples as well as numerous exercises. * Provides both clear and rigorous descriptions of methods and approaches. * Is thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work. * Features detailed discussions of both wet-lab approaches and statistical and computational methods. With clear and thorough descriptions of the various methods and approaches, this book is accessible to biologists, informaticians, and statisticians alike and is aimed at readers across the academic spectrum, from advanced undergraduate students to post doctorates entering the field.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 360
• Erscheinungstermin: 10. Dezember 2012
• Englisch
• ISBN-13: 9781118493779
• Artikelnr.: 37345244

Autorenporträt

Ingvar Eidhammer, Department of Informatics, University of Bergen, Norway Harald Barsnes, Department of Biomedicine, University of Bergen, Norway Geir Egil Eide, Centre for Clinical Research, Haukeland University,Norway Lennart Martens, Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Belgium

Inhaltsangabe

Preface xv Terminology xvii Acknowledgements xix 1 Introduction 1 1.1 The
composition of an organism 1 1.2 Homeostasis, physiology, and pathology 4
1.3 Protein synthesis 4 1.4 Site, sample, state, and environment 4 1.5
Abundance and expression - protein and proteome profiles 5 1.6 The
importance of exact specification of sites and states 6 1.7 Relative and
absolute quantification 8 1.8 In vivo and in vitro experiments 9 1.9 Goals
for quantitative protein experiments 10 1.10 Exercises 10 2 Correlations of
mRNA and protein abundances 12 2.1 Investigating the correlation 12 2.2
Codon bias 14 2.3 Main results from experiments 15 2.4 The ideal case for
mRNA-protein comparison 16 2.5 Exploring correlation across genes 17 2.6
Exploring correlation within one gene 18 2.7 Correlation across subsets 18
2.8 Comparing mRNA and protein abundances across genes from two situations
19 2.9 Exercises 20 2.10 Bibliographic notes 21 3 Protein level
quantification 22 3.1 Two-dimensional gels 22 3.2 Protein arrays 23 3.3
Western blotting 25 3.4 ELISA - Enzyme-Linked Immunosorbent Assay 26 3.5
Bibliographic notes 26 4 Mass spectrometry and protein identification 27
4.1 Mass spectrometry 27 4.2 Isotope composition of peptides 32 4.3
Presenting the intensities - the spectra 36 4.4 Peak intensity calculation
38 4.5 Peptide identification by MS/MS spectra 38 4.6 The protein inference
problem 42 4.7 False discovery rate for the identifications 44 4.8
Exercises 46 4.9 Bibliographic notes 47 5 Protein quantification by mass
spectrometry 48 5.1 Situations, protein, and peptide variants 48 5.2
Replicates 49 5.3 Run - experiment - project 50 5.4 Comparing
quantification approaches/methods 54 5.5 Classification of approaches for
quantification using LC-MS/MS 57 5.6 The peptide (occurrence) space 60 5.7
Ion chromatograms 62 5.8 From peptides to protein abundances 62 5.9 Protein
inference and protein abundance calculation 67 5.10 Peptide tables 70 5.11
Assumptions for relative quantification 70 5.12 Analysis for differentially
abundant proteins 71 5.13 Normalization of data 71 5.14 Exercises 72 5.15
Bibliographic notes 74 6 Statistical normalization 75 6.1 Some illustrative
examples 75 6.2 Non-normally distributed populations 76 6.3 Testing for
normality 78 6.4 Outliers 82 6.5 Variance inequality 90 6.6 Normalization
and logarithmic transformation 90 6.7 Exercises 94 6.8 Bibliographic notes
95 7 Experimental normalization 96 7.1 Sources of variation and level of
normalization 96 7.2 Spectral normalization 98 7.3 Normalization at the
peptide and protein level 103 7.4 Normalizing using sum, mean, and median
104 7.5 MA-plot for normalization 104 7.6 Local regression normalization -
LOWESS 106 7.7 Quantile normalization 107 7.8 Overfitting 108 7.9 Exercises
109 7.10 Bibliographic notes 109 8 Statistical analysis 110 8.1 Use of
replicates for statistical analysis 110 8.2 Using a set of proteins for
statistical analysis 111 8.3 Missing values 116 8.4 Prediction and
hypothesis testing 118 8.5 Statistical significance for multiple testing
121 8.6 Exercises 127 8.7 Bibliographic notes 128 9 Label based
quantification 129 9.1 Labeling techniques for label based quantification
129 9.2 Label requirements 130 9.3 Labels and labeling properties 130 9.4
Experimental requirements 132 9.5 Recognizing corresponding peptide
variants 133 9.6 Reference free vs. reference based 135 9.7 Labeling
considerations 136 9.8 Exercises 136 9.9 Bibliographic notes 137 10
Reporter based MS/MS quantification 138 10.1 Isobaric labels 138 10.2 iTRAQ
140 10.3 TMT - Tandem Mass Tag 145 10.4 Reporter based quantification runs
145 10.5 Identification and quantification 145 10.6 Peptide table 147 10.7
Reporter based quantification experiments 147 10.8 Exercises 152 10.9
Bibliographic notes 153 11 Fragment based MS/MS quantification 155 11.1 The
label masses 155 11.2 Identification 157 11.3 Peptide and protein
quantification 158 11.4 Exercises 158 11.5 Bibliographic notes 159 12 Label
based quantification by MS spectra 160 12.1 Different labeling techniques
160 12.2 Experimental setup 166 12.3 MaxQuant as a model 167 12.4 The
MaxQuant procedure 169 12.5 Exercises 183 12.6 Bibliographic notes 184 13
Label free quantification by MS spectra 185 13.1 An ideal case - two
protein samples 185 13.2 The real world 186 13.3 Experimental setup 187
13.4 Forms 187 13.5 The quantification process 188 13.6 Form detection 189
13.7 Pair-wise retention time correction 191 13.8 Approaches for form tuple
detection 193 13.9 Pair-wise alignment 193 13.10 Using a reference run for
alignment 196 13.11 Complete pair-wise alignment 197 13.12 Hierarchical
progressive alignment 197 13.13 Simultaneous iterative alignment 200 13.14
The end result and further analysis 202 13.15 Exercises 202 13.16
Bibliographic notes 204 14 Label free quantification by MS/MS spectra 205
14.1 Abundance measurements 205 14.2 Normalization 207 14.3 Proposed
methods 207 14.4 Methods for single abundance calculation 207 14.5 Methods
for relative abundance calculation 210 14.6 Comparing methods 212 14.7
Improving the reliability of spectral count quantification 213 14.8
Handling shared peptides 214 14.9 Statistical analysis 215 14.10 Exercises
215 14.11 Bibliographic notes 216 15 Targeted quantification - Selected
Reaction Monitoring 218 15.1 Selected Reaction Monitoring - the concept 218
15.2 A suitable instrument 219 15.3 The LC-MS/MS run 220 15.4 Label free
and label based quantification 224 15.5 Requirements for SRM transitions
227 15.6 Finding optimal transitions 229 15.7 Validating transitions 230
15.8 Assay development 232 15.9 Exercises 233 15.10 Bibliographic notes 234
16 Absolute quantification 235 16.1 Performing absolute quantification 235
16.2 Label based absolute quantification 236 16.3 Label free absolute
quantification 239 16.4 Exercises 242 16.5 Bibliographic notes 242 17
Quantification of post-translational modifications 244 17.1 PTM and mass
spectrometry 244 17.2 Modification degree 245 17.3 Absolute modification
degree 246 17.4 Relative modification degree 250 17.5 Discovery based
modification stoichiometry 251 17.6 Exercises 253 17.7 Bibliographic notes
253 18 Biomarkers 254 18.1 Evaluation of potential biomarkers 254 18.2
Evaluating threshold values for biomarkers 257 18.3 Exercises 258 18.4
Bibliographic notes 258 19 Standards and databases 259 19.1 Standard data
formats for (quantitative) proteomics 259 19.2 Databases for proteomics
data 262 19.3 Bibliographic notes 263 20 Appendix A: Statistics 264 20.1
Samples, populations, and statistics 264 20.2 Population parameter
estimation 265 20.3 Hypothesis testing 267 20.4 Performing the test - test
statistics and p-values 268 20.5 Comparing means of populations 271 20.6
Comparing variances 276 20.7 Percentiles and quantiles 278 20.8 Correlation
280 20.9 Regression analysis 287 20.10 Types of values and variables 290 21
Appendix B: Clustering and discriminant analysis 292 21.1 Clustering 292
21.2 Discriminant analysis 303 21.3 Bibliographic notes 312 Bibliography
313 Index 327