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E-grāmata: Computational and Statistical Methods for Protein Quantification by Mass Spectrometry

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(Centre for Clinical Research, Haukeland University, Norway), (Department of Biomedicine, University of Bergen, Norway), (European Bioinformatics Institute (EBI), Cambridge, UK), (University of Bergen, Norway)
  • Formāts: EPUB+DRM
  • Izdošanas datums: 10-Dec-2012
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118493779
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Computational and Statistical Methods for Protein Quantification by Mass SpectrometryPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 10-Dec-2012
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118493779
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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.

Recenzijas

Computational and Statistical Methods for Protein Quantification by Mass Spectrometry is a book that can be used by undergraduate students in both analytical chemistry and biochemistry, as well as by scientists who are familiar with the field. The book teaches the reader how to perform proteomic analysis by mass spectrometry and how to interpret the large amount of data collected.  (Analytical and Bioanalytical Chemistry, 10 January 2014)

Preface xv
Terminology xvii
Acknowledgements xix
1 Introduction
1(11)
1.1 The composition of an organism
1(3)
1.1.1 A simple model of an organism
1(2)
1.1.2 Composition of cells
3(1)
1.2 Homeostasis, physiology, and pathology
4(1)
1.3 Protein synthesis
4(1)
1.4 Site, sample, state, and environment
4(1)
1.5 Abundance and expression - protein and proteome profiles
5(1)
1.5.1 The protein dynamic range
6(1)
1.6 The importance of exact specification of sites and states
6(2)
1.6.1 Biological features
7(1)
1.6.2 Physiological and pathological features
7(1)
1.6.3 Input features
7(1)
1.6.4 External features
7(1)
1.6.5 Activity features
7(1)
1.6.6 The cell cycle
8(1)
1.7 Relative and absolute quantification
8(1)
1.7.1 Relative quantification
8(1)
1.7.2 Absolute quantification
9(1)
1.8 In vivo and in vitro experiments
9(1)
1.9 Goals for quantitative protein experiments
10(1)
1.10 Exercises
10(2)
2 Correlations of mRNA and protein abundances
12(10)
2.1 Investigating the correlation
12(2)
2.2 Codon bias
14(1)
2.3 Main results from experiments
15(1)
2.4 The ideal case for mRNA-protein comparison
16(1)
2.5 Exploring correlation across genes
17(1)
2.6 Exploring correlation within one gene
18(1)
2.7 Correlation across subsets
18(1)
2.8 Comparing mRNA and protein abundances across genes from two situations
19(1)
2.9 Exercises
20(1)
2.10 Bibliographic notes
21(1)
3 Protein level quantification
22(5)
3.1 Two-dimensional gels
22(1)
3.1.1 Comparing results from different experiments - DIGE
23(1)
3.2 Protein arrays
23(2)
3.2.1 Forward arrays
24(1)
3.2.2 Reverse arrays
25(1)
3.2.3 Detection of binding molecules
25(1)
3.2.4 Analysis of protein array readouts
25(1)
3.3 Western blotting
25(1)
3.4 ELISA - Enzyme-Linked Immunosorbent Assay
26(1)
3.5 Bibliographic notes
26(1)
4 Mass spectrometry and protein identification
27(21)
4.1 Mass spectrometry
27(5)
4.1.1 Peptide mass fingerprinting (PMF)
28(1)
4.1.2 MS/MS - tandem MS
29(1)
4.1.3 Mass spectrometers
29(3)
4.2 Isotope composition of peptides
32(4)
4.2.1 Predicting the isotope intensity distribution
34(1)
4.2.2 Estimating the charge
34(1)
4.2.3 Revealing isotope patterns
34(2)
4.3 Presenting the intensities - the spectra
36(2)
4.4 Peak intensity calculation
38(1)
4.5 Peptide identification by MS/MS spectra
38(4)
4.5.1 Spectral comparison
41(1)
4.5.2 Sequential comparison
41(1)
4.5.3 Scoring
42(1)
4.5.4 Statistical significance
42(1)
4.6 The protein inference problem
42(2)
4.6.1 Determining maximal explanatory sets
44(1)
4.6.2 Determining minimal explanatory sets
44(1)
4.7 False discovery rate for the identifications
44(2)
4.7.1 Constructing the decoy database
45(1)
4.7.2 Separate or composite search
46(1)
4.8 Exercises
46(1)
4.9 Bibliographic notes
47(1)
5 Protein quantification by mass spectrometry
48(27)
5.1 Situations, protein, and peptide variants
48(1)
5.1.1 Situation
48(1)
5.1.2 Protein variants - peptide variants
48(1)
5.2 Replicates
49(1)
5.3 Run - experiment - project
50(4)
5.3.1 LC-MS/MS run
50(1)
5.3.2 Quantification run
51(1)
5.3.3 Quantification experiment
52(1)
5.3.4 Quantification project
52(1)
5.3.5 Planning quantification experiments
52(2)
5.4 Comparing quantification approaches/methods
54(3)
5.4.1 Accuracy
54(1)
5.4.2 Precision
55(1)
5.4.3 Repeatability and reproducibility
56(1)
5.4.4 Dynamic range and linear dynamic range
56(1)
5.4.5 Limit of blank - LOB
56(1)
5.4.6 Limit of detection - LOD
57(1)
5.4.7 Limit of quantification - LOQ
57(1)
5.4.8 Sensitivity
57(1)
5.4.9 Selectivity
57(1)
5.5 Classification of approaches for quantification using LC-MS/MS
57(3)
5.5.1 Discovery or targeted protein quantification
58(1)
5.5.2 Label based vs. label free quantification
59(1)
5.5.3 Abundance determination - ion current vs. peptide identification
60(1)
5.5.4 Classification
60(1)
5.6 The peptide (occurrence) space
60(2)
5.7 Ion chromatograms
62(1)
5.8 From peptides to protein abundances
62(5)
5.8.1 Combined single abundance from single abundances
64(1)
5.8.2 Relative abundance from single abundances
65(1)
5.8.3 Combined relative abundance from relative abundances
66(1)
5.9 Protein inference and protein abundance calculation
67(3)
5.9.1 Use of the peptides in protein abundance calculation
67(1)
5.9.2 Classifying the proteins
68(1)
5.9.3 Can shared peptides be used for quantification?
68(2)
5.10 Peptide tables
70(1)
5.11 Assumptions for relative quantification
70(1)
5.12 Analysis for differentially abundant proteins
71(1)
5.13 Normalization of data
71(1)
5.14 Exercises
72(2)
5.15 Bibliographic notes
74(1)
6 Statistical normalization
75(21)
6.1 Some illustrative examples
75(1)
6.2 Non-normally distributed populations
76(2)
6.2.1 Skewed distributions
76(1)
6.2.2 Measures of skewness
76(1)
6.2.3 Steepness of the peak - kurtosis
77(1)
6.3 Testing for normality
78(4)
6.3.1 Normal probability plot
79(2)
6.3.2 Some test statistics for normality testing
81(1)
6.4 Outliers
82(8)
6.4.1 Test statistics for the identification of a single outlier
83(3)
6.4.2 Testing for more than one outlier
86(2)
6.4.3 Robust statistics for mean and standard deviation
88(1)
6.4.4 Outliers in regression
89(1)
6.5 Variance inequality
90(1)
6.6 Normalization and logarithmic transformation
90(4)
6.6.1 The logarithmic function
90(1)
6.6.2 Choosing the base
91(1)
6.6.3 Logarithmic normalization of peptide/protein ratios
91(1)
6.6.4 Pitfalls of logarithmic transformations
92(1)
6.6.5 Variance stabilization by logarithmic transformation
92(1)
6.6.6 Logarithmic scale for presentation
93(1)
6.7 Exercises
94(1)
6.8 Bibliographic notes
95(1)
7 Experimental normalization
96(14)
7.1 Sources of variation and level of normalization
96(2)
7.2 Spectral normalization
98(5)
7.2.1 Scale based normalization
99(2)
7.2.2 Rank based normalization
101(1)
7.2.3 Combining scale based and rank based normalization
101(1)
7.2.4 Reproducibility of the normalization methods
102(1)
7.3 Normalization at the peptide and protein level
103(1)
7.4 Normalizing using sum, mean, and median
104(1)
7.5 MA-plot for normalization
104(2)
7.5.1 Global intensity normalization
105(1)
7.5.2 Linear regression normalization
106(1)
7.6 Local regression normalization - LOWESS
106(1)
7.7 Quantile normalization
107(1)
7.8 Overfitting
108(1)
7.9 Exercises
109(1)
7.10 Bibliographic notes
109(1)
8 Statistical analysis
110(19)
8.1 Use of replicates for statistical analysis
110(1)
8.2 Using a set of proteins for statistical analysis
111(5)
8.2.1 Z-variable
111(1)
8.2.2 G-statistic
112(3)
8.2.3 Fisher-Irwin exact test
115(1)
8.3 Missing values
116(2)
8.3.1 Reasons for missing values
116(2)
8.3.2 Handling missing values
118(1)
8.4 Prediction and hypothesis testing
118(3)
8.4.1 Prediction errors
119(1)
8.4.2 Hypothesis testing
120(1)
8.5 Statistical significance for multiple testing
121(6)
8.5.1 False positive rate control
122(1)
8.5.2 False discovery rate control
123(4)
8.6 Exercises
127(1)
8.7 Bibliographic notes
128(1)
9 Label based quantification
129(9)
9.1 Labeling techniques for label based quantification
129(1)
9.2 Label requirements
130(1)
9.3 Labels and labeling properties
130(2)
9.3.1 Quantification level
130(1)
9.3.2 Label incorporation
131(1)
9.3.3 Incorporation level
131(1)
9.3.4 Number of compared samples
132(1)
9.3.5 Common labels
132(1)
9.4 Experimental requirements
132(1)
9.5 Recognizing corresponding peptide variants
133(2)
9.5.1 Recognizing peptide variants in MS spectra
133(1)
9.5.2 Recognizing peptide variants in MS/MS spectra
134(1)
9.6 Reference free vs. reference based
135(1)
9.6.1 Reference free quantification
135(1)
9.6.2 Reference based quantification
135(1)
9.7 Labeling considerations
136(1)
9.8 Exercises
136(1)
9.9 Bibliographic notes
137(1)
10 Reporter based MS/MS quantification
138(17)
10.1 Isobaric labels
138(2)
10.2 iTRAQ
140(5)
10.2.1 Fragmentation
141(2)
10.2.2 Reporter ion intensities
143(1)
10.2.3 iTRAQ 8-plex
144(1)
10.3 TMT - Tandem Mass Tag
145(1)
10.4 Reporter based quantification runs
145(1)
10.5 Identification and quantification
145(2)
10.6 Peptide table
147(1)
10.7 Reporter based quantification experiments
147(5)
10.7.1 Normalization across LC-MS/MS runs - use of a reference sample
147(2)
10.7.2 Normalizing within an LC-MS/MS run
149(1)
10.7.3 From reporter intensities to protein abundances
149(1)
10.7.4 Finding differentially abundant proteins
150(1)
10.7.5 Distributing the replicates on the quantification runs
151(1)
10.7.6 Protocols
152(1)
10.8 Exercises
152(1)
10.9 Bibliographic notes
153(2)
11 Fragment based MS/MS quantification
155(5)
11.1 The label masses
155(2)
11.2 Identification
157(1)
11.3 Peptide and protein quantification
158(1)
11.4 Exercises
158(1)
11.5 Bibliographic notes
159(1)
12 Label based quantification by MS spectra
160(25)
12.1 Different labeling techniques
160(6)
12.1.1 Metabolic labeling - SILAC
160(2)
12.1.2 Chemical labeling
162(3)
12.1.3 Enzymatic labeling - 180
165(1)
12.2 Experimental setup
166(1)
12.3 MaxQuant as a model
167(2)
12.3.1 HL-pairs
167(2)
12.3.2 Reliability of HL-pairs
169(1)
12.3.3 Reliable protein results
169(1)
12.4 The MaxQuant procedure
169(14)
12.4.1 Recognize HL-pairs
169(7)
12.4.2 Estimate HL-ratios
176(1)
12.4.3 Identify HL-pairs by database search
177(4)
12.4.4 Infer protein data
181(2)
12.5 Exercises
183(1)
12.6 Bibliographic notes
184(1)
13 Label free quantification by MS spectra
185(20)
13.1 An ideal case - two protein samples
185(1)
13.2 The real world
186(1)
13.2.1 Multiple samples
187(1)
13.3 Experimental setup
187(1)
13.4 Forms
187(1)
13.5 The quantification process
188(1)
13.6 Form detection
189(2)
13.7 Pair-wise retention time correction
191(2)
13.7.1 Determining potentially corresponding forms
191(1)
13.7.2 Linear corrections
192(1)
13.7.3 Nonlinear corrections
192(1)
13.8 Approaches for form tuple detection
193(1)
13.9 Pair-wise alignment
193(3)
13.9.1 Distance between forms
194(1)
13.9.2 Finding an optimal alignment
195(1)
13.10 Using a reference run for alignment
196(1)
13.11 Complete pair-wise alignment
197(1)
13.12 Hierarchical progressive alignment
197(3)
13.12.1 Measuring the similarity or the distance of two runs
198(1)
13.12.2 Constructing static guide trees
198(1)
13.12.3 Constructing dynamic guide trees
199(1)
13.12.4 Aligning subalignments
199(1)
13.12.5 SuperHim
199(1)
13.13 Simultaneous iterative alignment
200(2)
13.13.1 Constructing the initial alignment in XCMS
200(1)
13.13.2 Changing the initial alignment
201(1)
13.14 The end result and further analysis
202(1)
13.15 Exercises
202(2)
13.16 Bibliographic notes
204(1)
14 Label free quantification by MS/MS spectra
205(13)
14.1 Abundance measurements
205(2)
14.2 Normalization
207(1)
14.3 Proposed methods
207(1)
14.4 Methods for single abundance calculation
207(3)
14.4.1 emPAI
208(1)
14.4.2 PMSS
208(1)
14.4.3 NSAF
209(1)
14.4.4 SI
209(1)
14.5 Methods for relative abundance calculation
210(2)
14.5.1 PASC
210(1)
14.5.2 RIBAR
210(1)
14.5.3 xRIBAR
211(1)
14.6 Comparing methods
212(1)
14.6.1 An analysis by Griffin
212(1)
14.6.2 An analysis by Colaert
213(1)
14.7 Improving the reliability of spectral count quantification
213(1)
14.8 Handling shared peptides
214(1)
14.9 Statistical analysis
215(1)
14.10 Exercises
215(1)
14.11 Bibliographic notes
216(2)
15 Targeted quantification - Selected Reaction Monitoring
218(17)
15.1 Selected Reaction Monitoring - the concept
218(1)
15.2 A suitable instrument
219(1)
15.3 The LC-MS/MS run
220(4)
15.3.1 Sensitivity and accuracy
222(2)
15.4 Label free and label based quantification
224(3)
15.4.1 Label free SRM based quantification
224(1)
15.4.2 Label based SRM based quantification
225(2)
15.5 Requirements for SRM transitions
227(2)
15.5.1 Requirements for the peptides
227(1)
15.5.2 Requirements for the fragmentions
228(1)
15.6 Finding optimal transitions
229(1)
15.7 Validating transitions
230(2)
15.7.1 Testing linearity
230(1)
15.7.2 Determining retention time
231(1)
15.7.3 Limit of detection/quantification
231(1)
15.7.4 Dealing with low abundant proteins
231(1)
15.7.5 Checking for interference
232(1)
15.8 Assay development
232(1)
15.9 Exercises
233(1)
15.10 Bibliographic notes
234(1)
16 Absolute quantification
235(9)
16.1 Performing absolute quantification
235(1)
16.1.1 Linear dependency between the calculated and the real abundances
236(1)
16.2 Label based absolute quantification
236(3)
16.2.1 Stable isotope-labeled peptide standards
237(1)
16.2.2 Stable isotope-labeled concatenated peptide standards
238(1)
16.2.3 Stable isotope-labeled intact protein standards
239(1)
16.3 Label free absolute quantification
239(3)
16.3.1 Quantification by MS spectra
239(2)
16.3.2 Quantification by the number of MS/MS spectra
241(1)
16.4 Exercises
242(1)
16.5 Bibliographic notes
242(2)
17 Quantification of post-translational modifications
244(10)
17.1 PTM and mass spectrometry
244(1)
17.2 Modification degree
245(1)
17.3 Absolute modification degree
246(4)
17.3.1 Reversing the modification
246(2)
17.3.2 Use of two standards
248(1)
17.3.3 Label free modification degree analysis
249(1)
17.4 Relative modification degree
250(1)
17.5 Discovery based modification stoichiometry
251(2)
17.5.1 Separate LC-MS/MS experiments for modified and unmodified peptides
251(1)
17.5.2 Common LC-MS/MS experiment for modified and unmodified peptides
252(1)
17.5.3 Reliable results and significant differences
252(1)
17.6 Exercises
253(1)
17.7 Bibliographic notes
253(1)
18 Biomarkers
254(5)
18.1 Evaluation of potential biomarkers
254(3)
18.1.1 Taking disease prevalence into account
255(2)
18.2 Evaluating threshold values for biomarkers
257(1)
18.3 Exercises
258(1)
18.4 Bibliographic notes
258(1)
19 Standards and databases
259(5)
19.1 Standard data formats for (quantitative) proteomics
259(3)
19.1.1 Controlled vocabularies (CVs)
260(1)
19.1.2 Benefits of using CV terms to annotate metadata
260(1)
19.1.3 A standard for quantitative proteomics data
261(1)
19.1.4 HUPO PSI
262(1)
19.2 Databases for proteomics data
262(1)
19.3 Bibliographic notes
263(1)
20 Appendix A: Statistics
264(28)
20.1 Samples, populations, and statistics
264(1)
20.2 Population parameter estimation
265(2)
20.2.1 Estimating the mean of a population
266(1)
20.3 Hypothesis testing
267(1)
20.3.1 Two types of errors
268(1)
20.4 Performing the test - test statistics and p-values
268(3)
20.4.1 Parametric test statistics
269(1)
20.4.2 Nonparametric test statistics
269(1)
20.4.3 Confidence intervals and hypothesis testing
270(1)
20.5 Comparing means of populations
271(5)
20.5.1 Analyzing the mean of a single population
271(1)
20.5.2 Comparing the means from two populations
272(3)
20.5.3 Comparing means of paired populations
275(1)
20.5.4 Multiple populations
275(1)
20.5.5 Multiple testing
276(1)
20.6 Comparing variances
276(2)
20.6.1 Testing the variance of a single population
276(1)
20.6.2 Testing the variances of two populations
277(1)
20.7 Percentiles and quantiles
278(2)
20.7.1 A straightforward method for estimating the percentiles
279(1)
20.7.2 Quantiles
279(1)
20.7.3 Box plots
280(1)
20.8 Correlation
280(7)
20.8.1 Pearson's product-moment correlation coefficient
283(2)
20.8.2 Spearman's rank correlation coefficient
285(1)
20.8.3 Correlation line
286(1)
20.9 Regression analysis
287(3)
20.9.1 Regression line
288(1)
20.9.2 Relation between Pearson's correlation coefficient and the regression parameters
289(1)
20.10 Types of values and variables
290(2)
21 Appendix B: Clustering and discriminant analysis
292(21)
21.1 Clustering
292(11)
21.1.1 Distances and similarities
293(1)
21.1.2 Distance measures
294(1)
21.1.3 Similarity measures
295(1)
21.1.4 Distances between an object and a class
295(1)
21.1.5 Distances between two classes
296(1)
21.1.6 Missing data
297(1)
21.1.7 Clustering approaches
297(1)
21.1.8 Sequential clustering
298(2)
21.1.9 Hierarchical clustering
300(3)
21.2 Discriminant analysis
303(9)
21.2.1 Step-wise feature selection
304(3)
21.2.2 Linear discriminant analysis using original features
307(2)
21.2.3 Canonical discriminant analysis
309(3)
21.3 Bibliographic notes
312(1)
Bibliography 313(14)
Index 327
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
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