Computational and Statistical Methods for Protein Quantification by Mass Spectrometry (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
354
Utgivningsdatum
2013-01-04
Upplaga
2
Förlag
Wiley-Blackwell
Illustrationer
Illustrations
Dimensioner
234 x 152 x 19 mm
Vikt
589 g
Antal komponenter
1
Komponenter
14:B&W 6 x 9 in or 229 x 152 mm Case Laminate on White w/Gloss Lam
ISBN
9781119964001
Computational and Statistical Methods for Protein Quantification by Mass Spectrometry (inbunden)

Computational and Statistical Methods for Protein Quantification by Mass Spectrometry

Inbunden Engelska, 2013-01-04
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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.
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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)

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Övrig information

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

Innehållsförteckning

Preface xv Terminology xvii Acknowledgements xix 1 Introduction 1 1.1 The composition of an organism 1 1.1.1 A simple model of an organism 1 1.1.2 Composition of cells 3 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 1.5.1 The protein dynamic range 6 1.6 The importance of exact specification of sites and states 6 1.6.1 Biological features 7 1.6.2 Physiological and pathological features 7 1.6.3 Input features 7 1.6.4 External features 7 1.6.5 Activity features 7 1.6.6 The cell cycle 8 1.7 Relative and absolute quantification 8 1.7.1 Relative quantification 8 1.7.2 Absolute quantification 9 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 3.1.1 Comparing results from different experiments DIGE 23 3.2 Protein arrays 23 3.2.1 Forward arrays 24 3.2.2 Reverse arrays 25 3.2.3 Detection of binding molecules 25 3.2.4 Analysis of protein array readouts 25 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 4.1.1 Peptide mass fingerprinting (PMF) 28 4.1.2 MS/MS tandem MS 29 4.1.3 Mass spectrometers 29 4.2 Isotope composition of peptides 32 4.2.1 Predicting the isotope intensity distribution 34 4.2.2 Estimating the charge 34 4.2.3 Revealing isotope patterns 34 4.3 Presenting the intensities the spectra 36 4.4 Peak intensity calculation 38 4.5 Peptide identification by MS/MS spectra 38 4.5.1 Spectral comparison 41 4.5.2 Sequential comparison 41 4.5.3 Scoring 42 4.5.4 Statistical significance 42 4.6 The protein inference problem 42 4.6.1 Determining maximal explanatory sets 44 4.6.2 Determining minimal explanatory sets 44 4.7 False discovery rate for the identifications 44 4.7.1 Constructing the decoy database 45 4.7.2 Separate or composite search 46 4.8 Exercises 46 4.9 Bibliographic notes 47 5 Protein quantification by mass spectrometry 48 5.1 Situations, protein, and peptide variants 48 5.1.1 Situation 48 5.1.2 Protein variants peptide variants 48 5.2 Replicates 49 5.3 Run experiment project 50 5.3.1 LC-MS/MS run 50 5.3.2 Quantification run 51 5.3.3 Quantification experiment 52 5.3.4 Quantification project 52 5.3.5 Planning quantification experiments 52 5.4 Comparing quantification approaches/methods 54 5.4.1 Accuracy 54 5.4.2 Precision 55 5.4.3 Repeatability and reproducibility 56 5.4.4 Dynamic range and linear dynamic range 56 5.4.5 Limit of blank LOB 56 5.4.6 Limit of detection LOD 57 5.4.7 Limit of quantification LOQ 57 5.4.8 Sensitivity 57 5.4.9 Selectivity 57 5.5 Classification of approaches for quantification using LC-MS/MS 57 5.5.1 Discovery or targeted protein quantification 58 5.5.2 Label based vs. label free quantification 59 5.5.3 Abundance determination ion current vs. peptide identification 60 5.5.4 Classification 60 5.6 The peptide (occurrence) space 60 5.7 Ion chromatograms 62 5.8 From peptides to pr