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THÔNG TIN TÀI LIỆU
Cấu trúc
Cover
Frontmatter
Preface
Contents
Contributors
Part I:
Data Generation and Result Finding
Chapter 1: Instruments and Methods in Proteomics
1. Introduction
2. Gel-Based Protein Separation Techniques and Applications
2.1. One-Dimensional Protein Separation: 1D-PAGE
2.2. Two-Dimensional Protein Separation: 2D-PAGE
2.2.1. 2D-DIGE: A Sophisticated Application
3. Mass Spectrometry-Based Techniques and Applications
3.1. Setup of a Mass Spectrometer
3.1.1. Liquid Chromatography Techniques for Proteome Analysis
3.1.2. Ionization Methods
3.1.3. Types of Mass Analyzers and Hybrid Mass Spectrometers
3.2. Identification of Proteins by Mass Spectrometry: Scanning Methods and Fragmentation Types
3.3. MS-Data Interpretation
4. Quantitative Mass Spectrometry
4.1. Relative Quantification
4.1.1. Isotope Labeling
4.1.1.1. Chemical Labeling
4.1.1.2. Metabolic Labeling
4.1.2. Label-Free Quantification
4.2. Absolute Quantification
5. Summary
6. Notes
References
Chapter 2: In-Depth Protein Characterization by Mass Spectrometry
1. Introduction
2. Methods
2.1. Sample Preparation
2.1.1. Enabling Mass Spectrometric Analysis
2.1.2. Minimizing Risk of Primary Structure Change
2.2. Primary Structure Elucidation by Mass Spectrometry
2.3. Signal Extraction
2.4. Peptide Fragmentation Fingerprinting
2.5. Second Round Searches
2.6. De Novo Sequencing
2.7. Combination of Results (see Fig. 2)
2.8. Differential EIC
3. Conclusions
4. Notes
References
Chapter 3: Analysis of Phosphoproteomics Data
1. Introduction
2. Methods
2.1. Sample Preparation and Detection of Phosphorylations
2.2. Raw Data Processing
2.3. Downstream Analysis
2.3.1. Identification of Differential Phosphorylations
2.3.2. Enrichment Analysis
2.3.3. Multiple Conditions
3. Case Study: Mode of Action Analysis for Sorafenib
3.1 Raw Data Processing
3.2. Differentially Phosphorylated Sites
3.3. Pathway Mapping
4. Conclusion
5. Notes
References
Part II:
Databases
Chapter 4: The Origin and Early Reception of Sequence Databases
1. Introduction
2. The First Molecular Databases
3. The Problems of Establishing a Disciplinary Identity
4. The Challenges of Using Computers in Molecular Evolution
5. Conclusions
6. Notes
References
Chapter 5: Laboratory Data and Sample Management for Proteomics
1. Introduction
2. Materials
3. Methods
3.1. 2D-Gel Electrophoresis Case Study
3.1.1. Laboratory Work
3.1.2. ProSE Work
3.2. Quantitative LC-MS with Isobaric Labels Case Study
3.2.1. Laboratory Work
3.2.2. ProSE Work
4. Notes
References
Chapter 6: PRIDE and “Database on Demand” as Valuable Tools for Computational Proteomics
1. Introduction
2. Materials
2.1. Database on Demand
2.2. PRIDE BioMart Interface
3. Methods
3.1. Database on Demand
3.2. Cross-Resource BioMart Queries in the BioMart Central Portal
4. Notes
References
Chapter 7: Analysing Proteomics Identifications in the Context of Functional and Structural Protein Annotation: Integrating Annotation Using PICR, DAS, and BioMart
1. Introduction
2. Solving the Protein Identifier Problem – Just Too Many Databases?
3. Collating Annotation from Multiple Sources – DAS
4. The DAS Registry
5. Dasty2: A Powerful Web-Based Client
6. Retrieving Sequence Annotation Using BioMart
7. Notes
References
Chapter 8: Tranche Distributed Repository and ProteomeCommons.org
1. Introduction
2. Methods
3. Notes
References
Part III:
Standards
Chapter 9: Data Standardization by the HUPO-PSI: How has the Community Benefitted?
1. Introduction
2. What Constitutes a Community Standard?
3. Mass Spectrometry and Data Standardization
4. Proteomics Informatics
5. Protein Separations
6. Molecular Interactions
7. MIAPE and the Proteomics Journals
8. Summary
9. Notes
References
Chapter 10: mzIdentML: An Open Community-Built Standard Format for the Results of Proteomics Spectrum Identification Algorithms
1. Introduction
2. mzIdentML: A Standard Format for Proteomics Results
2.1. Design Principles and Use Cases
2.2. Ideas and Concepts of the mzIdentML Schema (Release 1.0.0)
2.3. The PSI–MS Controlled Vocabulary
2.4. Semantic Validation and Mapping Files
2.5. Conformance to MIAPE and Journal Guidelines
2.6. mzIdentML Examples
3. Examples
3.1. Example 1: Multiple Search Engines, Combination of Peptides, and Decoy Approach
3.2. Example 2: 14N/15N
3.3. Example 3: Fragmentation Information
4. Outlook
5. Notes
References
Chapter 11: Spectra, Chromatograms, Metadata: mzML-The Standard Data Format for Mass Spectrometer Output
1. Introduction
1.1. Motivation for a New File Format
1.2. History of mzML
1.3. The Creation of PSI Standards
2. Design of mzML
3. The PSI-MS Controlled Vocabulary
4. The mzML Schema (Release 1.1.0)
4.1. The XML Backbone in mzML
4.2. Parameter- and List-Elements: mzML’s Key Components
4.2.1. The Parameter Elements
4.2.2. The List Elements
4.3. Top Level Elements
4.3.1. The <mzML>Element
4.4. Indexing in mzML
5. Semantic Validation
6. Notes
References
Chapter 12: imzML: Imaging Mass Spectrometry Markup Language: A Common Data Format for Mass Spectrometry Imaging
1. Introduction
2. imzML Data Format
2.1. Data Structure
2.1.1. XML
2.1.2. Controlled Vocabulary
2.1.2.1. Image Orientation
2.1.2.2. Scan Pattern
2.1.3. Binary Data File
3. imzML File Properties
4. Implementation
4.1. Displaying Tools
4.2. Converters
5. Notes
References
Chapter 13: Tandem Mass Spectrometry Spectral Libraries and Library Searching
1. Introduction
2. Spectral Library Searching
2.1. Performance
2.2. Software
3. Spectral Libraries
3.1. Availability
3.2. Formats
3.3. Creating Private or Specialized Spectrum Libraries
4. Conclusion
References
Part IV:
Processing and Interpretation of Data
Chapter 14: Inter-Lab Proteomics: Data Mining in Collaborative Projects on the Basis of the HUPO Brain Proteome Project’s Pilot Studies
1. Introduction
2. Materials and Methods
2.1. Methods
2.2. Data Collection Center and Bioinformatics
3. Results
3.1. Centralised Analysis Strategy
3.2. Single Compared to Centralised Approaches
3.3. Technology Platforms
3.4. Data Mining
4. Discussion
5. Outlook
6. Notes
References
Chapter 15: Data Management and Data Integration in the HUPO Plasma Proteome Project
1. Introduction
2. Methods
2.1. Creating a Data Repository
2.2. PPP Reference Specimens
2.3. Inference from Peptides to Proteins
2.4. HPPP Data Integration Workflow Algorithm from Adamski et al. (3)
2.5. Summary of Collaborative Data
2.6. False-Positive Identifications
2.7. Correlating Immunoassay Quantitation of Proteins with Estimates of Abundance Based on Number of Peptides
2.8. Comparisons of Protein Identifications Across Different Studies
2.9. The Next Phase, now Current Phase, of the HUPO HPPP
3. Notes
References
Chapter 16: Statistics in Experimental Design, Preprocessing, and Analysis of Proteomics Data
1. Introduction
2. Designs and Planning of Experiments
2.1. One Experimental Factor with Two Categories
2.2. One Experimental Factor with More than Two Categories
2.3. Two or More Experimental Factors
2.4. Repeated Measures Designs
2.5. Randomization
2.6. Sample Size Calculations
3. Data Preprocessing
3.1. Variance Stabilization
3.2. Normalization
3.3. Standardization
3.4. Missing Values Imputation
4. Statistical Analysis
4.1. Statistical Hypothesis Testing
4.2. Comparing Two Groups
4.3. Multiple Hypothesis Testing
4.4. Analysis of Variance or Covariance
4.5. Fold Change and Confidence Intervals
5. Notes
5.1. Randomization
5.2. Sample Size Calculations
5.3. Quantile Normalization
5.4. Missing Values Imputation
5.5. Adjusting of p-Values
References
Chapter 17: The Evolution of Protein Interaction Networks
1. Introduction
2. Basic Tenets of Protein Interactions and the Structure of Protein Interaction Networks
2.1. Principles of Protein–Protein Interactions
2.2. Permanent and Transient Protein Interactions
2.3. Protein Domains as Binding Interfaces
3. The Structure of Protein Interaction Networks
3.1. Protein Interaction Data
3.2. Structural Properties of Protein Interaction Networks
4. Evolution of Protein Interaction Networks
4.1. Gene Duplications and the Evolutionary Conservation of Protein Interactions
4.2. Origin of Novel Binding Interfaces in Proteins
4.3. Conserved Structures in Protein Interaction Networks
5. Conclusions
6. Notes
References
Chapter 18: Cytoscape: Software for Visualization and Analysis of Biological Networks
1. Introduction
2. Basic Requirements for Computer-Aided Visualization of Biological Pathways
3. Data Visualization Software
3.1. Cytoscape
3.1.1. Description of the Basic Cytoscape Features (Network Establishment, Annotation, Analysis, and Visualization)
3.1.2. Advanced Features of the Cytoscape Software
3.1.2.1. Text Mining
3.1.2.2. Identifying Network Modules and Complexes
3.1.2.3. Using Cytoscape for Performing Advanced Bioinformatic Analysis with Respect to Molecular Interaction Networks
3.2. Special Features of Other Data Visualization Software
4. Notes
References
Chapter 19: Text Mining for Systems Modeling
1. Introduction
2. Specific Tasks Within Text Mining, Their Problems, and Limitations
2.1. Format Conversion
2.2. Identification of Word and Sentence Boundaries
2.3. Part-of-Speech Tagging
2.4. Named Entity Recognition and Word Sense Disambiguation
2.5. Identification of Relationships Between Entities
3. Biomedical Ontologies `and Text Mining
4. Examples of General Text Mining Systems in the Biomedical Domain
5. Measuring Success
6. An Example of Text Mining for Systems Biology
6.1. Document Representation
6.2. Feature Ranking and Dimensionality Reduction
6.3. Classification Performance and Feature Number
6.4. Classification Performance with 5,000 Features
7. Conclusions
References
Chapter 20: Identification of Alternatively Spliced Transcripts Using a Proteomic Informatics Approach
1. Introduction
2. Methods
2.1. Database of Translated Alternatively Spliced Sequences: The Modified ECgene Database
2.2. Searching Mass Spectral Data Against Alternative Splice Database
2.3. Postsearch Analyses
2.4. Michigan Peptide to Protein Integration
2.5. Sequence Analyses
2.6. Validation of Novel Peptides
2.7. Differential Expression of Alternative Splice Variants
2.8. Annotation of Novel Peptides
2.9. Alternative Splice Variant Analysis of a Pancreatic Tumor Dataset
3. Notes
4. Conclusions
References
Chapter 21: Distributions of Ion Series in ETD and CID Spectra: Making a Comparison
1. Introduction
2. Materials and Methods
2.1. Preparation of Proteolytic Digests for Mass Spectrometry
2.2. Tandem Mass Spectrometry
2.3. Data Processing and Database Searching
2.4. Estimation of False Discovery Rates and Generation of Product Ion Frequency Diagrams
3. Discussion of Results
4. Notes
References
Part V:
Tools
Chapter 22: Evaluation of Peak-Picking Algorithms for Protein Mass Spectrometry
1. Introduction
1.1. Data Set
2. Peak Picking
2.1. Algorithms
2.2. Reference Peaks
2.3. Comparing Peak-Picking Algorithms
2.4. Evaluating Peak-Picking Algorithms
2.4.1. Stability
3. Discussion
4. Notes
References
Chapter 23: OpenMS and TOPP: Open Source Software for LC-MS Data Analysis
1. Introduction
2. Materials
2.1. The OpenMS Framework
2.2. TOPP: The OpenMS Proteomics Pipeline
3. Methods
3.1. TOPP Workflows
3.2. Example: Peptide Identification Pipeline
3.3. Example: Quantitation Pipeline
4. Conclusions
5. Notes
References
Chapter 24: LC/MS Data Processing for Label-Free Quantitative Analysis
1. Introduction
2. Materials
2.1. Software and Hardware
2.2. Data Format
3. Methods
3.1. Displaying LC/MS Images with MSight
3.2. Adapting LC/MS Image Analysis with MSight
3.3. Comparing Data with MSight
3.4. Processing LC/MS Data with SuperHirn
3.5. Clustering Profiles with SuperHirn
3.6. Combining MSight and SuperHirn
4. Notes
References
Part VI:
Modelling and Systems Biology
Chapter 25: Spectral Properties of Correlation Matrices – Towards Enhanced Spectral Clustering
1. Introduction
2. Scenario 1: Correlated Noise with Many Variables and Many Measurements per Variable
2.1. One Correlated Cluster
2.2. Two Correlated Clusters
3. Improved Spectral Clustering
4. Scenario 2: Uncorrelated Noise with More Variables than Measurements per Variable
5. Scenario 3: Correlated Noise with More Variables than Measurements per Variable
6. Genetic Profile Scenario of Micro-array Data on Differential Expressions
7. Summary and Conclusion
8. Notes
References
Chapter 26: Standards, Databases, and Modeling Tools in Systems Biology
1. Introduction
2. Requirements for the Exchange of Quantitative Biological Models
2.1. CellML and SBML: Standard Formats for the Annotation of Biological Models
2.1.1. The Systems Biology Markup Language (SBML)
2.1.2. CellML
2.1.3. Comparison of SBML and CellML (see Notes 2 and 3)
2.2. Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) and the Systems Biology Ontology (SBO)
2.2.1. MIRIAM
2.2.2. Systems Biology Ontology (SBO)
2.3. Systems Biology-Related Databases
3. Software Packages for Systems Biology
3.1. Basic Requirements for Modeling Biochemical Networks
3.2. Comparison of the Reviewed Modeling Tools
4. Notes
References
Chapter 27: Modeling of Cellular Processes: Methods, Data, and Requirements
1. Systems Biology: System-Theoretic Studies of Cellular Dynamics
2. Why Is Modeling Necessary and How Will Experimentalists Profit from It?
3. Conceptual Models: The Relation Between Cellular Components and Cellular Processes
4. Requirements on Data: Quantitative, Complete, and Significant Information is Crucial for Systems Biology
5. Mathematical Modeling: Approaches for Simulation and Analysis of Cellular Systems
5.1. Kinetic Equations: The Conventional Approach Using Differential Equations
5.2. Stochastic Approaches: Fluctuations in Cellular Systems
5.3. Molecular Dynamics: The View Through the Computational Microscope