Keywords:
Tandem Mass Spectrometry - methods.
;
Electronic books.
Description / Table of Contents:
Bringing together world experts to provide clear explanations of the key algorithms, workflows and analysis frameworks, Proteome Informatics will provide a detailed introduction to the main informatics topics that underpin the various LC-MS/MS protocols used for protein identification and quantitation.
Type of Medium:
Online Resource
Pages:
1 online resource (429 pages)
Edition:
1st ed.
ISBN:
9781782626732
Series Statement:
Issn Series
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=4771326
DDC:
572.6
Language:
English
Note:
Cover -- Proteome Informatics -- Acknowledgements -- Contents -- Chapter 1 - Introduction to Proteome Informatics -- 1.1 Introduction -- 1.2 Principles of LC-MS/MS Proteomics -- 1.2.1 Protein Fundamentals -- 1.2.2 Shotgun Proteomics -- 1.2.3 Separation of Peptides by Chromatography -- 1.2.4 Mass Spectrometry -- 1.3 Identification of Peptides and Proteins -- 1.4 Protein Quantitation -- 1.5 Applications and Downstream Analysis -- 1.6 Proteomics Software -- 1.6.1 Proteomics Data Standards and Databases -- 1.7 Conclusions -- Acknowledgements -- References -- Section I - Protein Identification -- Chapter 2 - De novo Peptide Sequencing -- 2.1 Introduction -- 2.2 Manual De novo Sequencing -- 2.3 Computer Algorithms -- 2.3.1 Search Tree Pruning -- 2.3.2 Spectrum Graph -- 2.3.3 PEAKS Algorithm -- 2.4 Scoring Function -- 2.4.1 Likelihood Ratio -- 2.4.2 Utilization of Many Ion Types -- 2.4.3 Combined Use of Different Fragmentations -- 2.4.4 Machine Learning -- 2.4.5 Amino Acid Score -- 2.5 Computer Software -- 2.5.1 Lutefisk -- 2.5.2 Sherenga -- 2.5.3 PEAKS -- 2.5.4 PepNovo -- 2.5.5 DACSIM -- 2.5.6 NovoHMM -- 2.5.7 MSNovo -- 2.5.8 PILOT -- 2.5.9 pNovo -- 2.5.10 Novor -- 2.6 Conclusion: Applications and Limitations of De novo Sequencing -- 2.6.1 Sequencing Novel Peptides and Detecting Mutated Peptides -- 2.6.2 Assisting Database Search -- 2.6.3 De novo Protein Sequencing -- 2.6.4 Unspecified PTM Characterization -- 2.6.5 Limitations -- Acknowledgements -- References -- Chapter 3 - Peptide Spectrum Matching via Database Search and Spectral Library Search -- 3.1 Introduction -- 3.2 Protein Sequence Databases -- 3.3 Overview of Shotgun Proteomics Method -- 3.4 Collision Induced Dissociation Fragments Peptides in Predictable Ways -- 3.5 Overview of Database Searching -- 3.6 MyriMatch Database Search Engine -- 3.6.1 Spectrum Preparation.
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3.6.2 Peptide Harvesting from Database -- 3.6.3 Comparing Experimental MS/MS with Candidate Peptide Sequences -- 3.7 Accounting for Post-Translational Modifications During Database Search -- 3.8 Reporting of Database Search Peptide Identifications -- 3.9 Spectral Library Search Concept -- 3.10 Peptide Spectral Libraries -- 3.11 Overview of Spectral Library Searching -- 3.12 Pepitome Spectral Library Search Engine -- 3.12.1 Experimental MS2 Spectrum Preparation -- 3.12.2 Library Spectrum Harvesting and Spectrum-Spectrum Matching -- 3.12.3 Results Reporting -- 3.13 Search Results Vary Between Various Database Search Engines and Different Peptide Identification Search Strategies -- 3.14 Conclusion -- References -- Chapter 4 - PSM Scoring and Validation -- 4.1 Introduction -- 4.2 Statistical Scores and What They Mean -- 4.2.1 Statistical Probability p-Values and Multiple Testing -- 4.2.2 Expectation Scores -- 4.2.3 False Discovery Rates -- 4.2.4 q-Values -- 4.2.5 Posterior Error Probability -- 4.2.6 Which Statistical Measure to Use and When -- 4.2.7 Target Decoy Approaches for FDR Assessment -- 4.3 Post-Search Validation Tools and Methods -- 4.3.1 Qvality -- 4.3.2 PeptideProphet -- 4.3.3 Percolator -- 4.3.4 Mass Spectrometry Generating Function -- 4.3.5 Nokoi -- 4.3.6 PepDistiller -- 4.3.7 Integrated Workflow and Pipeline Analysis Tools -- 4.3.8 Developer Libraries -- 4.4 Common Pitfalls and Problems in Statistical Analysis of Proteomics Data -- 4.4.1 Target-Decoy Peptide Assumptions -- 4.4.2 Peptide Modifications -- 4.4.3 Search Space Size -- 4.4.4 Distinct Peptides and Proteins -- 4.5 Conclusion and Future Trends -- References -- Chapter 5 - Protein Inference and Grouping -- 5.1 Background -- 5.1.1 Assignment of Peptides to Proteins -- 5.1.2 Protein Groups and Families -- 5.2 Theoretical Solutions and Protein Scoring.
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5.2.1 Protein Grouping Based on Sets of Peptides -- 5.2.2 Spectral-Focussed Inference Approaches -- 5.2.3 Considerations of Protein Length -- 5.2.4 Handling Sub-Set and Same-Set Proteins within Groups -- 5.2.5 Assignment of Representative or Group Leader Proteins -- 5.2.6 Importance of Peptide Classification to Quantitative Approaches -- 5.2.7 Scoring or Probability Assignment at the Protein-Level -- 5.2.8 Handling "One Hit Wonders" -- 5.3 Support for Protein Grouping in Data Standards -- 5.4 Conclusions -- Acknowledgements -- References -- Chapter 6 - Identification and Localization of Post-Translational Modifications by High-Resolution Mass Spectrometry -- 6.1 Introduction -- 6.2 Sample Preparation Challenges -- 6.3 Identification and Localization of Post-Translational Modifications -- 6.3.1 Computational Challenges -- 6.3.2 Annotation of Modifications -- 6.3.3 Common Post-Translational Modifications Identified by Mass Spectrometry -- 6.3.4 Validation of Results -- 6.4 Conclusion -- Acknowledgements -- References -- Section II - Protein Quantitation -- Chapter 7 - Algorithms for MS1-Based Quantitation -- 7.1 Introduction -- 7.2 Feature Detection and Quantitation -- 7.2.1 Conventional Feature Detection -- 7.2.2 Recent Approaches Based on Sparsity and Mixture Modelling -- 7.3 Chromatogram Alignment -- 7.3.1 Feature-Based Pattern Matching -- 7.3.2 Raw Profile Alignment -- 7.4 Abundance Normalisation -- 7.5 Protein-Level Differential Quantification -- 7.5.1 Statistical Methods -- 7.5.2 Statistical Models Accounting for Shared Peptides -- 7.6 Discussion -- Acknowledgements -- References -- Chapter 8 - MS2-Based Quantitation -- 8.1 MS2-Based Quantification of Proteins -- 8.2 Spectral Counting -- 8.2.1 Implementations -- 8.2.2 Conclusion on Spectrum Counting -- 8.3 Reporter Ion-Based Quantification -- 8.3.1 Identification.
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8.3.2 Reporter Ion Intensities, Interferences and Deisotoping -- 8.3.3 Ratio Estimation and Normalization -- 8.3.4 Implementation -- 8.3.5 Conclusion on Reporter Ion-Based Quantification -- Acknowledgements -- References -- Chapter 9 - Informatics Solutions for Selected Reaction Monitoring -- 9.1 Introduction -- 9.1.1 SRM - General Concept and Specific Bioinformatic Challenges -- 9.1.2 SRM-Specific Bioinformatics Tools -- 9.2 SRM Assay Development -- 9.2.1 Target and Transition Selection, Proteotypic and Quantotypic Peptides -- 9.2.2 Spikes of Isotopically Labeled Peptides and Protein Standards and Additional Assay Development Steps -- 9.2.3 Retention Time Regressions and Retention Time Scheduling -- 9.2.4 Method Generation for MS Acquisitions -- 9.3 System Suitability Assessments -- 9.4 Post-Acquisition Processing and Data Analysis -- 9.4.1 mProphet False Discovery Analysis, Peak Detection and Peak Picking -- 9.4.2 Data Viewing and Data Management: Custom Annotation, Results and Document Grids, Group Comparisons -- 9.4.3 Data Reports, LOD-LOQ Calculations and Statistical Processing, Use of Skyline External Tools -- 9.4.4 Group Comparisons and Peptide & -- Protein Quantification -- 9.4.5 Easy Data Sharing and SRM Resources - Panorama -- 9.5 Post-Translational Modifications and Protein Isoforms or Proteoforms -- 9.6 Conclusion and Future Outlook -- Acknowledgements -- References -- Chapter 10 - Data Analysis for Data Independent Acquisition -- 10.1 Analytical Methods -- 10.1.1 Motivation -- 10.1.2 Background: Other MS Methods -- 10.1.3 DIA Concept -- 10.1.4 Theoretical Considerations -- 10.1.5 Main DIA Methods -- 10.1.5.1 PRM -- 10.1.5.2 MSE/HDMSE/AIF -- 10.1.5.3 PAcIFIC -- 10.1.5.4 SWATH-MS -- 10.1.5.5 MSX -- 10.1.6 Analyte Separation Methods -- 10.2 Data Analysis Methods -- 10.2.1 DIA Data Analysis -- 10.2.2 Untargeted Analysis, Spectrum-Centric.
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10.2.2.1 Signal Clustering -- 10.2.2.2 Pseudo-Spectra Identification -- 10.2.2.3 Peptide and Protein Quantification -- 10.2.3 Targeted Analysis, Chromatogram-Centric -- 10.2.3.1 Retention Time Normalisation -- 10.2.3.2 Chromatogram Extraction -- 10.2.3.3 Peak Group Scoring -- 10.2.3.3.1 Peak Picking.The aim of peak picking is to identify potential peak candidates (points of elution) for each peptide in the fragme... -- 10.2.3.3.2 Peak Scoring.The algorithm next operates on the peak group candidates found in the previous step and computes a set of scores fo... -- 10.2.3.4 Peak Quantification -- 10.2.3.5 Error Rate Estimation -- 10.2.3.6 Alignment -- 10.2.4 FDR -- 10.2.5 Results and Formats -- 10.3 Challenges -- References -- Section III - Open Source Software Environments for Proteome Informatics -- Chapter 11 - Data Formats of the Proteomics Standards Initiative -- 11.1 Introduction -- 11.2 mzML -- 11.2.1 Data Format -- 11.2.2 Software Implementations -- 11.2.3 Current Work -- 11.2.4 Variations of mzML -- 11.3 mzIdentML -- 11.3.1 Data Format -- 11.3.2 Software Implementations -- 11.3.3 Current Work -- 11.4 mzQuantML -- 11.4.1 Data Format -- 11.4.2 Software Implementations -- 11.4.3 Current Work -- 11.5 mzTab -- 11.5.1 Data Format -- 11.5.2 Software Implementations -- 11.5.3 Current Work -- 11.6 TraML -- 11.6.1 Data Format -- 11.6.2 Software Implementations -- 11.7 Other Data Standard Formats Produced by the PSI -- 11.8 Conclusions -- Abbreviations -- Acknowledgements -- References -- Chapter 12 - OpenMS: A Modular, Open-Source Workflow System for the Analysis of Quantitative Proteomics Data -- 12.1 Introduction -- 12.2 Peptide Identification -- 12.3 iTRAQ Labeling -- 12.4 Dimethyl Labeling -- 12.5 Label-Free Quantification -- 12.6 Conclusion -- Acknowledgements -- References -- Chapter 13 - Using Galaxy for Proteomics -- 13.1 Introduction.
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13.2 The Galaxy Framework as a Solution for MS-Based Proteomic Informatics.
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