Bringing together world experts to provide clear explanations of the key algorithms, workflows and analysis frameworks, 'Proteome Informatics' providea a detailed introduction to the main informatics topics that underpin the various LC-MS/MS protocols used for protein identification and quantitation. The field of proteomics has developed rapidly over the past decade nurturing the need for a detailed introduction to the various informatics topics that underpin the main liquid chromatography tandem mass spectrometry (LC-MS/MS) protocols used for protein identification and quantitation. Proteins are a key component of any biological system, and monitoring proteins using LC-MS/MS proteomics is becoming commonplace in a wide range of biological research areas. However, many researchers treat proteomics software tools as a black box, drawing conclusions from the output of such tools without considering the nuances and limitations of the algorithms on which such software is based. This book seeks to address this situation by bringing together world experts to provide clear explanations of the key algorithms, workflows and analysis frameworks, so that users of proteomics data can be confident that they are using appropriate tools in suitable ways 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. 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 Introduction2.2 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 Peptides2.6.2 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 3.6.2 Peptide Harvesting From Database3.6.3 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 Strategies3.14 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 Editor: Conrad Bessant. Mode Of Access: World Wide Web.
