Computational and Statistical Methods for Protein Quantification by Mass Spectrometry

Computational and Statistical Methods for Protein Quantification by Mass Spectrometry PDF

Author: Ingvar Eidhammer

Publisher: John Wiley & Sons

Published: 2012-12-10

Total Pages: 290

ISBN-13: 111849377X

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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.

Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry PDF

Author: Susmita Datta

Publisher: Springer

Published: 2016-12-15

Total Pages: 294

ISBN-13: 3319458094

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This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.

Computational Methods for Mass Spectrometry Proteomics

Computational Methods for Mass Spectrometry Proteomics PDF

Author: Ingvar Eidhammer

Publisher: John Wiley & Sons

Published: 2008-02-28

Total Pages: 296

ISBN-13: 9780470724293

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Proteomics is the study of the subsets of proteins present in different parts of an organism and how they change with time and varying conditions. Mass spectrometry is the leading technology used in proteomics, and the field relies heavily on bioinformatics to process and analyze the acquired data. Since recent years have seen tremendous developments in instrumentation and proteomics-related bioinformatics, there is clearly a need for a solid introduction to the crossroads where proteomics and bioinformatics meet. Computational Methods for Mass Spectrometry Proteomics describes the different instruments and methodologies used in proteomics in a unified manner. The authors put an emphasis on the computational methods for the different phases of a proteomics analysis, but the underlying principles in protein chemistry and instrument technology are also described. The book is illustrated by a number of figures and examples, and contains exercises for the reader. Written in an accessible yet rigorous style, it is a valuable reference for both informaticians and biologists. Computational Methods for Mass Spectrometry Proteomics is suited for advanced undergraduate and graduate students of bioinformatics and molecular biology with an interest in proteomics. It also provides a good introduction and reference source for researchers new to proteomics, and for people who come into more peripheral contact with the field.

Statistical Methods for the Analysis of Mass Spectrometry-based Proteomics Data

Statistical Methods for the Analysis of Mass Spectrometry-based Proteomics Data PDF

Author: Xuan Wang

Publisher:

Published: 2012

Total Pages:

ISBN-13:

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Proteomics serves an important role at the systems-level in understanding of biological functioning. Mass spectrometry proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. In the most widely used bottom-up approach to MS-based high-throughput quantitative proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and then analyzed using a mass spectrometer. The three fundamental challenges in the analysis of bottom-up MS-based proteomics are as follows: (i) Identifying the proteins that are present in a sample, (ii) Aligning different samples on elution (retention) time, mass, peak area (intensity) and etc, (iii) Quantifying the abundance levels of the identified proteins after alignment. Each of these challenges requires knowledge of the biological and technological context that give rise to the observed data, as well as the application of sound statistical principles for estimation and inference. In this dissertation, we present a set of statistical methods in bottom-up proteomics towards protein identification, alignment and quantification. We describe a fully Bayesian hierarchical modeling approach to peptide and protein identification on the basis of MS/MS fragmentation patterns in a unified framework. Our major contribution is to allow for dependence among the list of top candidate PSMs, which we accomplish with a Bayesian multiple component mixture model incorporating decoy search results and joint estimation of the accuracy of a list of peptide identifications for each MS/MS fragmentation spectrum. We also propose an objective criteria for the evaluation of the False Discovery Rate (FDR) associated with a list of identifications at both peptide level, which results in more accurate FDR estimates than existing methods like PeptideProphet. Several alignment algorithms have been developed using different warping functions. However, all the existing alignment approaches suffer from a useful metric for scoring an alignment between two data sets and hence lack a quantitative score for how good an alignment is. Our alignment approach uses "Anchor points" found to align all the individual scan in the target sample and provides a framework to quantify the alignment, that is, assigning a p-value to a set of aligned LC-MS runs to assess the correctness of alignment. After alignment using our algorithm, the p-values from Wilcoxon signed-rank test on elution (retention) time, M/Z, peak area successfully turn into non-significant values. Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical mass spectrometry-based proteomics data sets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of "presence / absence", we enable the selection of proteins not typically amendable to quantitative analysis; e.g., "one-state" proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence / absence analysis of a given data set in a principled way, resulting in a single list of selected proteins with a single associated FDR.

Mass Spectrometry Data Analysis in Proteomics

Mass Spectrometry Data Analysis in Proteomics PDF

Author: Rune Matthiesen

Publisher:

Published: 2019

Total Pages: 445

ISBN-13: 9781493997442

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The aim of this new edition is to provide detailed information on each topic and present novel ideas and views that can influence future developments in mass spectrometry-based proteomics. In contrast to the previous editions, this third edition aims to provide the most relevant computational methods, focusing on computational concepts. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Mass Spectrometry Data Analysis in Proteomics, Third Edition to ensure successful results in the further study of this vital field.

Quantitative Methods in Proteomics

Quantitative Methods in Proteomics PDF

Author: Katrin Marcus

Publisher: Humana

Published: 2021-05-06

Total Pages: 483

ISBN-13: 9781071610237

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This second edition provides new and updated methods on the principles underlying modern protein analysis, from statistical issues to gel-based and mass spectrometry-based applications. Chapters detail protein quantification as basis for realisation of quantitative studies, gel-based and mass spectrometry-based quantification techniques, TMT, IPTL, PRM, MALDI Imaging, SILAC, PTM analysis, DIA, cross-linking, and the up-to-date topics of software and data analysis. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Quantitative Methods in Proteomics, Second Edition aims to provide comprehensive and competent overview in the important and still growing field of quantitative proteomics.

Mass Spectrometry Analysis for Protein-Protein Interactions and Dynamics

Mass Spectrometry Analysis for Protein-Protein Interactions and Dynamics PDF

Author: M. Chance

Publisher: John Wiley & Sons

Published: 2008-09-22

Total Pages: 325

ISBN-13: 0470258861

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Presents a wide variety of mass spectrometry methods used to explore structural mechanisms, protein dynamics and interactions between proteins. Preliminary chapters cover mass spectrometry methods for examining proteins and are then followed by chapters devoted to presenting very practical, how-to methods in a detailed way. Includes footprinting and plistex specifically, setting this book apart from the competition.

Selected Reaction Monitoring Mass Spectrometry (SRM-MS) in Proteomics

Selected Reaction Monitoring Mass Spectrometry (SRM-MS) in Proteomics PDF

Author: Mahmud Hossain

Publisher: Springer Nature

Published: 2020-09-26

Total Pages: 283

ISBN-13: 3030534332

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Covering a wide-ranging facet of a “gold-standard” targeted mass spectrometry (MS) method for the consistent detection and accurate quantification of preselected proteins in complex biological matrices, Selected Reaction Monitoring Mass Spectrometry (SRM-MS) in Proteomics: A Comprehensive View describes: The knowledge-based development of highly efficient SRM methodology including assay workflow, selection of proteins, peptides, transitions and its validation, and quality assessment Available bioinformatic tools – for both pre-acquisition method development and post-MS acquisition data analysis and data repositories Various relative and absolute quantification techniques SRM-MS’ widespread applications in biomarker development and in clinical studies, as well as in the analysis of various posttranslational modifications (PTMs) Current challenges and contemporary trends to overcome those difficulties In addition, it features the historical development of modern-day mass spectrometry with its vivid applications and also covers basic MS instrumentation, ionization techniques, and various proteomics approaches. Comprehensive discussion, extensive references at the end of each chapter, and the list of review articles in the bibliography offer invaluable resources for advanced readings. Researchers from the undergraduate to postgraduate level and beyond in both academic or industry settings studying and working on mass spectrometry and/or proteomics will benefit from this book.

Proteomics Data Analysis

Proteomics Data Analysis PDF

Author: Daniela Cecconi

Publisher:

Published: 2021

Total Pages: 326

ISBN-13: 9781071616413

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This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. Authoritative and practical, Proteomics Data Analysis serves as an ideal guide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics.