Download or read book Computational and Statistical Methods for Protein Quantification by Mass Spectrometry written by Ingvar Eidhammer and published by John Wiley & Sons. This book was released on 2012-12-10 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Statistical Analysis of Proteomics Metabolomics and Lipidomics Data Using Mass Spectrometry written by Susmita Datta and published by Springer. This book was released on 2016-12-15 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Computational and Statistical Methods for Mass Spectrometry Data Analysis written by Mateusz Łącki and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Słowa kluczowe: obliczeniowa spektrometria mas, modelowanie dysocjacji indukowanej transferem elektronu, obliczenia dokładnej struktury izotopowej, statystyczne modelowanie dekonwolucji sygnału w spektrometrii mas, computational mass spectrometry, modelling electron transfer dissociation, calculations of the isotopic fine structure, statistical model of signal deconvolution in mass spectrometry.
Download or read book Computational and Statistical Methods for Analysing Big Data with Applications written by Shen Liu and published by Academic Press. This book was released on 2015-11-20 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. Advanced computational and statistical methodologies for analysing big data are developed Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable Case studies are discussed to demonstrate the implementation of the developed methods Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation Computing code/programs are provided where appropriate
Download or read book Computational Methods for Mass Spectrometry Data Analysis and Imaging written by Robin Schmid and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Methods for the Analysis of Mass Spectrometry Data written by Yuping Wu (Ph. D.) and published by . This book was released on 2006 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book High Performance Algorithms for Mass Spectrometry Based Omics written by Fahad Saeed and published by Springer Nature. This book was released on 2022-09-02 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: To date, processing of high-throughput Mass Spectrometry (MS) data is accomplished using serial algorithms. Developing new methods to process MS data is an active area of research but there is no single strategy that focuses on scalability of MS based methods. Mass spectrometry is a diverse and versatile technology for high-throughput functional characterization of proteins, small molecules and metabolites in complex biological mixtures. In the recent years the technology has rapidly evolved and is now capable of generating increasingly large (multiple tera-bytes per experiment) and complex (multiple species/microbiome/high-dimensional) data sets. This rapid advance in MS instrumentation must be matched by equally fast and rapid evolution of scalable methods developed for analysis of these complex data sets. Ideally, the new methods should leverage the rich heterogeneous computational resources available in a ubiquitous fashion in the form of multicore, manycore, CPU-GPU, CPU-FPGA, and IntelPhi architectures. The absence of these high-performance computing algorithms now hinders scientific advancements for mass spectrometry research. In this book we illustrate the need for high-performance computing algorithms for MS based proteomics, and proteogenomics and showcase our progress in developing these high-performance algorithms.
Download or read book Mass Spectrometry Data Analysis in Proteomics written by Rune Matthiesen and published by Humana. This book was released on 2019-10-01 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Quantitative Medical Data Analysis Using Mathematical Tools And Statistical Techniques written by Don Hong and published by World Scientific. This book was released on 2007-07-10 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative biomedical data analysis is a fast-growing interdisciplinary area of applied and computational mathematics, statistics, computer science, and biomedical science, leading to new fields such as bioinformatics, biomathematics, and biostatistics. In addition to traditional statistical techniques and mathematical models using differential equations, new developments with a very broad spectrum of applications, such as wavelets, spline functions, curve and surface subdivisions, sampling, and learning theory, have found their mathematical home in biomedical data analysis.This book gives a new and integrated introduction to quantitative medical data analysis from the viewpoint of biomathematicians, biostatisticians, and bioinformaticians. It offers a definitive resource to bridge the disciplines of mathematics, statistics, and biomedical sciences. Topics include mathematical models for cancer invasion and clinical sciences, data mining techniques and subset selection in data analysis, survival data analysis and survival models for cancer patients, statistical analysis and neural network techniques for genomic and proteomic data analysis, wavelet and spline applications for mass spectrometry data preprocessing and statistical computing.
Download or read book Novel Computational Techniques in Mass Spectrometry Based Proteomics written by Lukas Mueller and published by . This book was released on 2011-07 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Methods in Analyzing Mass Spectrometry Dataset written by Baolin Wu and published by . This book was released on 2004 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Analysis in Proteomics Novel Computational Strategies for Modeling and Interpreting Complex Mass Spectrometry Data written by and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Contemporary proteomics studies require computational approaches to deal with both the complexity of the data generated, and with the volume of data produced. The amalgamation of mass spectrometry -- the analytical tool of choice in proteomics -- with the computational and statistical sciences is still recent, and several avenues of exploratory data analysis and statistical methodology remain relatively unexplored. The current study focuses on three broad analytical domains, and develops novel exploratory approaches and practical tools in each. Data transform approaches are the first explored. These methods re-frame data, allowing for the visualization and exploitation of features and trends that are not immediately evident. An exploratory approach making use of the correlation transform is developed, and is used to identify mass-shift signals in mass spectra. This approach is used to identify and map post-translational modifications on individual peptides, and to identify SILAC modification-containing spectra in a full-scale proteomic analysis. Secondly, matrix decomposition and projection approaches are explored; these use an eigen-decomposition to extract general trends from groups of related spectra. A data visualization approach is demonstrated using these techniques, capable of visualizing trends in large numbers of complex spectra, and a data compression and feature extraction technique is developed suitable for use in spectral modeling. Finally, a general machine learning approach is developed based on conditional random fields (CRFs). These models are capable of dealing with arbitrary sequence modeling tasks, similar to hidden Markov models (HMMs), but are far more robust to interdependent observational features, and do not require limiting independence assumptions to remain tractable. The theory behind this approach is developed, and a simple machine learning fragmentation model is developed to test the hypothesis that reproducible sequence-specific intens.
Download or read book Introduction to Mass Spectrometry written by J. Throck Watson and published by John Wiley & Sons. This book was released on 2013-07-09 with total page 972 pages. Available in PDF, EPUB and Kindle. Book excerpt: Completely revised and updated, this text provides an easy-to-read guide to the concept of mass spectrometry and demonstrates its potential and limitations. Written by internationally recognised experts and utilising "real life" examples of analyses and applications, the book presents real cases of qualitative and quantitative applications of mass spectrometry. Unlike other mass spectrometry texts, this comprehensive reference provides systematic descriptions of the various types of mass analysers and ionisation, along with corresponding strategies for interpretation of data. The book concludes with a comprehensive 3000 references. This multi-disciplined text covers the fundamentals as well as recent advance in this topic, providing need-to-know information for researchers in many disciplines including pharmaceutical, environmental and biomedical analysis who are utilizing mass spectrometry
Download or read book Computational Approaches for Improved Identification Quantitation and Interpretation of Mass Spectrometry based omics Data written by Nicholas William Kwiecien and published by . This book was released on 2016 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research described in this dissertation presents novel computational algorithms and strategies for (1) improving the assignment of molecular identities to analytes profiled by high-resolution gas chromatography-mass spectrometry (GC/MS), (2) performing relative quantitation of large sets of metabolites across expansive sets of mass spectrometry data files, (3) disseminating processed mass spectrometry data and post hoc statistical results in web-based platforms, and (4) monitoring mass spectrometer performance via a web-based data processing and analysis tool. An overview of the aforementioned computational strategies and developed software tools is presented in Chapter 1. A novel algorithm for leveraging accurate mass--afforded by high-resolution GC/MS systems--to discriminate between putative identifications assigned to profiled small molecules is described in Chapter 2. In Chapter 3, an algorithm and accompanying software suite designed to enable untargeted quantitation of small molecules across expansive sets of raw GC/MS data files is described. In Chapter 4, these algorithms are employed as part of a larger study wherein 174 single gene deletion strains of yeast were comprehensively profiled at the proteomic, metabolomic, and lipidomic levels. These multi-omic data were then integrated through various analysis planes in order to define functions of uncharacterized mitochondrial proteins. Chapter 5 details numerous web-based data visualization utilities developed for various projects designed to enable researchers to more rapidly interrogate MS data sets at depth. In Chapter 6, the development of a web-based mass spectrometry data deposition, processing, and visualization tool for automated quality control analysis is described.
Download or read book Statistical Inference and Classification for Mass Spectrometry MS Data written by Mourad Atlas and published by . This book was released on 2009 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mass spectrometry has emerged as a core technology for high throughput proteomics profiling in biomedical research. However, the complexity of the data poses new statistical challenges for the analysis. Statistical methods and software developments for analyzing proteomics data are likely to continue as a major area of research in the coming years. In this dissertation, we develop a novel statistical method for analyzing MS data. We propose to use the chemical knowledge regarding isotopic distribution of the peptide molecules along with quantitative modeling to detect chemically valuable peaks from each spectrum. More specifically, a mixture of location-shifted Poisson distribution is fitted to the deamidated isotopic distribution of a peptide molecule in low to moderate molecular weight of the mass spectrum. Maximum likelihood estimation by the expectation-maximization (EM) technique is used to estimate the parameters of the distribution. We then identify the monoisotopic peaks of the spectrum through formal statistical hypotheses testing procedures. Unlike low to moderate range MS data a Poisson distribution is not suitable for high mass ranges of the spectrum data due to symmetric nature of the isotopic distribution. Also, due to preprocessing and pronounced effect of the additional sources of variability, a Poisson approximation to the binomial model to the isotopic distribution may not hold. Therefore, a mixture of location-shifted Normal model is fitted to model each of the deamidated (possibly) isotopic distribution of a mass spectrum. A nonlinear optimization method to maximize the observed data likelihood is applied instead of EM algorithm to estimate the parameters of the distribution. Similar statistical testing procedures are applied for the peak detection method. A study of the effectiveness of our features selection method compared to some other relatively new feature selection methods in classifying case and control samples is explored. Superiority of our method is established in terms of the overall classification accuracy, sensitivity, specificity and area under the receptor operative curve (ROC) curve.
Download or read book Computational Methods for the Analysis of Mass Spectrometry Images written by Karl Michael Stefan Hanselmann and published by . This book was released on 2010 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mass Spectrometry in Drug Discovery written by David T. Rossi and published by CRC Press. This book was released on 2001-11-07 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mass Spectrometry in Drug Discovery summarizes the theory, instrumentation, techniques, and application of mass spectrometry and atmospheric pressure ionization to screening, evaluating, and improving the performance and quality of drug candidates. It provides time- and cost-efficient approaches for the generation and analysis of effective pharmaceuticals, covers advances in combinatorial chemistry, molecular biology, bioanalysis automation, and computing, and demonstrates the use of mass spectrometry in the assessment of disease states, drug targets, and potential drug agents.
