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Novel Computational Methods For Mass Spectrometry Based Protein Identification
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Book Synopsis Novel Computational Methods for Mass Spectrometry Based Protein Identification by : Rachana Jain
Download or read book Novel Computational Methods for Mass Spectrometry Based Protein Identification written by Rachana Jain and published by . This book was released on 2010 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mass spectrometry (MS) is used routinely to identify proteins in biological samples. Peptide Mass Fingerprinting (PMF) uses peptide masses and a pre-specified search database to identify proteins. It is often used as a complementary method along with Peptide Fragment Fingerprinting (PFF) or de-novo sequencing for increasing confidence and coverage of protein identification during mass spectrometric analysis. At the core of a PMF database search algorithm lies a similarity measure or quality statistics that is used to gauge the level to which an experimentally obtained peaklist agrees with a list of theoretically observable mass-to-charge ratios for a protein in a database. In this dissertation, we use publicly available gold standard data sets to show that the selection of search criteria such as mass tolerance and missed cleavages significantly affects the identification results. We propose, implement and evaluate a statistical (Kolmogorov-Smirnov-based) test which is computed for a large mass error threshold thus avoiding the choice of appropriate mass tolerance by the user. We use the mass tolerance identified by the Kolmogorov-Smirnov test for computing other quality measures. The results from our careful and extensive benchmarks suggest that the new method of computing the quality statistics without requiring the end-user to select a mass tolerance is competitive. We investigate the similarity measures in terms of their information content and conclude that the similarity measures are complementary and can be combined into a scoring function to possibly improve the over all accuracy of PMF based identification methods. We describe a new database search tool, PRIMAL, for protein identification using PMF. The novelty behind PRIMAL is two-fold. First, we comprehensively analyze methods for measuring the degree of similarity between experimental and theoretical peaklists. Second, we employ machine learning as a means of combining the individual similarity measures into a scoring function. Finally, we systematically test the efficacy of PRIMAL in identifying proteins using highly curated and publicly available data. Our results suggest that PRIMAL is competitive if not better than some of the tools extensively used by the mass spectrometry community. A web server with an implementation of the scoring function is available at http://bmi.cchmc.org/primal. We also note that the methodology is directly extensible to MS/MS based protein identification problem. We detail how to extend our approaches to the more complex MS/MS data.
Book Synopsis Computational Methods for Mass Spectrometry Proteomics by : Ingvar Eidhammer
Download or read book Computational Methods for Mass Spectrometry Proteomics written by Ingvar Eidhammer and published by John Wiley & Sons. This book was released on 2008-02-28 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Computational and Statistical Methods for Protein Quantification by Mass Spectrometry by : Ingvar Eidhammer
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.
Book Synopsis Computational Methods in Mass Spectrometry-Based Protein 3D Studies by : Rosa M. Vitale
Download or read book Computational Methods in Mass Spectrometry-Based Protein 3D Studies written by Rosa M. Vitale and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Methods in Mass Spectrometry-Based Protein 3D Studies.
Book Synopsis Novel Computational Techniques in Mass Spectrometry Based Proteomics by : Lukas Mueller
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:
Book Synopsis Computational Methods for Protein-protein Complex Structure Prediction and Mass Spectrometry-based Identification by : Weiwei Tong
Download or read book Computational Methods for Protein-protein Complex Structure Prediction and Mass Spectrometry-based Identification written by Weiwei Tong and published by . This book was released on 2008 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Nearly all major processes in living cells are carried out by complex apparatus consisting of protein molecules. This thesis describes computational tools developed to help investigate two fundamental questions about proteins that underlie cell functions: how they interact with each other and form complex structures; and how they are expressed and modified in different cell states. In order to address the first question, several methods are developed to predict protein-protein complex structures. Protein interactions are energy driven processes. The prediction of protein complex structures is the search for the global minimum on the binding free-energy landscape. An approach is described that uses Van der Wools energy, desolvation energy and shape complementarity as the scoring functions and a five-dimensional fast Fourier transform algorithm to expedite the search. Two methods to screen and optimize the predicted protein complex structures are also introduced. They incorporate additional energy terms and clustering algorithms to provide more precise estimations of the binding free-energy. The same methods can also be used to predict hot spots, the mutations of which significantly alter the binding kinetics. To study the protein expression profiles, a two-step approach for protein identification using peptide mass fingerprinting data is developed. Peptide mass fingerprinting uses peptide masses determined by mass spectrometry to identify the peptides and subsequently, the proteins in the sample Peaks in the mass spectrum are associated with known peptide sequences in the database based on log-likelihood ratio test. A statistical algorithm is then used to identify proteins by comparing the probability of each protein's presence in the sample, given the peak assignments with the background probability. This method also discovers post-translational modifications in the identified proteins. The protein binding prediction program successfully predicts protein complex structures that closely resemble their native forms, as observed by x-ray crystallography or NMR. The refinements and hot spot predictions also give accurate and consistent results. The database search program that interprets mass spectrometry data is evaluated with artificial and experimental data. The program identifies proteins in the sample with high sensitivity and specificity. The results presented in this thesis demonstrate that computational methods help to better understand the structure and the composition of the protein machineries. All of the methods described herein have been implemented and made available for the research community over the Internet.
Book Synopsis Mass Spectrometry Data Analysis in Proteomics by : Rune Matthiesen
Download or read book Mass Spectrometry Data Analysis in Proteomics written by Rune Matthiesen and published by . This book was released on 2013 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the publishing of the first edition, the methodologies and instrumentation involved in the field of mass spectrometry-based proteomics has improved considerably. Fully revised and expanded, Mass Spectrometry Data Analysis in Proteomics, Second Edition presents expert chapters on specific MS-based methods or data analysis strategies in proteomics. The volume covers data analysis topics relevant for quantitative proteomics, post translational modification, HX-MS, glycomics, and data exchange standards, among other topics. Written in the highly successful Methods in Molecular Biology series format, chapters include brief introductions to their respective subjects, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Updated and authoritative, Mass Spectrometry Data Analysis in Proteomics, Second Edition serves as a detailed guide for all researchers seeking to further our knowledge in the field of proteomics.
Book Synopsis Novel Computational Techniques for Quantitative Mass Spectrometry Based Proteomics by : Lukas Niklaus Müller
Download or read book Novel Computational Techniques for Quantitative Mass Spectrometry Based Proteomics written by Lukas Niklaus Müller and published by . This book was released on 2008 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Biology and Applied Bioinformatics by : Heitor Lopes
Download or read book Computational Biology and Applied Bioinformatics written by Heitor Lopes and published by IntechOpen. This book was released on 2011-09-02 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays it is difficult to imagine an area of knowledge that can continue developing without the use of computers and informatics. It is not different with biology, that has seen an unpredictable growth in recent decades, with the rise of a new discipline, bioinformatics, bringing together molecular biology, biotechnology and information technology. More recently, the development of high throughput techniques, such as microarray, mass spectrometry and DNA sequencing, has increased the need of computational support to collect, store, retrieve, analyze, and correlate huge data sets of complex information. On the other hand, the growth of the computational power for processing and storage has also increased the necessity for deeper knowledge in the field. The development of bioinformatics has allowed now the emergence of systems biology, the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behavior of a living being. This book presents some theoretical issues, reviews, and a variety of bioinformatics applications. For better understanding, the chapters were grouped in two parts. In Part I, the chapters are more oriented towards literature review and theoretical issues. Part II consists of application-oriented chapters that report case studies in which a specific biological problem is treated with bioinformatics tools.
Author :Willy Vincent Bienvenut Publisher :Springer Science & Business Media ISBN 13 :1402033192 Total Pages :314 pages Book Rating :4.4/5 (2 download)
Book Synopsis Acceleration and Improvement of Protein Identification by Mass Spectrometry by : Willy Vincent Bienvenut
Download or read book Acceleration and Improvement of Protein Identification by Mass Spectrometry written by Willy Vincent Bienvenut and published by Springer Science & Business Media. This book was released on 2005-12-05 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: At present where protein identification and characterisation using mass spectrometry is a method of choice, this book is presenting a review of basic proteomic techniques. The second part of the book is related to the novel high throughput protein identification technique called the 'molecular scanner'. Several protein identification techniques are described, especially the peptide mass fingerprint with MALDI-MS based method. E.g. ionisation process, matrix available, signal reproducibility and suppression effect, as well as date treatment for protein identification using bioinformatics tools.
Book Synopsis Introduction to Protein Mass Spectrometry by : Pradip K. Ghosh
Download or read book Introduction to Protein Mass Spectrometry written by Pradip K. Ghosh and published by Academic Press. This book was released on 2015-12-10 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Protein Mass Spectrometry provides a comprehensive overview of this increasingly important, yet complex, analytical technique. Unlike many other methods which automatically yield an absolutely unique protein name as output, protein mass spectrometry generally requires a deduction of protein identity from determination of peptide fragmentation products. This book enables readers to both understand, and appreciate, how determinations about protein identity from mass spectrometric data are made. Coverage begins with the technical basics, including preparations, instruments, and spectrometric analysis of peptides and proteins, before exploring applied use in biological applications, bioinformatics, database, and software resources. Citing the most recent and relevant work in the field of biological mass spectrometry, the book is written for researchers and scientists new to the field, but is also an ideal resource for those hoping to hone their analytical abilities. Offers introductory information for scientists and researchers new to the field, as well as advanced insight into the critical assessment of computer-analyzed mass spectrometric results and their current limitations Provides examples of commonly-used MS instruments from Bruker, Applied Biosystems, JEOL, Thermo Scientific/Thermo Fisher Scientific, IU, and Waters Includes biological applications and exploration of analytical tools and databases for bioinformatics
Book Synopsis Computational Methods for Understanding Mass Spectrometry-Based Shotgun Proteomics Data by : Pavel Sinitcyn
Download or read book Computational Methods for Understanding Mass Spectrometry-Based Shotgun Proteomics Data written by Pavel Sinitcyn and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry-based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.
Book Synopsis Combining Mass Spectrometry Analysis and Computational Methods for the Identification of Protein Phosphorylation and Acetylation by : 吳欣怡
Download or read book Combining Mass Spectrometry Analysis and Computational Methods for the Identification of Protein Phosphorylation and Acetylation written by 吳欣怡 and published by . This book was released on 2009 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Methods for Mass Spectrometry-based Study of Protein-RNA Or Protein-DNA Complexes and Quantitative Metaproteomics by :
Download or read book Computational Methods for Mass Spectrometry-based Study of Protein-RNA Or Protein-DNA Complexes and Quantitative Metaproteomics written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Methods in Biomedical Research by : Ravindra Khattree
Download or read book Computational Methods in Biomedical Research written by Ravindra Khattree and published by CRC Press. This book was released on 2007-12-12 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuing advances in biomedical research and statistical methods call for a constant stream of updated, cohesive accounts of new developments so that the methodologies can be properly implemented in the biomedical field. Responding to this need, Computational Methods in Biomedical Research explores important current and emerging computational statistical methods that are used in biomedical research. Written by active researchers in the field, this authoritative collection covers a wide range of topics. It introduces each topic at a basic level, before moving on to more advanced discussions of applications. The book begins with microarray data analysis, machine learning techniques, and mass spectrometry-based protein profiling. It then uses state space models to predict US cancer mortality rates and provides an overview of the application of multistate models in analyzing multiple failure times. The book also describes various Bayesian techniques, the sequential monitoring of randomization tests, mixed-effects models, and the classification rules for repeated measures data. The volume concludes with estimation methods for analyzing longitudinal data. Supplying the knowledge necessary to perform sophisticated statistical analyses, this reference is a must-have for anyone involved in advanced biomedical and pharmaceutical research. It will help in the quest to identify potential new drugs for the treatment of a variety of diseases.
Book Synopsis Computational Acceleration of Mass-spectrometry Based Protein Identification Algorithms Using Field-programmable Gate Arrays by :
Download or read book Computational Acceleration of Mass-spectrometry Based Protein Identification Algorithms Using Field-programmable Gate Arrays written by and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bioinformatics Methods for Protein Identification Using Peptide Mass Fingerprinting Data by : Zhao Song
Download or read book Bioinformatics Methods for Protein Identification Using Peptide Mass Fingerprinting Data written by Zhao Song and published by . This book was released on 2009 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Protein identification using mass spectrometry is an important yet partially solved problem in the study of proteomics during the post-genomic era. The major techniques used in mass spectrometry are Peptide Mass Fingerprinting (PMF) and Tandem mass spectrometry (MS/MS). PMF is faster and economical compared with MS/MS and widely applicable in many fields. Our work focus on the method development for protein identification using PMF data and this work covers three subjects: (1) Protein Identification scoring function development: we developed the Probability Based Scoring Function (PBSF) which is used to quantify the degree of match between PMF data and candidate protein. The derived score is used to rank the protein and predict the identification. (2) Confidence Assessment development: scoring function may lead to false positive identification since the top hit from a database search may not be the target protein. In addition, the identification scores assigned singly by a scoring function (raw scores) are not normalized. Therefore, the ranking based on raw scores may be biased. To address the above issue, we have developed a statistical model to evaluate the confidence of the raw score and to improve the ranking of proteins for identification. (3) Software development: we implemented our computational methods in an open source package "ProteinDecision" which is freely available upon request.
