Download or read book Development of Algorithms for Mass Spectrometry Based Proteomics written by Lukas Reiter and published by . This book was released on 2009 with total page 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 Algorithms for Tandem Mass Spectrometry based Proteomics written by Ari Michael Frank and published by . This book was released on 2008 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tandem mass spectrometry (MS/MS) has emerged as the leading technology for high-throughput proteomics analysis, making it possible to rapidly identify and characterize thousands of different proteins in complex biological samples. In recent years we have witnessed a dramatic increase in the capability to acquire proteomics MS/MS data. To avoid computational bottlenecks, this growth in acquisition power must be accompanied by a comparable improvement in analysis capabilities. In this dissertation we present several algorithms we developed to meet some of the major computational challenges that have arisen in MS/MS analysis. Throughout our work we continually address two (sometimes overlapping) problems: how to make MS/MS-based sequence identifications more accurate, and how to make the identification process work much faster. Much of the work we present revolves around algorithms for de novo sequencing of peptides, which aims to discover the amino acid sequence of protein digests (peptides), solely from their experimental mass spectrum. We start off by describing a new scoring model which is used in our de novo sequencing algorithm called PepNovo. Our scoring scheme is based on a graphical model decomposition that describes many of the conditions that determine the intensities of fragment ions observed in mass spectra, such as dependencies between related fragment ions and the influence of the amino acids adjacent to the cleavage site. Besides predicting whole peptide sequences, one of the most useful applications of de novo algorithms is to generate short sequence tags for the purpose of database filtration. We demonstrate how using these tags speeds up database searches by two orders of magnitude compared to conventional methods. We extend the use of tag filtration and show that with high-resolution data, our de novo sequencing is accurate enough to enable extremely rapid identification via direct hash lookup of peptide sequences. The vast amount of MS/MS data that has become available has made it possible to use advanced data-driven machine learning methods to devise more acute algorithms. We describe a new scoring function for peptide-spectrum matches that uses the RankBoost ranking algorithm to learn and model the influences of the many intricate processes that occur during peptide fragmentation. Our method's superior discriminatory power boosts PepNovo's performance beyond the current state-of-the-art de novo sequencing algorithms. Our score also greatly improves the performance of database search programs, significantly increasing both their speed and sensitivity. When we applied our method to the challenging task of a proteogenomic search against a six-frame translation of the human genome, we were able to significantly increase the number of peptide identifications compared to current techniques by 60\%. To help speed up MS/MS analysis, we developed a clustering algorithm that exploits the redundancy that is inherent in large mass spectrometry datasets (these often contain hundreds and even thousands of spectra of the same peptide). When applied to large MS/MS datasets on the order of ten million spectra, our clustering algorithm reduces the number of spectra by an order of magnitude, without losing peptide identifications. Finally, we touch upon sequencing of intact proteins (``top-down'' analysis), which from a computational perspective, is only in its infancy -- very few algorithms have been developed for analysis of this type of data. We developed MS-TopDown, which uses the Spectral Alignment algorithm to characterize protein forms (i.e., determine the modification/mutation sites). Our algorithm can handle heavily modified proteins and can also distinguish between several isobaric protein forms present in the same spectrum.
Download or read book Proteome Informatics written by Conrad Bessant and published by Royal Society of Chemistry. This book was released on 2016-11-15 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Development of Algorithms for Mass Spectometry Based Proteomics written by Lukas Reiter and published by . This book was released on 2009 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Proteomics Sample Preparation written by Jörg von Hagen and published by John Wiley & Sons. This book was released on 2011-08-24 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This long-awaited first guide to sample preparation for proteomics studies overcomes a major bottleneck in this fast growing technique within the molecular life sciences. By addressing the topic from three different angles -- sample, method and aim of the study -- this practical reference has something for every proteomics researcher. Following an introduction to the field, the book looks at sample preparation for specific techniques and applications and finishes with a section on the preparation of sample types. For each method described, a summary of the pros and cons is given, as well as step-by-step protocols adaptable to any specific proteome analysis task.
Download or read book Proteomics Data Analysis written by Daniela Cecconi and published by . This book was released on 2021 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book The Development and Application of Methods for the Large scale Identification and Quantification of Proteins Using Mass Spectrometry written by and published by . This book was released on 2015 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: The work described in this dissertation highlights the versatility of mass spectrometry-based proteomics, detailing the development and/or application of several diverse methods that enable, and improve upon, the large-scale identification and/or quantification of whole proteomes. A broad overview of mass spectrometry-based proteomics and the technological innovations that have driven the field forward are presented in Chapter 1. Chapter 2 outlines a method that utilizes NeuCode SILAC labeling and machine learning algorithms to enable product ion annotation within tandem mass spectra, facilitating the implementation of both automated database searching and de novo sequencing. Chapter 3 presents a strategy for performing multiplexed quantification in the context of data-independent acquisition. Chapter 4 describes the extension of QuantMode, a strategy that utilizes gas-phase purification to improve the quantitative accuracy of isobaric tag-based methods, to an ETD-enabled ion trap system. Chapter 5 outlines a method that improves the sampling depth of label-free experiments without the use of offline fractionation or the significant increase in analysis time. In Chapter 6, both label-free and isobaric tag-based strategies are employed to evaluate the localization and functionality of proteins, protein phosphorylation, and protein acetylation within the various tissues of the model legume Medicago truncatula.
Download or read book Mass Spectrometry Based Chemical Proteomics written by W. Andy Tao and published by John Wiley & Sons. This book was released on 2019-07-10 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROVIDES STRATEGIES AND CONCEPTS FOR UNDERSTANDING CHEMICAL PROTEOMICS, AND ANALYZING PROTEIN FUNCTIONS, MODIFICATIONS, AND INTERACTIONS—EMPHASIZING MASS SPECTROMETRY THROUGHOUT Covering mass spectrometry for chemical proteomics, this book helps readers understand analytical strategies behind protein functions, their modifications and interactions, and applications in drug discovery. It provides a basic overview and presents concepts in chemical proteomics through three angles: Strategies, Technical Advances, and Applications. Chapters cover those many technical advances and applications in drug discovery, from target identification to validation and potential treatments. The first section of Mass Spectrometry-Based Chemical Proteomics starts by reviewing basic methods and recent advances in mass spectrometry for proteomics, including shotgun proteomics, quantitative proteomics, and data analyses. The next section covers a variety of techniques and strategies coupling chemical probes to MS-based proteomics to provide functional insights into the proteome. In the last section, it focuses on using chemical strategies to study protein post-translational modifications and high-order structures. Summarizes chemical proteomics, up-to-date concepts, analysis, and target validation Covers fundamentals and strategies, including the profiling of enzyme activities and protein-drug interactions Explains technical advances in the field and describes on shotgun proteomics, quantitative proteomics, and corresponding methods of software and database usage for proteomics Includes a wide variety of applications in drug discovery, from kinase inhibitors and intracellular drug targets to the chemoproteomics analysis of natural products Addresses an important tool in small molecule drug discovery, appealing to both academia and the pharmaceutical industry Mass Spectrometry-Based Chemical Proteomics is an excellent source of information for readers in both academia and industry in a variety of fields, including pharmaceutical sciences, drug discovery, molecular biology, bioinformatics, and analytical sciences.
Download or read book Quantitative Methods in Proteomics written by Katrin Marcus and published by Humana Press. This book was released on 2012-06-08 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: Protein modifications and changes made to them, as well as the quantities of expressed proteins, can define the various functional stages of the cell. Accordingly, perturbations can lead to various diseases and disorders. As a result, it has become paramount to be able to detect and monitor post-translational modifications and to measure the abundance of proteins within the cell with extreme sensitivity. While protein identification is an almost routine requirement nowadays, reliable techniques for quantifying unmodified proteins (including those that escape detection under standard conditions, such as protein isoforms and membrane proteins) is not routine. Quantitative Methods in Proteomics gives a detailed survey of topics and methods on the principles underlying modern protein analysis, from statistical issues when planning proteomics experiments, to gel-based and mass spectrometry-based applications. The quantification of post-translational modifications is also addressed, followed by the “hot” topics of software and data analysis, as well as various overview chapters which provide a comprehensive overview of existing methods in quantitative proteomics. Written in the successful Methods in Molecular BiologyTM series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and notes on troubleshooting and avoiding known pitfalls. Authoritative and easily accessible, Quantitative Methods in Proteomics serves as a comprehensive and competent overview of the important and still growing field of quantitative proteomics.
Download or read book Novel Data Analysis Methods and Algorithms for Identification of Peptides and Proteins by Use of Tandem Mass Spectrometry written by Hua Xu and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Tandem mass spectrometry is one of the most important tools for protein analysis. This thesis is focused on the development of new methods and algorithms for tandem mass spectrometry data analysis. A database search engine, MassMatrix, has also been developed that incorporates these methods and algorithms. The program is publicly available both on the web server at www.massmatrix.net and as a deliverable software package for personal computers. Three different scoring algorithms have been developed to identify and characterize proteins and peptides by use of tandem mass spectrometry data. The first one is targeted at the next generation of tandem mass spectrometers that are capable of high mass accuracy and resolution. Two scores calculated by the algorithm are sensitive to high mass accuracy due to the fact that this new algorithm explicitly incorporates mass accuracy into scoring potential peptide and protein matches for tandem mass spectra. The algorithm is further improved by employing Monte Carlo Simulations to calculate ion abundance based scores without any assumptions or simplifications. For high mass accuracy data, MassMatrix provides improvements in sensitivity over other database search programs. The second scoring algorithm based on peptide sequence tags inferred from tandem mass spectra further improves the performance of MassMatrix for low mass accuracy tandem mass spectrometry data. The third algorithm is the first automated data analysis method that uses peptide retention times in liquid chromatography to evaluate potential peptide matches for tandem mass spectrometry data. The algorithm predicts reverse phase liquid chromatography retention times of peptides by their hydrophobicities and compares the predicted retention times with the observed ones to evaluate the peptide matches. In order to handle low quality data, a new method has also been developed to reduce noise in tandem mass spectra and screen poor quality spectra. In addition, a data analysis method for identification of disulfide bonds in proteins and peptides by tandem mass spectrometry data has been developed and incorporated in MassMatrix. By this new approach, proteins and peptides with disulfide bonds can be directly identified in tandem mass spectrometry with high confidence without any chemical reduction and/or other derivatization.
Download or read book Statistical Methods for the Analysis of Mass Spectrometry based Proteomics Data written by Xuan Wang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Proteome Informatics written by Conrad Bessant and published by Royal Society of Chemistry. This book was released on 2016-11-23 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Mass Spectrometry Based Proteomics written by Kris Gevaert and published by Springer Nature. This book was released on 2023-09-04 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This detailed volume explores contemporary techniques in mass spectrometry-based proteomics. After covering overall proteome coverage and the cellular surfaceome, the book delves into proximity-induced biotinylation, abduction of protein complexes in viral-like particles, and thermal proteome profiling, as well as protocols for identifying protein N-terminal acetylation, protein processing by proteases, protein N-glycosylation, and protein phosphorylation. The book also collects chapters on automated preparation of clinical samples, the analysis of formalin-fixed paraffin-embedded samples, protocols for the isolation of extracellular vesicles and for the monitoring of selected protein modifications in clinical samples, and, finally, structural proteomics. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step and readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Mass Spectrometry-Based Proteomics serves as an ideal guide to its subject for both novices in the field of proteomics as well as specialists.
Download or read book Improving Peptide Detection in Mass Spectrometry based Proteomics written by Andy Lin and published by . This book was released on 2022 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last 30 years, the field of computational mass spectrometry-based proteomics has made great strides. Specifically, the development of database search engines has allowed for the automatic annotation of observed spectra. In addition, the application of target-decoy competition for the purposes of estimating the false discovery rate of a set of peptide-spectrum matches has been instrumental for improving the statistical evidence for a set of confidently detected peptides. While great advances have been made, additional progress is still possible. This work describes three methods for improving computational proteomics methods. The first method describes a new database score function, combined p-value, that aims to take advantage of two advances in database searching: high-resolution MS/MS spectra and statistical calibration. The next method presents a variant of the target-decoy competition process for estimating the false discovery rate. Specifically, this variant is applicable when a subset of peptides in a sample are relevant to the hypothesis being asked. Finally, the last method describes MS1Connect, which measures the similarity of a pair of proteomics runs for the goal of inferring metadata of proteomics runs. Metadata is information about data. For example, given some data, metadata would include information regarding who generated the data and how the data was generated. Metadata is critical for the proper analysis of proteomics data but often it is missing or incorrect. Therefore, methods are needed that can predict metadata of proteomics data. As part of this method, we have also developed MS1Connect, a new score for measuring the similarity of a pair of mass spectrometry runs. We demonstrate that this score can be used for accurate metadata inference of species labels for mass spectrometry runs.
Download or read book Advancing Mass Spectrometry based Proteomic Analysis Strategies for the Investigation of Human Health and Disease written by Justin Morgan McKetney and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation describes advances in mass spectrometry-based analysis of the human proteome and applies the most current technologies to investigations of age, neurodegeneration and stress. Although direct protein measurements in human tissues provide valuable insight into the development and progression of disease, the complex and dynamic nature of human proteomes provides several challenges. Chapter 1 details some of these hurdles along with the basic concepts of bottom-up proteomics in terms of sample preparation and instrument operation. Chapter 2 describes regional protein signatures for nine neuroanatomically distinct regions of the aged human brain. These region-specific proteins are then compared to proteins associated with Alzheimer's disease (AD). An efficient and scalable method for proteomic analysis of AD in cerebrospinal fluid is demonstrated in Chapter 3 using an age- and sex-matched sample cohort. In Chapter 4, a proteomic analysis is performed on saliva of soldiers before and after a simulated combat training exercise in order to quantify the proteomic effects of stress. To expand the method development toolkit for proteomics of human tissues and biofluids, a machine learning model is developed for predicting peptides' transmissive compensating voltage when using high field asymmetric waveform ion mobility spectrometry (FAIMS)(Chapter 5). Conclusions and future directions for these projects, including expanded analyses and continued technological development are discussed in Chapter 6. A chapter conveying aspects of this dissertation to a general public audience is included after the conclusion (Chapter 7).
Download or read book Encyclopedia of Mass Spectrometry written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
