Download or read book Computational Methods to Improve and Validate Peptide Identifications in Proteomics written by Lei Wang (Computer scientist) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid development of mass spectrometry technology in the past decade and the recent large-scale proteomics projects, massive and highly redundant tandem mass spectra (MS/MS) are being generated at an unprecedented speed. Hundreds of publications have been made for proteomics studies, yet computational methods which can efficiently identify and analyze the sheer amount of proteomic MS/MS data are still outstanding. The thesis aims to provide systematic approaches to studying MS/MS data from three aspects: spectral clustering, spectral library searching and validation of peptide-spectrum matchings (PSMs).I first introduce a rapid algorithm accelerated by Locality Sensitive Hashing (LSH) techniques to reduce the redundancy in proteomics datasets via clustering similar spectra. The proposed method demonstrates 7-11X performance improvement in running time while retaining superior sensitivity and accuracy when compared to the state of the art spectral clustering algorithms. In addition to the reduction of repetition of similar spectra, the time to search protein database, i.e. a commonly used technique for peptide identification, can be greatly shortened when using the consensus spectra that usually exhibit higher quality than the raw spectra. As a result, It can be demonstrated that more peptide identifications were obtained at the same low false discovery rate (FDR).The second chapter delves into spectral library searching, a complementary approach to database searching for peptide identifications on MS/MS spectra. LSH techniques ensure that similar spectra are placed into the same buckets, whereas spectra with low pairwise similarity are scattered into different buckets. Each input experimental spectrum can then be compared against a subset of highly similar spectra, thus diminishing the unnecessary spectral similarity computation between the input spectrum and all possible combinations of candidate peptides. The identified peptides overlap with those reported by other existing algorithms to a great extent. More importantly, the acceleration rate in the running time of proposed algorithm compared to existing ones increases with the growing size of spectral libraries.Redundancy in large scale proteomic datasets are exploited to further improve the searching results by eliminating the false PSMs examined through a post-processing step. Despite the success of data searching algorithms in proteomics, the peptide identification results usually contain a small fraction of incorrect peptide assignments. Target decoy approach was introduced in previous work to assess the quality of identifications, by searching spectrum against both target and decoy sequences. I formalize the method to improve peptide identifications by removing false PSMs in a probabilistic post-processing approach. As a result, as low as 0.8\\% FDR can be obtained on the remaining PSMs previously reported at 1\\% FDR level and up to 38\\% more unique peptides can be reported at the expected FDR level.I anticipate the computational methods developed in the dissertation can advance the proteomics research field by improving the protein identification through database searching, spectral library searching and validating the searching outputs in a subsequent step. Although the algorithms were evaluated for proteomics studies, they can be extended to small molecules such as natural products, lipids and glycoconjugates. These algorithms can also be generalized to the identification of experimental MS/MS spectra from a molecule of specific interest in massive omic datasets.
Download or read book Computational Methods for Mass Spectrometry Proteomics written by Ingvar Eidhammer and published by Wiley-Interscience. 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.
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.
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 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 Protein Structure Analysis written by Roza Maria Kamp and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Protein Structure Analysis - Preparation and Characterization" is a compilation of practical approaches to the structural analysis of proteins and peptides. Here, about 20 authors describe and comment on techniques for sensitive protein purification and analysis. These methods are used worldwide in biochemical and biotechnical research currently being carried out in pharmaceu tical and biomedical laboratories or protein sequencing facilities. The chapters have been written by scientists with extensive ex perience in these fields, and the practical parts are well documen ted so that the reader should be able to easily reproduce the described techniques. The methods compiled in this book were demonstrated in student courses and in the EMBO Practical Course on "Microsequence Analysis of Proteins" held in Berlin September 10-15, 1995. The topics also derived from a FEBS Workshop, held in Halkidiki, Thessaloniki, Greece, in April, 1995. Most of the authors participated in these courses as lecturers and tutors and made these courses extremely lively and successful. Since polypeptides greatly vary depending on their specific structure and function, strategies for their structural analysis must for the most part be adapted to each individual protein. Therefore, advantages and limitations of the experimen tal approaches are discussed here critically, so that the reader becomes familiar with problems that might be encountered.
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.
Download or read book Informatics In Proteomics written by Sudhir Srivastava and published by CRC Press. This book was released on 2005-06-24 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: The handling and analysis of data generated by proteomics investigations represent a challenge for computer scientists, biostatisticians, and biologists to develop tools for storing, retrieving, visualizing, and analyzing genomic data. Informatics in Proteomics examines the ongoing advances in the application of bioinformatics to proteomics researc
Download or read book Computational Methods for Protein Structure Prediction and Modeling written by Ying Xu and published by Springer Science & Business Media. This book was released on 2007-08-24 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume One of this two-volume sequence focuses on the basic characterization of known protein structures, and structure prediction from protein sequence information. Eleven chapters survey of the field, covering key topics in modeling, force fields, classification, computational methods, and structure prediction. Each chapter is a self contained review covering definition of the problem and historical perspective; mathematical formulation; computational methods and algorithms; performance results; existing software; strengths, pitfalls, challenges, and future research.
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 ClassCleaner written by Melissa C. Key and published by . This book was released on 2020 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics infers the peptide sequence of each measurement, there is inherent uncertainty in the identity of each peptide and its originating protein. Removing misidentified peptides can improve the accuracy and power of downstream analyses when differences between proteins are of primary interest. In this dissertation I present classCleaner, a novel algorithm designed to identify misidentified peptides from each protein using the available quantitative data. The algorithm is based on the idea that distances between peptides belonging to the same protein are stochastically smaller than those between peptides in different proteins. The method first determines a threshold based on the estimated distribution of these two groups of distances. This is used to create a decision rule for each peptide based on counting the number of within-protein distances smaller than the threshold. Using simulated data, I show that classCleaner always reduces the proportion of misidentified peptides, with better results for larger proteins (by number of constituent peptides), smaller inherent misidentification rates, and larger sample sizes. ClassCleaner is also applied to a LC-MS/MS proteomics data set and the Congressional Voting Records data set from the UCI machine learning repository. The later is used to demonstrate that the algorithm is not specific to proteomics.
Download or read book Enabling Community driven Proteomics written by Damon May and published by . This book was released on 2018 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the field of proteomics matures, it faces several computational challenges. This dis- sertation describes three new computational methods to address some of these challenges: Metapeptides, Param-Medic and GLEAMS. Metapeptides constructs a database from site-specific metagenomic sequencing of micro- bial community samples to facilitate identification of mass spectra. Even massive public databases offer incomplete coverage of a given microbial community sample. Metaproteomes assembled from site-specific metagenomic sequencing offer better coverage but fail to include all the variability present in sequencing data. Metapeptides constructs a small, sample- targeted peptide database optimized for database search, offering superior sequence coverage and providing a dramatic boost to metaproteomic database search sensitivity at a controlled false discovery rate (FDR). Param-Medic infers optimal database search parameters directly from mass spectrometry data. Tight precursor and fragment mass tolerances can increase database search sensitivity at a given FDR. However, too-tight tolerances reduce sensitivity by improperly excluding match candidates and lowering match scores. Param-Medic infers optimal precursor and fragment tolerances by analyzing pairs of acquired spectra that are likely to have been generated by the same peptide ion, yielding more high confidence identifications at a given FDR than tolerances based on per-instrument best practice or even determined by experts. GLEAMS embeds mass spectra into a low-dimensional space in which spectra generated by the same peptide are close together, enabling rapid propagation of sequence identifications among communities of nearby spectra. Public proteomics repositories contain billions of spectra from researchers around the world, but traditional data analysis workflows fail to take advantage of those data. GLEAMS detects communities of spectra that represent the same peptide. Identifications can be propagated from identified to unidentified spectra, and unidentified communities can then be characterized by targeted downstream analysis. GLEAMS enables identification of 8% more spectra in a sample repository of five million spectra at low computational expense. Scaled up to an entire public repository, GLEAMS offers an efficient, community-driven approach to proteomics data analysis.
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.
Download or read book Activity Based Protein Profiling written by Benjamin F. Cravatt and published by Springer. This book was released on 2019-01-25 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides a collection of contemporary perspectives on using activity-based protein profiling (ABPP) for biological discoveries in protein science, microbiology, and immunology. A common theme throughout is the special utility of ABPP to interrogate protein function and small-molecule interactions on a global scale in native biological systems. Each chapter showcases distinct advantages of ABPP applied to diverse protein classes and biological systems. As such, the book offers readers valuable insights into the basic principles of ABPP technology and how to apply this approach to biological questions ranging from the study of post-translational modifications to targeting bacterial effectors in host-pathogen interactions.
Download or read book Modern Proteomics Sample Preparation Analysis and Practical Applications written by Hamid Mirzaei and published by Springer. This book was released on 2016-12-14 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume serves as a proteomics reference manual, describing experimental design and execution. The book also shows a large number of examples as to what can be achieved using proteomics techniques. As a relatively young area of scientific research, the breadth and depth of the current state of the art in proteomics might not be obvious to all potential users. There are various books and review articles that cover certain aspects of proteomics but they often lack technical details. Subject specific literature also lacks the broad overviews that are needed to design an experiment in which all steps are compatible and coherent. The objective of this book was to create a proteomics manual to provide scientists who are not experts in the field with an overview of: 1. The types of samples can be analyzed by mass spectrometry for proteomics analysis. 2. Ways to convert biological or ecological samples to analytes ready for mass spectral analysis. 3. Ways to reduce the complexity of the proteome to achieve better coverage of the constituent proteins. 4. How various mass spectrometers work and different ways they can be used for proteomics analysis 5. The various platforms that are available for proteomics data analysis 6. The various applications of proteomics technologies in biological and medical sciences This book should appeal to anyone with an interest in proteomics technologies, proteomics related bioinformatics and proteomics data generation and interpretation. With the broad setup and chapters written by experts in the field, there is information that is valuable for students as well as for researchers who are looking for a hands on introduction into the strengths, weaknesses and opportunities of proteomics.
Download or read book Proteomic Profiling and Analytical Chemistry written by Pawel Ciborowski and published by Elsevier. This book was released on 2016-03-02 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteomic Profiling and Analytical Chemistry: The Crossroads, Second Edition helps scientists without a strong background in analytical chemistry to understand principles of the multistep proteomic experiment necessary for its successful completion. It also helps researchers who do have an analytical chemistry background to break into the proteomics field. Highlighting points of junction between proteomics and analytical chemistry, this resource links experimental design with analytical measurements, data analysis, and quality control. This targeted point of view will help both biologists and chemists to better understand all components of a complex proteomic study. The book provides detailed coverage of experimental aspects such as sample preparation, protein extraction and precipitation, gel electrophoresis, microarrays, dynamics of fluorescent dyes, and more. The key feature of this book is a direct link between multistep proteomic strategy and quality control routinely applied in analytical chemistry. This second edition features a new chapter on SWATH-MS, substantial updates to all chapters, including proteomic database search and analytical quantification, expanded discussion of post-hoc statistical tests, and additional content on validation in proteomics. Covers the analytical consequences of protein and peptide modifications that may have a profound effect on how and what researchers actually measure Includes practical examples illustrating the importance of problems in quantitation and validation of biomarkers Helps in designing and executing proteomic experiments with sound analytics
Download or read book The Proteomics Protocols Handbook written by John M. Walker and published by Springer Science & Business Media. This book was released on 2007-10-09 with total page 969 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on researchers describe in step-by-step detail 73 proven laboratory methods and bioinformatics tools essential for analysis of the proteome. These cutting-edge techniques address such important tasks as sample preparation, 2D-PAGE, gel staining, mass spectrometry, and post-translational modification. There are also readily reproducible methods for protein expression profiling, identifying protein-protein interactions, and protein chip technology, as well as a range of newly developed methodologies for determining the structure and function of a protein. The bioinformatics tools include those for analyzing 2D-GEL patterns, protein modeling, and protein identification. All laboratory-based protocols follow the successful Methods in Molecular BiologyTM series format, each offering step-by-step laboratory instructions, an introduction outlining the principle behind the technique, lists of the necessary equipment and reagents, and tips on troubleshooting and avoiding known pitfalls.
