Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Download Computational Acceleration Of Mass Spectrometry Based Protein Identification Algorithms Using Field Programmable Gate Arrays full books in PDF, epub, and Kindle. Read online Computational Acceleration Of Mass Spectrometry Based Protein Identification Algorithms Using Field Programmable Gate Arrays ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
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:
Author : Fahad Saeed
Publisher : Springer Nature
ISBN 13 : 3031019601
Total Pages : 146 pages
Book Rating : 4.0/5 (31 download)
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.
Author : Willy Vincent Bienvenut
Publisher : Springer Science & Business Media
ISBN 13 : 9781402033186
Total Pages : 324 pages
Book Rating : 4.0/5 (331 download)
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 2005-04-19 with total page 324 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.
Author : Ganesh Naik
Publisher : Springer Nature
ISBN 13 : 9811390975
Total Pages : 432 pages
Book Rating : 4.8/5 (113 download)
Download or read book Biomedical Signal Processing written by Ganesh Naik and published by Springer Nature. This book was released on 2019-11-12 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals. It methodically collects and presents in a unified form the research findings previously scattered throughout various scientific journals and conference proceedings. In addition, the chapters are self-contained and can be read independently. Accordingly, the book will be of interest to university researchers, R&D engineers and graduate students who wish to learn the core principles of biomedical signal analysis, algorithms, and applications, while also offering a valuable reference work for biomedical engineers and clinicians who wish to learn more about the theory and recent applications of neural engineering and biomedical signal processing.
Author : Rachana Jain
Publisher :
ISBN 13 :
Total Pages : 129 pages
Book Rating : 4.:/5 (68 download)
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.
Author : Krzysztof Iniewski
Publisher : John Wiley & Sons
ISBN 13 : 1118468643
Total Pages : 314 pages
Book Rating : 4.1/5 (184 download)
Download or read book Embedded Systems written by Krzysztof Iniewski and published by John Wiley & Sons. This book was released on 2012-10-26 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers the significant embedded computing technologies highlighting their applications in wireless communication and computing power An embedded system is a computer system designed for specific control functions within a larger system often with real-time computing constraints. It is embedded as part of a complete device often including hardware and mechanical parts. Presented in three parts, Embedded Systems: Hardware, Design, and Implementation provides readers with an immersive introduction to this rapidly growing segment of the computer industry. Acknowledging the fact that embedded systems control many of today's most common devices such as smart phones, PC tablets, as well as hardware embedded in cars, TVs, and even refrigerators and heating systems, the book starts with a basic introduction to embedded computing systems. It hones in on system-on-a-chip (SoC), multiprocessor system-on-chip (MPSoC), and network-on-chip (NoC). It then covers on-chip integration of software and custom hardware accelerators, as well as fabric flexibility, custom architectures, and the multiple I/O standards that facilitate PCB integration. Next, it focuses on the technologies associated with embedded computing systems, going over the basics of field-programmable gate array (FPGA), digital signal processing (DSP) and application-specific integrated circuit (ASIC) technology, architectural support for on-chip integration of custom accelerators with processors, and O/S support for these systems. Finally, it offers full details on architecture, testability, and computer-aided design (CAD) support for embedded systems, soft processors, heterogeneous resources, and on-chip storage before concluding with coverage of software support in particular, O/S Linux. Embedded Systems: Hardware, Design, and Implementation is an ideal book for design engineers looking to optimize and reduce the size and cost of embedded system products and increase their reliability and performance.
Author : Lei Wang (Computer scientist)
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.3/5 (514 download)
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.
Author : Weiwei Tong
Publisher :
ISBN 13 :
Total Pages : 282 pages
Book Rating : 4.:/5 (474 download)
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.
Author : Julio Ng
Publisher :
ISBN 13 : 9781124703626
Total Pages : 149 pages
Book Rating : 4.7/5 (36 download)
Download or read book Computational Mass Spectrometry written by Julio Ng and published by . This book was released on 2011 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mass spectrometry has revolutionized protein identification in the last decade. Efficient algorithms have been developed to identify peptides that are encoded in protein databases. This work presents novel methods for interpretation of mass spectrometry data on compounds that are not directly encoded in protein databases. One example of compounds that are not in protein databases are nonribosomal peptides. Because of the specialized machinery that synthesizes these compounds and their unique (often cyclic) structure, traditional database search tools cannot analyze these data. With new algorithmic developments, we show that mass spectrometry can speed up the process of characterization of cyclic peptides which are compounds of great interest in drug discovery. A second class of peptides that are not encoded in protein databases are peptides that are the product of fused proteins. This type of peptides can arise from cancer proteomes, where the peptide spans a fusion point. Again, traditional search tools cannot identify fusion peptides because they are not directly encoded in the databases. We also present an algorithm to identify such peptides. Finally, mutated and modified peptides are also not directly encoded in protein databases. Existing tools are particularly inefficient when searching for unexpected modifications. Although this problem has been addressed with "blind" database search tools, their running time is too demanding to be practical for large databases. We develop new methods to search for mutated and modified peptides that are orders of magnitude faster than existing tools. Overall, with new algorithmic developments we enable mass spectrometry to characterize novel compounds that evade identification with traditional MS/MS tools.
Author : Nicholas William Kwiecien
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (11 download)
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 0 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.
Author : Bertil Schmidt
Publisher : CRC Press
ISBN 13 : 1439814899
Total Pages : 370 pages
Book Rating : 4.4/5 (398 download)
Download or read book Bioinformatics written by Bertil Schmidt and published by CRC Press. This book was released on 2010-07-15 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: New sequencing technologies have broken many experimental barriers to genome scale sequencing, leading to the extraction of huge quantities of sequence data. This expansion of biological databases established the need for new ways to harness and apply the astounding amount of available genomic information and convert it into substantive biological
Author : Bharat Sukhwani
Publisher :
ISBN 13 :
Total Pages : 522 pages
Book Rating : 4.:/5 (744 download)
Download or read book Accelerating Molecular Docking and Binding Site Mapping Using FPGAs and GPUs written by Bharat Sukhwani and published by . This book was released on 2011 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Computational accelerators such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) possess tremendous compute capabilities and are rapidly becoming viable options for effective high performance computing (HPC). In addition to their huge computational power, these architectures provide further benefits of reduced size and power dissipation. Despite their immense raw capabilities, achieving overall high performance for production HPC applications remains challenging due to programmability, lack of parallelism in existing codes, poor resource utilization, and communication overheads. In this dissertation, we present methods for the effective use of these platforms for the acceleration of two production molecular modeling applications: molecular docking and binding site mapping. Molecular docking refers to the computational prediction of the structure of the intermolecular complex formed when two independent proteins interact. Binding site mapping, on the other hand, aims at finding the region on the surface of a protein that is likely to bind a small molecule with high affinity. Docking and mapping find application in drug discovery which involves docking-based screening of millions of drug candidates for a given protein target; mapping helps identify the site on the protein where the binding is likely to occur, thus limiting the docking-based search to a small region. Both docking and binding site mapping are computationally very demanding, requiring many hours to days on a serial processor. This makes it impractical for biologists to run them interactively on their desktop computers; production docking and mapping programs typically run in batch on large clusters. In this dissertation, we present the FPGA and CPU based acceleration of the production molecular docking program PIPER and the production binding site mapping program FTMap, enabling desktop based molecular modeling solutions which are fast and cost effective as well as more power efficient. The proposed FPGA-docking algorithms achieve multi-hundred-fold speedup of the code that represents over 95% of the original run-time, resulting in 36x overall speedup for small molecule docking. For effective docking of large molecules, we propose CPU accelerated docking algorithms which result in an overall speedup of 18x. The acceleration of mapping computations on FPGAs and CPUs poses further challenges for two reasons: the process is iterative, with relatively little computation per iteration, and a large fraction of the computation is serial. We address these issues on the FPGAs by creating highly customized, deeply pipelined processors. On GPUs, we introduce two new data structures that enable effective parallelization. The result using the GPUs is 6x to 28x speedup on different parts of the algorithm, with an overall speedup for FTMap of 13x. The FPGA-accelerated algorithms obtain 42x performance improvements on the core computations, resulting in an overall speedup of 30x. Many of the proposed algorithms and hardware structures are general and can vii be applied to a variety of other applications, both in the field of molecular modeling as well as other domains such as object recognition, n-body simulations and full-field biomechanics deformation and strain-measurement.
Author : Clement Chung
Publisher :
ISBN 13 : 9780494794173
Total Pages : pages
Book Rating : 4.7/5 (941 download)
Download or read book Machine Learning Approaches to Refining Post-translational Modification Predictions and Protein Identifications from Tandem Mass Spectrometry written by Clement Chung and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Petra Gutenbrunner
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (131 download)
Download or read book Mass Spectrometry-based Computational Identification of Ancient Protein Sequences to Unravel Evolutionary History written by Petra Gutenbrunner and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Ari Michael Frank
Publisher :
ISBN 13 :
Total Pages : 205 pages
Book Rating : 4.:/5 (244 download)
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.
Author : Qiang Kou
Publisher :
ISBN 13 :
Total Pages : 198 pages
Book Rating : 4.:/5 (18 download)
Download or read book Complex Proteoform Identification Using Top-Down Mass Spectrometry written by Qiang Kou and published by . This book was released on 2018 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteoforms are distinct protein molecule forms created by variations in genes, gene expression, and other biological processes. Many proteoforms contain multiple primary structural alterations, including amino acid substitutions, terminal truncations, and posttranslational modifications. These primary structural alterations play a crucial role in determining protein functions: proteoforms from the same protein with different alterations may exhibit different functional behaviors. Because top-down mass spectrometry directly analyzes intact proteoforms and provides complete sequence information of proteoforms, it has become the method of choice for the identification of complex proteoforms. Although instruments and experimental protocols for top-down mass spectrometry have been advancing rapidly in the past several years, many computational problems in this area remain unsolved, and the development of software tools for analyzing such data is still at its very early stage. In this dissertation, we propose several novel algorithms for challenging computational problems in proteoform identification by top-down mass spectrometry. First, we present two approximate spectrum-based protein sequence filtering algorithms that quickly find a small number of candidate proteins from a large proteome database for a query mass spectrum. Second, we describe mass graph-based alignment algorithms that efficiently identify proteoforms with variable post-translational modifications and/or terminal truncations. Third, we propose a Markov chain Monte Carlo method for estimating the statistical signi ficance of identified proteoform spectrum matches. They are the first efficient algorithms that take into account three types of alterations: variable post-translational modifications, unexpected alterations, and terminal truncations in proteoform identification. As a result, they are more sensitive and powerful than other existing methods that consider only one or two of the three types of alterations. All the proposed algorithms have been incorporated into TopMG, a complete software pipeline for complex proteoform identification. Experimental results showed that TopMG significantly increases the number of identifications than other existing methods in proteome-level top-down mass spectrometry studies. TopMG will facilitate the applications of top-down mass spectrometry in many areas, such as the identification and quantification of clinically relevant proteoforms and the discovery of new proteoform biomarkers.
Author : Julie Standig Wertz
Publisher :
ISBN 13 :
Total Pages : 93 pages
Book Rating : 4.:/5 (11 download)
Download or read book Maestro written by Julie Standig Wertz and published by . This book was released on 2017 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tandem mass spectrometry has become a leading method of analyzing large-scale proteomics data, necessitating fast and accurate computational methods of interpreting mass spectrometer output to identify the contents of protein samples. A wide array of algorithms have been developed to this end--protein database searches are a common approach, but other types of searches, such as spectral library searches, and post-translational modification-based searches, may also be valuable tools. It is often advantageous to run a single dataset through multiple algorithms, given that one search algorithm may be able to identify spectra that other algorithms cannot identify, or identify certain types of spectra more quickly than other algorithms. However, combining the results from multiple searches manually is time-consuming and prone to error, and can make interpretation of the results difficult. The Maestro workflow is introduced here, which runs spectra automatically through multiple search algorithms, aggregates the results, and produces in-depth analyses and visualizations of the data. Maestro identifies a wide variety of peptides and modifications.
