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Author: Julianus Pfeuffer
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Since the beginning of this millennium, the advent of high-throughput methods in numerous fields of the life sciences led to a shift in paradigms. A broad variety of technologies emerged that allow comprehensive quantification of molecules involved in biological processes. Simultaneously, a major increase in data volume has been recorded with these techniques through enhanced instrumentation and other technical advances. By supplying computational methods that automatically process raw data to obtain biological information, the field of bioinformatics plays an increasingly important role in the analysis of the ever-growing mass of data. Computational mass spectrometry in particular, is a bioinformatics field of research which provides means to gather, analyze and visualize data from high-throughput mass spectrometric experiments. For the study of the entirety of proteins in a cell or an environmental sample, even current techniques reach limitations that need to be circumvented by simplifying the samples subjected to the mass spectrometer. These pre-digested (so-called bottom-up) proteomics experiments then pose an even bigger computational burden during analysis since complex ambiguities need to be resolved during protein inference, grouping and quantification. In this thesis, we present several developments in the pursuit of our goal to provide means for a fully automated analysis of complex and large-scale bottom-up proteomics experiments. Firstly, due to prohibitive computational complexities in state-of-the-art Bayesian protein inference techniques, a refined, more stable technique for performing inference on sums of random variables was developed to enable a variation of standard Bayesian inference for the problem. nextflow and part of a set of standardized, well-tested, and community-maintained workflows by the nf-core collective. Our workflow runs on large-scale data with complex experimental designs and allows a one-command analysis of local and publicly available data sets with state-of-the-art accuracy on various high-performance computing environments or the cloud.
Author: Julianus Pfeuffer
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Since the beginning of this millennium, the advent of high-throughput methods in numerous fields of the life sciences led to a shift in paradigms. A broad variety of technologies emerged that allow comprehensive quantification of molecules involved in biological processes. Simultaneously, a major increase in data volume has been recorded with these techniques through enhanced instrumentation and other technical advances. By supplying computational methods that automatically process raw data to obtain biological information, the field of bioinformatics plays an increasingly important role in the analysis of the ever-growing mass of data. Computational mass spectrometry in particular, is a bioinformatics field of research which provides means to gather, analyze and visualize data from high-throughput mass spectrometric experiments. For the study of the entirety of proteins in a cell or an environmental sample, even current techniques reach limitations that need to be circumvented by simplifying the samples subjected to the mass spectrometer. These pre-digested (so-called bottom-up) proteomics experiments then pose an even bigger computational burden during analysis since complex ambiguities need to be resolved during protein inference, grouping and quantification. In this thesis, we present several developments in the pursuit of our goal to provide means for a fully automated analysis of complex and large-scale bottom-up proteomics experiments. Firstly, due to prohibitive computational complexities in state-of-the-art Bayesian protein inference techniques, a refined, more stable technique for performing inference on sums of random variables was developed to enable a variation of standard Bayesian inference for the problem. nextflow and part of a set of standardized, well-tested, and community-maintained workflows by the nf-core collective. Our workflow runs on large-scale data with complex experimental designs and allows a one-command analysis of local and publicly available data sets with state-of-the-art accuracy on various high-performance computing environments or the cloud.
Author: Pavel Sinitcyn
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Ingvar Eidhammer
Publisher: John Wiley & Sons
ISBN: 111849377X
Category : Mathematics
Languages : en
Pages : 290
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Book Description
The definitive introduction to data analysis in quantitative proteomics This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistical approaches to pursue the planning, design and analysis of quantitative proteomics experiments. The author’s carefully constructed approach allows readers to easily make the transition into the field of quantitative proteomics. Through detailed descriptions of wet-lab methods, computational approaches and statistical tools, this book covers the full scope of a quantitative experiment, allowing readers to acquire new knowledge as well as acting as a useful reference work for more advanced readers. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry: Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs. Is illustrated by a large number of figures and examples as well as numerous exercises. Provides both clear and rigorous descriptions of methods and approaches. Is thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work. Features detailed discussions of both wet-lab approaches and statistical and computational methods. With clear and thorough descriptions of the various methods and approaches, this book is accessible to biologists, informaticians, and statisticians alike and is aimed at readers across the academic spectrum, from advanced undergraduate students to post doctorates entering the field.
Author: Ingvar Eidhammer
Publisher: Wiley-Interscience
ISBN: 0470724293
Category : Medical
Languages : en
Pages : 296
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Book Description
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.
Author: Ying Xu
Publisher: Springer Science & Business Media
ISBN: 0387683720
Category : Science
Languages : en
Pages : 408
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Book Description
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.
Author: Martin Vingron
Publisher: Springer Science & Business Media
ISBN: 3540788387
Category : Computers
Languages : en
Pages : 495
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Book Description
This book constitutes the refereed proceedings of the 12th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2008. It presents current issues in algorithmic, theoretical, and experimental bioinformatics.
Author: Conrad Bessant
Publisher: Royal Society of Chemistry
ISBN: 1782626735
Category : Science
Languages : en
Pages : 429
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Book Description
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.
Author: Lei Wang (Computer scientist)
Publisher:
ISBN:
Category : Machine learning
Languages : en
Pages : 0
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Book Description
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: Frédérique Lisacek
Publisher: Humana
ISBN: 9781071640067
Category : Computers
Languages : en
Pages : 0
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Book Description
This detailed volume explores techniques for protein bioinformatics research, including databases, software tools, and computational methods, in the context of protein science or proteomics and opening to other omics areas. Beginning with a section on proteogenomics, the book continues by covering posttranslational modifications, processing large-scale mass spectrometry data, protein structure and interactions, as well as protein feature inference. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice to ensure efficacious results. Authoritative and practical, Protein Bioinformatics serves as an ideal guide for researchers in disciplines encompassing the biotechnological, pharmaceutical, biological, and medical sciences, as well as the computational and engineering sciences.
Author: Hamid Mirzaei
Publisher: Springer
ISBN: 3319414488
Category : Science
Languages : en
Pages : 525
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Book Description
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.
