Introduction to Computational Proteomics PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Introduction to Computational Proteomics PDF full book. Access full book title Introduction to Computational Proteomics by Golan Yona. Download full books in PDF and EPUB format.
Author: Golan Yona
Publisher: CRC Press
ISBN: 1000738272
Category : Mathematics
Languages : en
Pages : 643
Get Book Here
Book Description
Introduction to Computational Proteomics introduces the field of computational biology through a focused approach that tackles the different steps and problems involved with protein analysis, classification, and meta-organization. The book starts with the analysis of individual entities and works its way through the analysis of more complex entitie
Author: Golan Yona
Publisher: CRC Press
ISBN: 1000738272
Category : Mathematics
Languages : en
Pages : 643
Get Book Here
Book Description
Introduction to Computational Proteomics introduces the field of computational biology through a focused approach that tackles the different steps and problems involved with protein analysis, classification, and meta-organization. The book starts with the analysis of individual entities and works its way through the analysis of more complex entitie
Author: Anna Tramontano
Publisher: CRC Press
ISBN: 1420010883
Category : Science
Languages : en
Pages : 193
Get Book Here
Book Description
Guiding readers from the elucidation and analysis of a genomic sequence to the prediction of a protein structure and the identification of the molecular function, Introduction to Bioinformatics describes the rationale and limitations of the bioinformatics methods and tools that can help solve biological problems. Requiring only a limited mathematical and statistical background, the book shows how to efficiently apply these approaches to biological data and evaluate the resulting information. The author, an expert bioinformatics researcher, first addresses the ways of storing and retrieving the enormous amount of biological data produced every day and the methods of decrypting the information encoded by a genome. She then covers the tools that can detect and exploit the evolutionary and functional relationships among biological elements. Subsequent chapters illustrate how to predict the three-dimensional structure of a protein. The book concludes with a discussion of the future of bioinformatics. Even though the future will undoubtedly offer new tools for tackling problems, most of the fundamental aspects of bioinformatics will not change. This resource provides the essential information to understand bioinformatics methods, ultimately facilitating in the solution of biological problems.
Author: Altuna Akalin
Publisher: CRC Press
ISBN: 1498781861
Category : Mathematics
Languages : en
Pages : 463
Get Book Here
Book Description
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
Author: Ingvar Eidhammer
Publisher: John Wiley & Sons
ISBN: 111849377X
Category : Mathematics
Languages : en
Pages : 290
Get Book Here
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: Röbbe Wünschiers
Publisher: Springer Science & Business Media
ISBN: 3642347495
Category : Science
Languages : en
Pages : 453
Get Book Here
Book Description
This greatly expanded 2nd edition provides a practical introduction to - data processing with Linux tools and the programming languages AWK and Perl - data management with the relational database system MySQL, and - data analysis and visualization with the statistical computing environment R for students and practitioners in the life sciences. Although written for beginners, experienced researchers in areas involving bioinformatics and computational biology may benefit from numerous tips and tricks that help to process, filter and format large datasets. Learning by doing is the basic concept of this book. Worked examples illustrate how to employ data processing and analysis techniques, e.g. for - finding proteins potentially causing pathogenicity in bacteria, - supporting the significance of BLAST with homology modeling, or - detecting candidate proteins that may be redox-regulated, on the basis of their structure. All the software tools and datasets used are freely available. One section is devoted to explaining setup and maintenance of Linux as an operating system independent virtual machine. The author's experiences and knowledge gained from working and teaching in both academia and industry constitute the foundation for this practical approach.
Author: Susmita Datta
Publisher: Springer
ISBN: 3319458094
Category : Medical
Languages : en
Pages : 294
Get Book Here
Book Description
This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.
Author: Daniel Liebler
Publisher: Springer Science & Business Media
ISBN: 0896039919
Category : Medical
Languages : en
Pages : 210
Get Book Here
Book Description
Daniel C. Liebler masterfully introduces the science of proteomics by spelling out the basics of how one analyzes proteins and proteomes, and just how these approaches are then employed to investigate their roles in living systems. He explains the key concepts of proteomics, how the analytical instrumentation works, what data mining and other software tools do, and how these tools can be integrated to study proteomes. Also discussed are how protein and peptide separation techniques are applied in proteomics, how mass spectrometry is used to identify proteins, and how data analysis software enables protein identification and the mapping of modifications. In addition, there are proteomic approaches for analyzing differential protein expression, characterizing proteomic diversity, and dissecting protein-protein interactions and networks.
Author: C. Stan Tsai
Publisher: John Wiley & Sons
ISBN: 0471461091
Category : Science
Languages : en
Pages : 381
Get Book Here
Book Description
This comprehensive text offers a solid introduction to the biochemical principles and skills required for any researcher applying computational tools to practical problems in biochemistry. Each chapter includes an introduction to the topic, a review of the biological concepts involved, a discussion of the programming and applications used, key references, and problem sets and answers. Providing detailed coverage of biochemical structures, enzyme reactions, metabolic simulation, genomic and proteomic analyses, and molecular modeling, this is the perfect resource for students and researchers in biochemistry, bioinformatics, bioengineering and computational science.
Author: Nello Cristianini
Publisher: Cambridge University Press
ISBN: 9780521856034
Category : Computers
Languages : en
Pages : 200
Get Book Here
Book Description
Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use their internal clock? The genomic revolution in biology enables us to answer such questions. But the revolution would have been impossible without the support of powerful computational and statistical methods that enable us to exploit genomic data. Many universities are introducing courses to train the next generation of bioinformaticians: biologists fluent in mathematics and computer science, and data analysts familiar with biology. This readable and entertaining book, based on successful taught courses, provides a roadmap to navigate entry to this field. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden Markov models, gene and motif finding and more, are introduced in a rigorous yet accessible way. A companion website provides the reader with Matlab-related software tools for reproducing the steps demonstrated in the book.
Author: Xuhua Xia
Publisher: Springer Science & Business Media
ISBN:
Category : Science
Languages : en
Pages : 376
Get Book Here
Book Description
Biological and biomedical sciences are becoming more interdisciplinary, and scientists of the future need inte rdisciplinary training instead of the conventional disciplinary training. Just as Sean Eddy (2005) wiselypointed out that sending monolingual diplomats to the United Nations maynot enhance international collaborations, combining strictly disciplinary scientists trained in either mathematics, computational science or molecular biology will not create a productive inte rdisciplinary team ready to solve interdisciplinary problems. Molecular biology is an interdiscip linary science back in its heyday, and founders of molecular biology were ofte n interdisciplinary scientists. Indeed, Francis Crick considered himself as “a mixture of crystallographer, biophysicist, biochemist, and geneticist” (Crick, 1965). Because it was too cumbersome to explain to people that he was such a mixture, the term “molecular biologist” came handy. To get the crystallographer, biophysicist, biochemist, and geneticist within hi mself to collaborate with each other probably worked better than a team with a crystallographer, a biophysicist, a biochemist and a geneticist who maynot even be interested in each other’s problems.
