Python for Bioinformatics

EBook Description: Programming knowledge is often necessary for finding a solution to a biological problem. Based on the author�s experience working for an agricultural biotechnology company, Python for Bioinformatics helps scientists solve their biological problems by helping them understand the basics of programming. Requiring no prior knowledge of programming-related concepts, the book focuses on the easy-to-use, yet powerful, Python computer language. The book begins with a very basic introduction that teaches the principles of programming. It then introduces the Biopython package, which can be useful in solving life science problems. The next section covers sophisticated tools for bioinformatics, including relational database management systems and XML. The last part illustrates applications with source code, such as sequence manipulation, filtering vector contamination, calculating DNA melting temperature, parsing a genbank file, inferring splicing sites, and more. The appendices provide a wealth of supplementary information, including instructions for installing Python and Biopython and a Python language and style guide. By incorporating examples in biology as well as code fragments throughout, the author places a special emphasis on practice, encouraging readers to experiment with the code. He shows how to use Python and the Biopython package for building web applications, genomic annotation, data manipulation, and countless other applications.

This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis.

�Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics
�Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences
�Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis
�Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis
�Offers programming examples and datasets
�Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material
�Features supplementary materials, including datasets, links, and a statistical package available online
Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.

Systems Bioinformatics - An Engineering

This trail-blazing work introduces a quantitative systems approach to bioinformatics research using powerful computational tools drawn from signal processing, circuit analysis, control systems, and communications. It presents the functionality of biological processes in an engineering context to facilitate the application of technical skills in solving the field's challenges, from the lab bench to data analysis and modeling, and to enable reverse engineering from biology in the development of synthetic biological devices.

This first-of-its-kind text explores how the knowledge bases of various technical disciplines relate to, and are observed, in biological systems. You learn fundamental signal processing techniques that are essential to biological data analysis, including biomedical imaging and image processing, feature extraction, classification, and estimation. You gain a thorough understanding of cellular regulatory systems and their similarities to traditional control systems, protein and gene networks, inference networks, and network dynamics. The book also addresses how biology-inspired molecular structures are being used to solve engineering challenges, and how one can mimic biology's designs in creating more robust technologies. Moreover, you discover the latest developments in proteomics, where these tools can make an immense impact due to the number, complexity, and interaction networks of proteins. A major addition under the evolving umbrella of systems biology and bioinformatics, this groundbreaking work points you to new frontiers in the convergence of engineering and biological research.

Essential Bioinformatics

Essential Bioinformatics is a concise yet comprehensive textbook of bioinformatics, which provides a broad introduction to the entire field. Written specifically for a life science audience, the basics of bioinformatics are explained, followed by discussions of the state-of-the-art computational tools available to solve biological research problems. All key areas of bioinformatics are covered including biological databases, sequence alignment, genes and promoter prediction, molecular phylogenetics, structural bioinformatics, genomics and proteomics. The book emphasizes how computational methods work and compares the strengths and weaknesses of different methods. This balanced yet easily accessible text will be invaluable to students who do not have sophisticated computational backgrounds. Technical details of computational algorithms are explained with a minimum use of mathematical formulae; graphical illustrations are used in their place to aid understanding. The effective synthesis of existing literature as well as in-depth and up-to-date coverage of all key topics in bioinformatics make this an ideal textbook for all bioinformatics courses taken by life science students and for researchers wishing to develop their knowledge of bioinformatics to facilitate their own research.

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