With The Flood Of Information Originating From Genome Sequencing Projects, Biology Is Being Transformed From A Laboratory-Based Science Into An Information Science. Now, A Stage Has Been Reached Where Students And Scholars Of Biology Cannot Study Or Carry Out Research In Biology Without Using The Tools Of Computers And Bioinformatics Tools Which An Ordinary Biologist May Not Be Proficient In.

Powerful, flexible, and easy to use, Python is an ideal language for building software tools and applications for life science research and development. This unique book shows you how to program with Python, using code examples taken directly from bioinformatics. In a short time, you'll be using sophisticated techniques and Python modules that are particularly effective for bioinformatics programming. Bioinformatics Programming Using Python is perfect for anyone involved with bioinformatics -- researchers, support staff, students, and software developers interested in writing bioinformatics applications. You'll find it useful whether you already use Python, write code in another language, or have no programming experience at all. It's an excellent self-instruction tool, as well as a handy reference when facing the challenges of real-life programming tasks. Become familiar with Python's fundamentals, including ways to develop simple applications Learn how to use Python modules for pattern matching, structured text processing, online data retrieval, and database access Discover generalized patterns that cover a large proportion of how Python code is used in bioinformatics Learn how to apply the principles and techniques of object-oriented programming Benefit from the "tips and traps" section in each chapter

Are you a seasoned Java developer who wishes to learn Python? Perhaps you’ve just joined a project where a chunk of system integration code is written in Python. Or maybe you need to implement a report generation module in the next sprint and your colleague mentioned that Python would be the perfect tool for the job. In any case, if you are in a situation where you have to pick up the Python programming language overnight, this book is just for you! Hit the ground running and gain a fast-paced overview of what the Python language is all about, the syntax that it uses and the ecosystem of libraries and tools that surround the language. This concise book doesn’t spend time on details from an introductory programming course or document every single Python feature. Instead, Python for the Busy Java Developer is designed for experienced Java developers to obtain sufficient familiarity with the language and dive into coding, quickly. What You'll Learn Discover the fundamentals of the core Python language and how they compare to Java Understand Python syntax and the differences between Python 2.x and 3.x Explore the Python ecosystem, its standard libraries, and how to implement them Who This Book Is For Working programmers who are comfortable with Java or another object-oriented programming language such as C#

The book is a ready reckoner aimed at the student community aspiring to take up a career in bioinformatics. The book firstly provides a perspective on the domain and addresses the challenges faced by community namely the attempts to understand data produced by genome sequencing projects. It then brings to light High Performance Computing (HPC) as it helps in interpreting and analyzing genome sequences. The book also dwells on how interactions in a systems (organism), the components that interact with each other and the outcome of such interactions. It then calls for a consensus on the tools like rapid and inexpensive DNA sequencing technologies, HAPMAP projects, Dollar One Genome (DOG), to enable a reader understand how bioinformatics transits from research, to vocation and avocation. Further it extols the virtues of in silico for bioinformatical predictions as it helps wet-lab biologists reduce time for experiments. Also it describes the intricacies of bioinformatics and its usefulness to wet-based biologists and other cross-disciplinarians. The book lists out 10 reasons for taking up bioinformatics as a career, and includes insights from global experts on the domain. It also makes a case for a mediocre student getting into bioinformatics with discipline, determination, dynamism and diligence. The book further describes BioinformaTICKS a tool for emerging as a winner in bioinformatics.

Collaborative research in bioinformatics and systems biology is a key element of modern biology and health research. This book highlights and provides access to many of the methods, environments, results and resources involved, including integral laboratory data generation and experimentation and clinical activities. Collaborative projects embody a research paradigm that connects many of the top scientists, institutions, their resources and research worldwide, resulting in first-class contributions to bioinformatics and systems biology. Central themes include describing processes and results in collaborative research projects using computational biology and providing a guide for researchers to access them. The book is also a practical guide on how science is managed. It shows how collaborative researchers are putting results together in a way accessible to the entire biomedical community.

Explains how to write code and discusses topics including Web servers, databases, polymorphism, and the Java Virtual Machine.

Bioinformatics Algorithms: an Active Learning Approach is one of the first textbooks to emerge from the recent Massive Online Open Course (MOOC) revolution. A light-hearted and analogy-filled companion to the authors' acclaimed online course (http://coursera.org/course/bioinformatics), this book presents students with a dynamic approach to learning bioinformatics. It strikes a unique balance between practical challenges in modern biology and fundamental algorithmic ideas, thus capturing the interest of students of biology and computer science students alike.Each chapter begins with a central biological question, such as "Are There Fragile Regions in the Human Genome?" or "Which DNA Patterns Play the Role of Molecular Clocks?" and then steadily develops the algorithmic sophistication required to answer this question. Hundreds of exercises are incorporated directly into the text as soon as they are needed; readers can test their knowledge through automated coding challenges on Rosalind (http://rosalind.info), an online platform for learning bioinformatics.The textbook website (http://bioinformaticsalgorithms.org) directs readers toward additional educational materials, including video lectures and PowerPoint slides.