Popular Bioinformatics Books for Understanding Biological Data
Bioinformatics For Dummies is packed with valuable information that introduces you to this exciting new discipline. This easy-to-follow guide leads you step by step through every bioinformatics task that can be done over the Internet. Forget long equations, computer-geek gibberish, and installing bulky programs that slow down your computer. You’ll be amazed at all the things you can accomplish just by logging on and following these trusty directions.
What You Will Learn
- Analyze all types of sequences
- Use all types of databases
- Work with DNA and protein sequences
- Conduct similarity searches
- Build a multiple sequence alignment
- Edit and publish alignments
- Visualize protein 3-D structures
- Construct phylogenetic trees.
Introduction The goal of this book is to introduce XML to a bioinformatics audience. It does so by introducing the fundamentals of XML, Document Type De?nitions (DTDs), XML Namespaces, XML Schema, and XML parsing, and illustrating these concepts with speci?c bioinformatics case studies.
The book does not assume any previous knowledge of XML and is geared toward those who want a solid introduction to fundamental XML concepts. The book is divided into nine chapters.
Chapter 1: Introduction to XML for Bioinformatics. This chapter provides an introduction to XML and describes the use of XML in biological data exchange. A bird’s-eye view of our ?rst case study, the Distributed Annotation System (DAS), is provided and we examine a sample DAS XML document. The chapter concludes with a discussion of the pros and cons of using XML in bioinformatic applications.
Chapter 2: Fundamentals of XML and BSML. This chapter introduces the fundamental concepts of XML and the Bioinformatic Sequence Markup Language (BSML). We explore the origins of XML, de?ne basic rules for XML document structure, and introduce XML Na- spaces. We also explore several sample BSML documents and visualize these documents in the TM Rescentris Genomic Workspace Viewer.
This is Vol. 2 of Bioinformatics Algorithms: an Active Learning Approach, one of the first textbooks to emerge from the recent Massive Open Online Course (MOOC) revolution. A light-hearted and analogy-filled companion to the authors' acclaimed Bioinformatics Specialization on Coursera, 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 both biology and computer science. Each chapter begins with a 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 the Rosalind Bioinformatics Textbook.
What You Will Learn
- Strikes a careful balance between biology and computer science, introducing those aspects of computer science which underpin the subject without demanding detailed prior knowledge.
- Contains numerous learning features, including exercises, problems, and WebLems.
- Retains the eloquent style and clarity of explanation for which the author is renowned.
- An Online Resource Centre includes figures from the book available to download to facilitate lecture preparation, as well as a variety of interactive resources, including web links, 3D structures, and data sets
- New chapter on biological organization in space and reflects recent advances in genomics, transcriptomics, proteomics, and metabolomics.
- New chapter on scientific publications and archives provides a state of the art inventory on sourcing scientific literature.
- Expanded coverage of structural bioinformatics.
- Enhanced Online Resource Centre, with new guided tours of key websites, and lab assignments to support the in-depth exploration of concepts and themes covered in the book.
Departing from O'Reilly's earlier monograph Developing Bioinformatic Computer Skills, Tisdall's text is organized aggressively along didactic lines. Nearly all of the 13 chapters begin with twin bullet lists of Perl programming tools and the bioinformatic methods that require them. Likewise, the chapters end with exercises. String concatenation is illustrated with gene splicing, and regular expressions are taught with gene transcription and motif searching.
Although he introduces bioinformatics as an academic discipline, Tisdall treats it as a trade throughout his book. He indicates that open questions and computational hard problems exist, but does not describe what they are or how they are being tackled. Ultimately, Tisdall presents bioinformatics as another arrow in a bench scientist's quiver, very much like HPLC, 2D-PAGE, and the various spectroscopies.
- Go from handling small problems with messy scripts to tackling large problems with clever methods and tools
- Focus on high-throughput (or "next generation") sequencing data
- Learn data analysis with modern methods, versus covering older theoretical concepts
- Understand how to choose and implement the best tool for the job
- Delve into methods that lead to easier, more reproducible, and robust bioinformatics analysis.
About This Book
- Discover and learn the most important Python libraries and applications to do a complex bioinformatics analysis
- Focuses on the most modern tools to do research with next generation sequencing, genomics, population genetics, phylogenomics, and proteomics
- Uses real-world examples and teaches you to implement high-impact research methods
What You Will Learn
- Gain a deep understanding of Python's fundamental bioinformatics libraries and be exposed to the most important data science tools in Python
- Process genome-wide data with Biopython
- Analyze and perform quality control on next-generation sequencing datasets using libraries such as PyVCF or PySAM
- Use DendroPy and Biopython for phylogenetic analysis
- Perform population genetics analysis on large datasets
- Simulate complex demographies and genomic features with simuPOP.
About This Book
- Use the existing R-packages to handle biological data
- Represent biological data with attractive visualizations
- An easy-to-follow guide to handle real-life problems in Bioinformatics like Next Generation Sequencing and Microarray Analysis
What You Will Learn
- Retrieve biological data from within an R environment without hassling web pages
- Annotate and enrich your data and convert the identifiers
- Find relevant text from PubMed on which to perform text mining
- Find phylogenetic relations between species
- Infer relations between genomic content and diseases via GWAS
- Classify patients based on biological or clinical features
- Represent biological data with attractive visualizations, useful for publications and presentations.
Rapid technological developments have led to increasingly efficient sequencing approaches. Next Generation Sequencing (NGS) is increasingly common and has become cost-effective, generating an explosion of sequenced data that need to be analyzed.
The skills required to apply computational analysis to target research on a wide range of applications that include identifying causes of cancer, vaccine design, new antibiotics, drug development, personalized medicine and higher crop yields in agriculture are highly sought after.
This invaluable book provides step-by-step guides to complex topics that make it easy for readers to perform essential analyses from raw sequenced data to answering important biological questions. It is an excellent hands-on material for teachers who conduct courses in bioinformatics and as a reference material for professionals.
The chapters are written to be standalone recipes making it suitable for readers who wish to self-learn selected topics. Readers will gain skills necessary to work on sequenced data from NGS platforms and hence making themselves more attractive to employers who need skilled bioinformaticians to handle the deluge of data.