Popular Bioinformatics Books for Understanding Biological Data

Posted on 10-26-2017 by onlinebooksreview


Bioinformatics is the application of computer science and information technology to the field of biology and medicine. Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. read more Here your will get some books of best bioinformatics books to learn ins and out.

Bioinformatics For Dummies

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.

XML for Bioinformatics

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.


BIOINFORMATICS ALGORITHMS,VOL.II

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.


Introduction to Bioinformatics

An Introduction to Bioinformatics introduces students to the immense power of bioinformatics as a set of scientific tools. The book explains how to access the data archives of genomes and proteins, and the kinds of questions these data and tools can answer, such as how to make inferences from data archives and how to make connections among them to derive useful and interesting predictions.

Blending factual content with many opportunities for active learning, An Introduction to Bioinformatics offers a truly reader-friendly way to get to grips with this subject, making it the ideal resource for anyone new to the field.

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.

Beginning Perl for Bioinformatics

Biology, it seems, is a good showcase for the talents of Perl. Newcomers to Perl who understand biological information will find James Tisdall's Beginning Perl for Bioinformatics to be an excellent compendium of examples. Teachers of Perl will likewise find the text to be filled with fresh programming illustrations of growing scientific importance. Seasoned Perlmongers who want to learn biology, however, should search elsewhere, as Tisdall's emphasis is on Perl's logic rather than Mother Nature's.

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.


Building Bioinformatics Solutions: with Perl, R and MySQL

Modern bioinformatics encompasses a broad and ever-changing range of activities involved with the management and analysis of data from molecular biology experiments. Despite the diversity of activities and applications, the basic methodology and core tools needed to tackle bioinformatics problems is common to many projects.

Building Bioinformatics Solutions provides a comprehensive introduction to this methodology, explaining how to acquire and use the most popular development tools, how to apply them to build processing pipelines, and how to make the results available through visualizations and web-based services for deployment either locally or via the Internet.

The methodologies introduced are platform independent, and all the examples that feature have been tested on Windows, Linux and Mac OS.

This advanced textbook is suitable for graduate students and researchers in the life sciences who wish to automate analyses or create their own databases and web-based tools. No prior knowledge of software development is assumed. Having worked through the book, the reader should have the necessary core skills to develop computational solutions for their specific research programmers. 

Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools

This practical book teaches the skills that scientists need for turning large sequencing datasets into reproducible and robust biological findings. Many biologists begin their bioinformatics training by learning scripting languages like Python and R alongside the Unix command line. But there's a huge gap between knowing a few programming languages and being prepared to analyze large amounts of biological data.

What You Will Learn 

  • 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.

Bioinformatics with Python Cookbook

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.


Bioinformatics with R Cookbook

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.

Bioinformatics: A Practical Handbook of Next Generation Sequencing and Its Applications

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.