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.
Rather than teach bioinformatics as a set of workflows that are likely to change with this rapidly evolving field, this book demsonstrates the practice of bioinformatics through data skills. Rigorous assessment of data quality and of the effectiveness of tools is the foundation of reproducible and robust bioinformatics analysis. Through open source and freely available tools, you’ll learn not only how to do bioinformatics, but how to approach problems as a bioinformatician.

• 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

Table of Contents

Part I. Ideology: Data Skills for Robust and Reproducible Bioinformatics
Chapter 1. How to Learn Bioinformatics

Part II. Prerequisites: Essential Skills for Getting Started with a Bioinformatics Project
Chapter 2. Setting Up and Managing a Bioinformatics Project
Chapter 3. Remedial Unix Shell
Chapter 4. Working with Remote Machines
Chapter 5. Git for Scientists
Chapter 6. Bioinformatics Data

Part III. Practice: Bioinformatics Data Skills
Chapter 7. Unix Data Tools
Chapter 8. A Rapid Introduction to the R Language
Chapter 9. Working with Range Data
Chapter 10. Working with Sequence Data
Chapter 11. Working with Alignment Data
Chapter 12. Bioinformatics Shell Scripting, Writing Pipelines, and Parallelizing Tasks
Chapter 13. Out-of-Memory Approaches: Tabix and SQLite
Chapter 14. Conclusion