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

About the Author

Vince Buffalo is currently a first-year graduate student studying population genetics in Graham Coop's lab at UC Davis in the Population Biology Graduate Group. Before starting his PhD in population genetics, Vince worked professionally as a bioinformatician in the Bioinformatics Core at the UC Davis Genome Center and in the Department of Plant Sciences. An obsessive programmer since he was a young teenager, Vince was drawn to the statistical and computational problems of genomics. He works on open source bioinformatics tools in his work and free time, and enjoys fly fishing and cooking when away from the computer.