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 languages like Perl and R alongside the Unix command line. But theres 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, youll 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

Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, youll learn how to use freely available open source tools to extract meaning from large complex biological data sets.

At no other point in human history has our ability to understand lifes complexities been so dependent on our skills to work with and analyze data. This intermediate-level book teaches the general computational and data skills you need to analyze biological data. If you have experience with a scripting language like Python, youre ready to get started.

• Go from handling small problems with messy scripts to tackling large problems with clever methods and tools
• Process bioinformatics data with powerful Unix pipelines and data tools
• Learn how to use exploratory data analysis techniques in the R language
• Use efficient methods to work with genomic range data and range operations
• Work with common genomics data file formats like FASTA, FASTQ, SAM, and BAM
• Manage your bioinformatics project with the Git version control system
• Tackle tedious data processing tasks with with Bash scripts and Makefiles