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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.
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Go from handling small problems with messy scripts to tackling large problems with clever methods and tools
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Focus on high-throughput (or "next generation") sequencing data
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Learn data analysis with modern methods, versus covering older theoretical concepts
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Understand how to choose and implement the best tool for the job
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Delve into methods that lead to easier, more reproducible, and robust bioinformatics analysis
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