Synopsis
Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, youâ ll 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 lifeâ s 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, youâ re 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
"synopsis" may belong to another edition of this title.
About the Author
Vince Buffalo is a bioinformatician at the UC Davis Department of Plant Sciences, in Jorge Dubcovsky's wheat genomics lab. Before this, he was the primary statistical programmer at the UC Davis Genome Center's Bioinformatics Core where he analyzed many diverse genomics datasets. 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.
"About this title" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. 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 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 Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, you'll learn how to use freely available open source tools to extract meaning from large complex biological data sets. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781449367374