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 demonstrates 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

[From the Back Cover]

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 datasets.

At no other point in human history has our ability to understand life’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, you’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

Vince Buffalo is currently a first-year graduate student studying population
genetics in Graham Coop's lab at University of California, 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.

这本实用的书讲授了科学家将大型测序数据集转化为可重复的、稳健的生物学发现所需的技能。许多生物学家在开始他们的生物信息学培训时，在Unix命令行旁边学习Python和R等脚本语言。但是，在了解一些编程语言和准备分析大量生物数据之间存在着巨大的差距。

本书不是将生物信息学作为一套可能随着这个快速发展的领域而改变的工作流程来教授，而是通过数据技能展示生物信息学的实践。对数据质量和工具有效性的严格评估是可重复的和强大的生物信息学分析的基础。通过开源和免费提供的工具，你将不仅学会如何做生物信息学，而且学会如何作为一个生物信息学家处理问题。

- 从用混乱的脚本处理小问题到用巧妙的方法和工具解决大问题

- 专注于高通量（或 "下一代"）测序数据

- 用现代方法学习数据分析，而不是覆盖旧的理论概念

- 了解如何为工作选择和实施最佳工具

- 深入研究能使生物信息学分析更容易、更可重复、更强大的方法

[来自封底的信息］

学习必要的数据技能，将大型测序数据集变成

可重现和稳健的生物学发现。通过这本实用指南，你将

学习如何使用免费的开源工具，从大型复杂的生物数据集中提取意义。

大型复杂生物数据集中提取意义。

在人类历史上，我们理解生命的能力从未像现在这样依赖于我们的技能。

我们理解生命复杂性的能力从未如此依赖于我们处理和分析数据的技能。

这本中级别的书教给你分析生物数据所需的一般计算和数据技能

你需要分析生物数据。如果你有使用Python这样的脚本语言的经验

语言的经验，你就可以开始学习了。

- 从用混乱的脚本处理小问题到用聪明的方法和工具解决

巧妙的方法和工具来处理大型问题

- 用强大的Unix管道处理生物信息学数据

和数据工具

- 学习如何使用R语言中的探索性数据分析技术。

R语言

- 使用有效的方法来处理基因组范围数据和

范围操作的有效方法

- 使用常见的基因组学数据文件格式，如FASTA,

FASTQ，SAM和BAM

- 用Git版本控制系统管理你的生物信息学项目

控制系统管理你的生物信息学项目

- 用Bash脚本处理繁琐的数据处理任务

和Makefiles

文斯-布法罗（Vince Buffalo）目前是格雷厄姆-库普（Graham Coop）的一年级研究生，研究群体遗传学。

在加州大学戴维斯分校的Graham Coop实验室研究群体遗传学。

生物学研究生小组。在开始他的人口遗传学博士课程之前，Vince

在加州大学戴维斯分校基因组中心的生物信息学核心担任专业的生物信息学家。

戴维斯基因组中心和植物科学系的生物信息学家。