Henke Katrin, Bowen Margot E, Harris Matthew P
Department of Genetics, Harvard Medical School, and Department of Orthopedics, Boston Children's Hospital, Boston, Massachusetts.
Curr Protoc Mol Biol. 2013 Oct 11;104:7.13.1-7.13.33. doi: 10.1002/0471142727.mb0713s104.
Whole-genome sequencing (WGS) has been used in many invertebrate model organisms as an efficient tool for mapping and identification of mutations affecting particular morphological or physiological processes. However, the application of WGS in highly polymorphic, larger genomes of vertebrates has required new experimental and analytical approaches. As a consequence, a wealth of different analytical tools has been developed. As the generation and analysis of data stemming from WGS can be unwieldy and daunting to researchers not accustomed to many common bioinformatic analyses and Unix-based computational tools, we focus on how to manage and analyze next-generation sequencing datasets without an extensive computational infrastructure and in-depth bioinformatic knowledge. Here we describe methods for the analysis of WGS for use in mapping and identification of mutations in the zebrafish. We stress key elements of the experimental design and the analytical approach that allow the use of this method across different sequencing platforms and in different model organisms with annotated genomes.
全基因组测序（WGS）已在许多无脊椎动物模型生物中用作绘制和鉴定影响特定形态或生理过程的突变的有效工具。然而，WGS在高度多态的大型脊椎动物基因组中的应用需要新的实验和分析方法。因此，已经开发了大量不同的分析工具。由于WGS产生的数据的生成和分析对于不熟悉许多常见生物信息学分析和基于Unix的计算工具的研究人员来说可能既繁琐又令人生畏，我们专注于如何在没有广泛计算基础设施和深入生物信息学知识的情况下管理和分析下一代测序数据集。在这里，我们描述了用于斑马鱼中突变的绘制和鉴定的WGS分析方法。我们强调实验设计和分析方法的关键要素，这些要素允许在不同的测序平台上以及在具有注释基因组的不同模型生物中使用该方法。
