Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the ‘scientific status’ of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.

中文翻译：

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使用来自新兴模式生物的短读数据进行转录组的从头组装和差异基因表达分析 - 简要指南


生物学中的许多问题都受益于各种模型系统的使用。高通量测序方法是多样化模型系统民主化的胜利。它们允许对整个基因组或感兴趣的转录组进行经济的测序，并且通过技术变化甚至可以提供对基因组组织以及基因表达和调控的深入了解。如此大的数据集的分析和生物学解释可能会带来重大挑战，这取决于模型系统的“科学地位”。虽然高质量的基因组和转录组参考可随时用于完善的模型系统，但为新兴模型系统建立此类参考通常需要大量资源，例如财务、专业知识和计算能力。转录组的从头组装代表了新兴模型系统中遗传和分子研究的绝佳切入点，因为它可以有效评估基因内容，同时也可以作为差异基因表达研究的参考。然而，从头转录组组装的过程并不简单，通常必须针对每个数据集根据经验进行优化。对于使用新兴模型系统的研究人员来说，并且几乎没有从 Illumina 平台组装和量化短读长数据的经验，这些过程可能令人望而生畏。在本指南中，我们概述了从头建立参考转录组时面临的主要挑战，并就如何实现这一目标提供了建议。我们描述了主要的实验和生物信息学步骤，为从头转录组组装和差异基因表达分析的新手提供了一些广泛的建议和注意事项。 此外，我们提供了初步选择的工具，可以帮助从原始短读数据到组装的转录组和差异表达基因列表。