Jackson Daniel J, Cerveau Nicolas, Posnien Nico
University of Göttingen, Department of Geobiology, Goldschmidtstr.3, Göttingen, 37077, Germany.
University of Göttingen, Department of Developmental Biology, GZMB, Justus-Von-Liebig-Weg 11, Göttingen, 37077, Germany.
Front Zool. 2024 Jun 20;21(1):17. doi: 10.1186/s12983-024-00538-y.
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.
生物学中的许多问题通过使用多种模型系统能得到极大的助益。高通量测序方法在使各种模型系统大众化方面取得了巨大成功。它们能够经济高效地对感兴趣的整个基因组或转录组进行测序，并且通过技术变体甚至可以深入了解基因组组织以及基因的表达和调控。对此类大型数据集的分析和生物学解读可能会带来重大挑战，而这些挑战取决于模型系统的“科学地位”。虽然对于成熟的模型系统来说，高质量的基因组和转录组参考序列很容易获取，但为新兴模型系统建立这样的参考序列通常需要大量资源，如资金、专业知识和计算能力。转录组的从头组装是新兴模型系统遗传和分子研究的一个绝佳切入点，因为它可以有效地评估基因内容，同时还能作为差异基因表达研究的参考。然而，从头转录组组装过程并非易事，通常必须针对每个数据集进行经验性优化。对于使用新兴模型系统且几乎没有或完全没有从Illumina平台组装和定量短读长数据经验的研究人员来说，这些过程可能令人生畏。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，并就如何开展此类工作提供建议。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一些广泛的建议和注意事项。此外，我们初步挑选了一些工具，这些工具可以帮助从原始短读长数据过渡到组装好的转录组以及差异表达基因列表。
