Abstract：
RNA sequencing (RNA-seq) has emerged as a powerful tool for assessing genome-wide gene expression, revolutionizing various fields of biology. However, analyzing large RNA-seq datasets can be challenging, especially for students or researchers lacking bioinformatics experience. To address these challenges, we present a comprehensive guide to provide step-by-step workflows for analyzing RNA-seq data, from raw reads to functional enrichment analysis, starting with considerations for experimental design. This is designed to aid students and researchers working with any organism, irrespective of whether an assembled genome is available. Within this guide, we employ various recognized bioinformatics tools to navigate the landscape of RNA-seq analysis and discuss the advantages and disadvantages of different tools for the same task. Our protocol focuses on clarity, reproducibility, and practicality to enable users to navigate the complexities of RNA-seq data analysis easily and gain valuable biological insights from the datasets. Additionally, all scripts and a sample dataset are available in a GitHub repository to facilitate the implementation of the analysis pipeline. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Analysis of data from a model plant with an available reference genome Basic Protocol 2: Gene ontology enrichment analysis Basic Protocol 3: De novo assembly of data from non-model plants.
摘要：
RNA测序（RNA-seq）已成为评估全基因组基因表达的强大工具。彻底改变了生物学的各个领域。然而,分析大型RNA-seq数据集可能具有挑战性，特别是对于缺乏生物信息学经验的学生或研究人员。为了应对这些挑战，我们提供了一个全面的指南，以提供分析RNA-seq数据的分步工作流程，从原始读数到功能富集分析，从实验设计的考虑开始。这是为了帮助学生和研究人员与任何有机体一起工作，无论组装的基因组是否可用。在本指南中,我们使用各种公认的生物信息学工具来浏览RNA-seq分析，并讨论相同任务的不同工具的优缺点。我们的协议侧重于清晰度，再现性，和实用性，使用户能够轻松浏览RNA-seq数据分析的复杂性，并从数据集中获得有价值的生物学见解。此外,GitHub存储库中提供了所有脚本和示例数据集，以促进分析管道的实施。©2024作者WileyPeriodicalsLLC出版的当前协议。基本方案1:来自具有可用参考基因组的模型植物的数据分析基本方案2:基因本体论富集分析基本方案3:来自非模型植物的数据的从头组装。
