Laboratorio Nacional de Genómica para la Biodiversidad-Unidad de Genómica Avanzada, Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV), Unidad Irapuato, Irapuato, México.
Departamento de Ingeniería Genética, CINVESTAV, Unidad Irapuato, Irapuato, México.
Curr Protoc. 2024 May;4(5):e1054. doi: 10.1002/cpz1.1054.
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.
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,我们提供了一份全面的指南,提供了从原始读取到功能富集分析的分析 RNA-seq 数据的分步工作流程,从实验设计的考虑开始。这旨在帮助与任何生物体一起工作的学生和研究人员,无论是否有组装的基因组。在本指南中,我们使用各种公认的生物信息学工具来探索 RNA-seq 分析的领域,并讨论了同一任务不同工具的优缺点。我们的方案侧重于清晰性、可重复性和实用性,使用户能够轻松地驾驭 RNA-seq 数据分析的复杂性,并从数据集获得有价值的生物学见解。此外,所有脚本和一个示例数据集都可在 GitHub 存储库中获得,以方便分析管道的实施。
版权所有© 2024 作者。Wiley Periodicals LLC 出版的当前方案。基本方案 1:具有可用参考基因组的模式植物数据的分析基本方案 2:基因本体富集分析基本方案 3:非模式植物数据的从头组装。