RNA‐Seq Data Analysis: A Practical Guide for Model and Non‐Model Organisms

Author:

Pola‐Sánchez Enrique1,Hernández‐Martínez Karen Magdalena2,Pérez‐Estrada Rafael3,Sélem‐Mójica Nelly34,Simpson June2,Abraham‐Juárez María Jazmín1,Herrera‐Estrella Alfredo14,Villalobos‐Escobedo José Manuel456ORCID

Affiliation:

1. 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

2. Departamento de Ingeniería Genética, CINVESTAV Unidad Irapuato Irapuato México

3. Centro de Ciencias Matemáticas Universidad Nacional Autónoma de México (UNAM) Morelia México

4. The LatAmBio Initiative Irapuato México

5. Department of Plant and Microbial Biology University of California‐Berkeley Berkeley California United States

6. Institute for Obesity Research Tecnológico de Monterrey Monterrey Mexico

Abstract

AbstractRNA 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 genomeBasic Protocol 2: Gene ontology enrichment analysisBasic Protocol 3: De novo assembly of data from non‐model plants

Publisher

Wiley

Reference47 articles.

1. Alexa A. &Rahnenfuhrer J.(2023). topGO: Enrichment analysis for gene ontology. GO R package version 2.54.0.https://bioconductor.org/packages/topGO

2. HTSeq--a Python framework to work with high-throughput sequencing data

3. Blighe K. Rana S. &Lewis M.(2019). EnhancedVolcano: Publication‐ready volcano plots with enhanced coloring and labeling. R package version 1(0) https://bioconductor.org/packages/release/bioc/html/EnhancedVolcano.html

4. Trimmomatic: a flexible trimmer for Illumina sequence data

5. GO::TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3