A Beginner's Guide to RNA Sequencing Data Analysis

A Beginner's Guide to RNA Sequencing Data Analysis

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RNA sequencing (RNA-seq) has revolutionized the field of genomics by enabling researchers to explore the transcriptome and gain insights into gene expression patterns. However, the analysis of RNA-seq data can be complex, especially for beginners. In this guide, we will take you through the process step by step. By following this guide, beginners can gain a solid foundation for effectively analyzing RNA-Seq data.

What You Need for RNA-Seq Analysis

RNA-seq data analysis requires specific hardware, software, and skills to effectively process and interpret the data. Here's a breakdown of what you need to get started:


  • Linux environment or server: RNA-seq analysis is often performed on Linux-based systems due to their robustness and compatibility with many analysis tools. You can set up a Linux environment on your local machine or use a remote server accessed via shell terminals like PuTTY or MobaXterm.
  • Virtual machine (optional): If you are working on a Windows machine, you can set up a virtual machine running a Linux distribution to perform the analysis. This allows you to have a dedicated Linux environment without altering your host operating system.
  • Sufficient hardware specifications: The hardware requirements may vary depending on the size and complexity of your RNA-seq project. It is recommended to have at least 32GB of RAM, especially when working with larger genomes or datasets. Additionally, having 1TB or higher storage capacity is advisable to accommodate the large amount of data generated during the analysis.


  • FastQC: FastQC is a quality control tool used to assess the quality of RNA-seq reads. It provides detailed reports on sequence quality, adapter content, overrepresented sequences, and more.
  • Trimmomatic: Trimmomatic is a popular tool used for read trimming and filtering. It helps remove low-quality bases, adapter sequences, and artifacts that could affect downstream analysis.
  • STAR: STAR (Spliced Transcripts Alignment to a Reference) is a highly efficient RNA-seq aligner. It maps the trimmed reads to a reference genome, taking into account splice junctions, and generates alignment files in the SAM/BAM format.
  • Samtools: Samtools is a suite of utilities for manipulating SAM/BAM files generated by aligners like STAR. It provides functionalities for sorting, indexing, and extracting information from alignment files.
  • FeatureCounts: FeatureCounts is a tool used to assign RNA-seq reads to specific genomic features, such as genes. It counts the number of reads mapping to each feature, allowing for gene-level expression quantification.
  • DESeq2: DESeq2 is an R package used for differential gene expression analysis. It employs statistical methods to compare gene expression levels between different conditions or experimental groups, enabling the identification of significantly differentially expressed genes.


  • Using the command line: Proficiency in working with the command line interface is crucial for executing analysis tools, navigating file systems, and managing data files.
  • Installing and executing software: You should be comfortable with installing software packages and executing them from the command line. This may involve downloading and configuring dependencies as well.
  • Navigating file trees: Understanding how to navigate and organize files and directories is important for managing the various input and output files generated during the analysis.
  • Scripting: Basic scripting skills in languages like Bash, Python, and/or Perl can be beneficial for automating repetitive tasks, customizing workflows, and manipulating data files.
  • R scripting: Familiarity with R scripting is valuable when working with DESeq2 and performing downstream analysis and visualization of the differential expression results.

By having the necessary hardware, installing the required software tools, and developing essential skills, you will be well-equipped to embark on RNA-seq data analysis and derive meaningful insights from your experimental data.

Workflow of RNA Sequencing Data Analysis

We will take you through the process step by step, starting with the Linux command line and covering essential tools such as FastQC, Trimmomatic, STAR, FeatureCounts, and DESeq2 we mentioned above.

Mastering the Linux Command Line:

To begin analyzing RNA-seq data, it's crucial to have a basic understanding of the Linux command line interface. The command line allows you to interact with your computer and execute various operations efficiently. Familiarize yourself with basic commands such as navigating directories, creating and deleting files, and executing programs.

Quality Assessment and Assurance with FastQC:

Before diving into data analysis, it's essential to assess the quality of your RNA-seq reads. FastQC is a popular tool that provides valuable information about sequence quality, overrepresented sequences, GC content, and more. By running FastQC on your raw data, you can identify potential issues and make informed decisions for downstream analysis. You can read our article Quality control: How do you read your FASTQC results? for more information.

Trimming Reads:

RNA-seq reads often contain low-quality bases or adapter sequences that may impact downstream analysis. Trimmomatic is a versatile tool used to trim and filter reads based on quality scores. By removing poor-quality bases or adapter contamination, Trimmomatic helps improve the accuracy and reliability of subsequent analysis steps.

Aligning Reads to the Reference Genome with STAR:

Aligning RNA-seq reads to a reference genome is a critical step in the analysis pipeline. STAR (Spliced Transcripts Alignment to a Reference) is a widely-used and highly efficient aligner that accurately maps reads to their genomic locations, considering splice junctions. By aligning the reads, you can identify which parts of the genome they originated from, enabling downstream analysis such as gene expression quantification.

Calculating Gene Hit Counts with FeatureCounts:

After aligning the reads, it's essential to quantify gene expression levels. FeatureCounts, advanced normalization techniques including TMM and RPKM/FPKM, and discuss the importance of batch effect removal for robust analysis. It assigns each read to a feature and provides the counts, which represent the expression level of genes. These counts serve as the basis for differential expression analysis and further downstream analysis. And normalization is crucial to account for technical biases and variations in RNA-Seq data. We discussed normalization methods such as RPKM (reads per kilobase per million mapped reads) and FPKM (fragments per kilobase per million mapped reads) and their considerations.

Comparing Hit Counts between Groups with DESeq2:

Once you have obtained gene hit counts, you can compare expression levels between different groups or conditions. DESeq2 is a widely-used R package that performs differential gene expression analysis, taking into account the inherent variability in RNA-seq data. It helps identify genes that show significant differences in expression between groups, providing valuable insights into the underlying biology.

By following these steps, you can gain a comprehensive understanding of your RNA-seq data and extract meaningful insights from it. Remember, this guide provides a simplified overview, and there are numerous additional tools and techniques available for more advanced analysis. Exploring these resources and further enhancing your skills will enable you to delve deeper into the exciting world of RNA-seq analysis. Armed with this beginner's guide, you are ready to embark on your RNA-seq data analysis journey and unlock valuable insights into gene expression dynamics.

Why it is important to analyze RNA Sequencing Data?

RNA sequencing data analysis is essential for several reasons. Firstly, it allows researchers to identify differentially expressed genes (DEGs) between different biological conditions or treatments. DEGs can provide insights into the molecular mechanisms underlying disease and can be used as potential targets for drug development. Secondly, RNA sequencing data analysis enables researchers to perform functional annotation and enrichment analysis to identify the biological pathways and functions associated with the DEGs. Finally, RNA sequencing data analysis can be used to visualize gene expression patterns and identify co-expressed genes or modules, providing insights into the gene regulatory networks underlying cellular processes.

* For Research Use Only. Not for use in diagnostic procedures.
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