RNA-Seq data analysis

A primary goal of RNA-Seq data analysis is to identify differential gene expression and coregulated genes and transform raw sequencing reads into biological insights. Sources of material commonly used for RNA-Seq studies include sorted cells, whole-tissue homogenates, and cells cultured in vitro.

RNA-Seq is important as it provides a quantitative, genome-wide view of the transcriptome. Data analysis bridges raw sequencing data to actionable biological insights, allowing researchers to understand how gene expression changes across tissues, conditions, and treatments.²

Visit our RNA sequencing page or watch our Introduction to RNA sequencing webinar to learn more about RNA-Seq, library prep kits, input quantity, and data quality recommendations.

Several key steps in RNA-Seq data analysis are commonly used to make sure that raw RNA-Seq reads are transformed into biological insights, for example, identifying differentially expressed genes.

Key steps in RNA-Seq data analysis include the following:

1. Quality control and read cleaning: Using tools such as FastQC to evaluate raw reads for sequencing issues, including adapter contamination, read quality, and removing low-quality reads.
2. Read alignment and quantification: DRAGEN secondary analysis software and other related tools map reads to a reference genome or transcriptome and quantify gene or transcript expression.
3. Data preprocessing and normalization: Account for library size and normalize counts to correct for biases to compare gene expression across samples.
4. Differential expression (DE) analysis and downstream analysis: Identify genes with statistically significant differences in expression between samples or conditions and interpret biological insights through pathway enrichment and visualizations.³

Quality control and trimming are important steps in RNA-Seq preprocessing where QC is used to check raw reads for issues, such as sequencing errors, while trimming removes low-quality bases and adapter sequences. These steps ultimately help improve read alignment accuracy and reliable downstream analyses.⁴

The Illumina DRAGEN Single Cell RNA app is like the 10X Genomics Cell Ranger pipeline in terms of functionality (read alignment, transcript counting, output metrics, and cell-by-gene matrix analysis metrics). Outputs from both pipelines can be loaded into Illumina Connected Multiomics for downstream analysis. Cell Ranger is specific to 10X Genomics data only. The DRAGEN Single Cell RNA app can support multiple assays, although we have optimized parameters for the Illumina Single Cell 3′ RNA prep kit.