RNA Sequencing Explained: Measuring Gene Expression
This article is part of the Foundations of Genomic Data series.
What does RNA sequencing capture?
RNA sequencing measures the abundance and composition of RNA molecules present in a biological sample at a given moment.
Unlike DNA sequencing, which records a largely stable genetic sequence, RNA sequencing reflects active biological processes. It captures which genes are being transcribed, how strongly, and in some cases how transcripts are processed.
For this reason, RNA sequencing is best understood as a snapshot of gene activity rather than genetic potential.
From DNA to RNA
Cells share the same DNA but differ in function because they express different sets of genes. RNA serves as the intermediate layer that connects genetic information to cellular behavior.
Measuring RNA therefore provides insight into regulatory programs, environmental responses, and cell identity that are not visible at the DNA level alone.
Types of RNA sequencing
RNA sequencing can be applied in several forms, depending on the biological question.
Bulk RNA sequencing measures average gene expression across a population of cells. It is well suited for comparing conditions, tissues, or disease states.
Single-cell RNA sequencing measures expression at the level of individual cells. This enables the study of cellular heterogeneity, rare cell populations, and developmental trajectories.
Small RNA sequencing focuses on short RNA species such as microRNAs. These molecules play regulatory roles and are often studied in the context of post-transcriptional control.
Library preparation as a modeling choice
RNA sequencing does not measure all RNA molecules equally. Library preparation determines which transcripts are captured and quantified.
Poly-A selection enriches for messenger RNA by targeting polyadenylated transcripts. This approach emphasizes protein-coding genes.
Ribosomal RNA depletion removes abundant ribosomal RNA, allowing a broader range of coding and noncoding transcripts to be measured.
These choices influence what aspects of gene regulation are observable and should be considered part of the experimental design rather than a technical detail.
What RNA sequencing is well suited for
RNA sequencing is commonly used to quantify gene expression and compare transcriptional states across samples.
It supports analyses such as differential expression, splicing variation, transcript discovery, and inference of cell-type composition.
Because RNA levels can change rapidly, RNA sequencing is particularly informative for studying dynamic biological responses.
What RNA sequencing does not measure
RNA sequencing does not directly measure protein abundance or activity. Post-transcriptional and post-translational processes can further modify cellular behavior.
It also does not capture genetic variation with the same completeness or accuracy as DNA sequencing.
RNA sequencing as part of a larger picture
RNA sequencing provides a functional layer between genotype and phenotype. On its own, it offers partial insight.
When combined with DNA sequencing and other molecular measurements, it helps reveal how genetic variation translates into biological outcomes.
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