RNA-seq is a sequencing-based method used to measure and compare RNA molecules in biological samples.
RNA-seq helps researchers study gene expression, transcript abundance, alternative splicing, sample similarity, and biological changes between conditions. Unlike older expression technologies, RNA-seq can measure expression across the transcriptome with a wide dynamic range and can be applied to many organisms when a suitable reference genome or transcriptome is available.
A typical RNA-seq experiment can identify genes that are more highly expressed in one condition than another, genes that are downregulated after treatment, or expression patterns that distinguish disease and control samples. RNA-seq can also support pathway analysis, clustering, principal component analysis, and visualization of sample-level patterns.
RNA-seq output depends on library design, sequencing depth, read quality, reference genome choice, annotation version, sample grouping, and statistical model. Incorrect condition labels or mismatched reference annotation can lead to misleading results even if the software runs successfully.
RNA-seq measures RNA abundance, not necessarily protein activity or biological function. Important findings should be interpreted with experimental context and validated using independent methods when needed.
This guide is provided for research and educational purposes. RNA-seq results should be interpreted with appropriate experimental design, quality control, statistical review, and biological validation.