RNA sequencing
RNA sequencing, often called RNA-seq, measures RNA molecules in a sample using sequencing. It helps researchers study gene expression, transcriptomes, cell states, disease biology, and how genomes are used in living cells.
What RNA sequencing is
RNA sequencing is a family of methods for reading RNA-derived sequences from a sample. Because RNA molecules reflect genes being transcribed, RNA-seq can show which genes are active, which transcripts are present, and how expression changes across time, tissues, disease states, or treatments.
RNA-seq versus DNA sequencing
DNA sequencing reads genetic information that is usually stable across cells in an organism. RNA-seq reads a changing layer of biology: the RNA molecules present at a particular time and place. That makes RNA-seq useful for studying activity, regulation, and cell identity rather than only inherited sequence.
From RNA to reads
A typical RNA-seq workflow starts with sample collection, RNA extraction, RNA quality checks, library preparation, sequencing, and computational analysis. Many platforms first use reverse transcriptase to make complementary DNA, or cDNA, because common sequencing instruments read DNA more readily than RNA.
Transcriptomes
The transcriptome is the set of RNA transcripts in a cell, tissue, or sample under particular conditions. RNA-seq can estimate transcript abundance, reveal alternative splicing, detect gene fusions, and help annotate genes. The result is a snapshot, not a permanent property of the organism.
Bulk and single-cell RNA-seq
Bulk RNA-seq averages signal across many cells, which is useful for tissue-level comparisons. Single-cell RNA-seq profiles many individual cells, making it possible to identify cell types, developmental paths, and rare cell states. Single-cell data are powerful but sparse and computationally demanding.
Analysis and normalization
After sequencing, reads are filtered, aligned or pseudoaligned, counted, normalized, and analyzed statistically. Researchers may compare expression between groups, cluster cells, study pathways, or visualize transcript patterns. Normalization matters because raw read counts reflect library size, transcript length, protocol choices, and biology.
Limits and bias
RNA is fragile, and RNA-seq results can be shaped by sample handling, batch effects, sequencing depth, reference annotations, and data-processing choices. The presence of RNA does not always mean a protein is made, and absence from a dataset does not always mean a gene is inactive.
Why it matters
RNA sequencing connects genomes to living cell behavior. It supports cancer research, developmental biology, immunology, infectious disease studies, neuroscience, drug discovery, and clinical genomics, especially when scientists need to understand not just what DNA is present but how it is being used.