RNA Sequencing and Transcriptomics

RNA sequencing (RNA-seq) determines the identity and abundance of RNA molecules in a sample by high-throughput sequencing, giving a quantitative, genome-wide picture of gene expression. Transcriptomics is the study of this complete set of transcripts, the transcriptome, and how it changes between tissues, conditions, and disease states.

Definition

RNA sequencing is a method that converts RNA into a library of sequenced fragments and counts reads mapped to genes or transcripts to quantify expression across the transcriptome, the complete set of RNA molecules present in a cell or tissue.

Scope

This topic covers how RNA-seq turns sequence reads into expression estimates, the meaning of the transcriptome as a dynamic readout, common quantification units, and the quality and standardisation issues specific to sequencing-based measurement. It treats RNA-seq as a measurement and discovery platform rather than as a clinical test protocol.

Core questions

• How are sequencing reads converted into quantitative expression estimates?
• What does the transcriptome capture beyond a fixed list of genes?
• How do normalisation and read depth affect comparability?
• How is the accuracy and reproducibility of RNA-seq assessed?

Key concepts

• Transcriptome
• Read mapping and counting
• Normalisation (e.g. depth and length scaling)
• Differential expression
• Spike-in controls
• Single-cell and bulk transcriptomics

Mechanisms

RNA is extracted, reverse-transcribed into cDNA, fragmented, and prepared into a sequencing library; the resulting reads are aligned to a reference genome or transcriptome, and the number of reads overlapping each feature provides a count proportional to its expression (Mortazavi et al., 2008). Because total read depth and transcript length influence raw counts, the data are normalised before transcripts can be compared, and abundance is often expressed in length- and depth-scaled units. RNA-seq can detect novel transcripts, splice variants, and a wide dynamic range of expression, distinguishing it from earlier hybridisation-based profiling (Wang et al., 2009). External spike-in standards and consortium benchmarking are used to characterise the platform's accuracy and limits (Jiang et al., 2011; SEQC/MAQC-III Consortium, 2014).

Clinical relevance

RNA-seq increasingly underlies molecular tumour profiling, fusion detection, and expression-based classification, and interpreting such data requires understanding how counts become expression estimates. This entry describes the method and its quantitative properties; it does not provide diagnostic interpretations or treatment guidance, which rest on validated assays and clinical criteria.

Evidence & guidelines

Foundational descriptions of RNA-seq as a quantitative method (Mortazavi et al., 2008; Wang et al., 2009) are complemented by community efforts on accuracy and reproducibility, including external spike-in standards (Jiang et al., 2011) and the SEQC/MAQC-III benchmarking of RNA-seq performance (2014).

History

Transcriptome measurement moved from expressed-sequence-tag and microarray approaches to direct sequencing in the late 2000s, when next-generation sequencing made whole-transcriptome read counting practical (Mortazavi et al., 2008). RNA-seq quickly became a standard for expression studies, and subsequent consortium work addressed how to make its measurements comparable and reproducible (SEQC/MAQC-III Consortium, 2014).

Debates

How should RNA-seq counts be normalised for comparison?

Raw counts depend on sequencing depth and transcript length, and different normalisation choices can change which genes appear differentially expressed; selecting appropriate normalisation and controls remains a methodological concern.

Key figures

• Zhong Wang
• Michael Snyder
• Ali Mortazavi
• Barbara Wold

Seminal works

• wang-2009
• mortazavi-2008
• seqc-2014

Frequently asked questions

What is the transcriptome?

The transcriptome is the complete set of RNA transcripts present in a cell or tissue at a given time; because it changes with condition and cell type, measuring it shows which genes are active and at what level.

How does RNA-seq differ from microarrays for expression?

RNA-seq sequences and counts RNA directly, so it can detect novel transcripts and splice variants and covers a wide dynamic range, whereas microarrays measure hybridisation to predefined probes and are limited to known sequences.