RNA-seq

RNA-seq measures the abundance of all RNA transcripts in a sample, from highly expressed housekeeping genes to rare isoforms. Unlike microarrays, it does not rely on pre-designed probes, allowing for the discovery of novel transcripts and splice junctions. The output is a digital count of sequencing reads mapped to each gene or transcript, providing a dynamic range that spans over five orders of magnitude. This enables precise quantification of gene expression levels across different conditions, cell types, or developmental stages.

The technique provides nucleotide-level precision, revealing the exact boundaries of exons, introns, and untranslated regions. This granularity is essential for identifying single nucleotide polymorphisms (SNPs) within expressed transcripts, detecting RNA editing events, and precisely mapping transcription start sites. It allows researchers to distinguish between highly similar gene family members and allele-specific expression, where transcripts from maternal and paternal chromosomes are expressed at different levels.

Advanced library preparation protocols preserve the orientation of the original RNA molecule. This strand-specific information is critical for accurately resolving the expression of overlapping genes transcribed from opposite DNA strands, a common feature in compact genomes. It also enables the correct assignment of reads to antisense transcripts and long non-coding RNAs, preventing the misquantification that occurs with non-stranded protocols where the transcript of origin is ambiguous.

By sequencing the entire length of cDNA fragments, RNA-seq captures the connectivity between exons. This allows computational tools like Salmon and Kallisto to quantify the abundance of specific transcript isoforms produced by alternative splicing. Understanding isoform switching is fundamental to developmental biology and disease, as different protein variants with distinct functions can be generated from a single gene. This level of detail is unattainable with 3'-biased tag-sequencing methods.

Beyond messenger RNA, the technique captures the full spectrum of the non-coding transcriptome. This includes long non-coding RNAs (lncRNAs), microRNAs, piwi-interacting RNAs (piRNAs), and circular RNAs. By using protocols that deplete ribosomal RNA or enrich for specific RNA classes, researchers can profile these regulatory molecules. The discovery and quantification of these non-coding RNA species have been pivotal in revealing new layers of genomic regulation that control gene expression epigenetically and post-transcriptionally.

Paired-end sequencing reads that map to two distinct, distant genes are a hallmark of chromosomal rearrangements. RNA-seq is a powerful tool for the de novo discovery of gene fusions, which are driver mutations in many cancers. Algorithms analyze discordant read pairs and split reads that span the fusion junction, pinpointing the exact genomic breakpoint. This capability provides both a diagnostic marker and a potential therapeutic target, directly linking the transcriptomic profile to the underlying genomic structural variation.

Transcripts Per Million is a normalization method for RNA-seq data that corrects for both gene length and sequencing depth. TPM values represent the proportion of transcripts in a sample, enabling direct comparison of relative abundance across genes and samples.

• First normalizes for gene length (reads per kilobase)
• Then scales to per-million total across all transcripts
• Sum of all TPM values in a sample equals one million
• Preferred over RPKM/FPKM for cross-sample comparisons

Systematic non-biological variations in high-throughput data introduced by differences in sample processing, reagent lots, or sequencing platforms. These technical artifacts can confound machine learning models and lead to spurious biological conclusions if not corrected.

• Common sources: different extraction dates, technicians, flow cells
• Can dominate biological signal in PCA and clustering
• Correction methods include ComBat-seq and limma-voom
• Critical to account for in multi-institutional studies

A statistical batch correction method specifically adapted for RNA-seq count data. ComBat-Seq uses a negative binomial regression model to adjust for known technical covariates while preserving biological variability and the discrete nature of count data.

• Extends the original ComBat method for microarrays
• Maintains integer counts after adjustment
• Preserves differential expression signals between conditions
• Requires known batch covariate labels for each sample

Genomic loci, known as eQTLs, where genetic variants are statistically associated with variation in mRNA expression levels of a specific gene. eQTL analysis links regulatory DNA to transcript abundance, revealing the genetic architecture of gene regulation.

• Cis-eQTLs act on nearby genes (within 1 Mb)
• Trans-eQTLs affect distant genes, often via transcription factors
• Discovered through GWAS integration with RNA-seq data
• Key resource: the GTEx consortium database