RNASeq

RNA sequencing (RNA-seq) is a high-throughput method for profiling the transcriptome by sequencing complementary DNA (cDNA) derived from RNA molecules. It is used to measure gene expression levels, identify and quantify transcripts, and discover novel RNA species. RNA-seq can be performed with short-read or long-read sequencing technologies, with Illumina platforms being the most common for bulk RNA-seq and specialized protocols used for single-cell RNA-seq. Long-read platforms such as PacBio and Oxford Nanopore enable sequencing of full-length transcripts and improved isoform discovery.

In a typical workflow, RNA is extracted from a sample, and mRNA is enriched or rRNA is depleted. The RNA is reverse transcribed into cDNA, which is then prepared into a sequencing library and sequenced. The resulting reads are subjected to quality control and either aligned to a reference genome or transcriptome, or quantified using alignment-free approaches. Common analysis steps include read alignment or pseudoalignment, transcript quantification, normalization, and downstream statistical analysis to identify differential expression. Tools such as STAR or HISAT2 are used for alignment, while Salmon or kallisto enable fast, alignment-free quantification. Downstream analyses often involve DESeq2, edgeR, or limma to test for differential gene expression, along with methods to explore alternative splicing and isoform usage. Annotation and databases (e.g., GENCODE, Ensembl) guide interpretation.

Applications of RNA-seq include measuring gene expression across conditions or tissues, discovering novel transcripts, characterizing alternative splicing patterns, and identifying fusion genes or non-coding RNAs. Limitations include technical bias in library preparation, sequencing depth requirements, batch effects, and the need for careful experimental design. Advances in single-cell and long-read RNA-seq continue to expand resolution and transcript annotation capabilities.