Therefore, messenger RNA (mRNA) molecules were the most frequently studied RNA species because they encoded proteins via the genetic code. Historically, RNA molecules were relegated as a simple intermediate between genes and proteins, as encapsulated in the central dogma of molecular biology. The transcriptome has a high degree of complexity and encompasses multiple types of coding and noncoding RNA species. Collectively defined as the transcriptome, these RNA molecules are essential for interpreting the functional elements of the genome and understanding development and disease. The transcription of a subset of genes into complementary RNA molecules specifies a cell's identity and regulates the biological activities within the cell. The ultimate expression of this genetic information modified by environmental factors characterizes the phenotype of an organism. The central dogma of molecular biology outlines the flow of information that is stored in genes as DNA, transcribed into RNA, and finally translated into proteins ( Crick 1958 Crick 1970).
This article provides an introduction to RNA-Seq methods, including applications, experimental design, and technical challenges.
In addition to polyadenylated messenger RNA (mRNA) transcripts, RNA-Seq can be applied to investigate different populations of RNA, including total RNA, pre-mRNA, and noncoding RNA, such as microRNA and long ncRNA. Recent advances in the RNA-Seq workflow, from sample preparation to library construction to data analysis, have enabled researchers to further elucidate the functional complexity of the transcription. Beyond quantifying gene expression, the data generated by RNA-Seq facilitate the discovery of novel transcripts, identification of alternatively spliced genes, and detection of allele-specific expression. Compared to previous Sanger sequencing- and microarray-based methods, RNA-Seq provides far higher coverage and greater resolution of the dynamic nature of the transcriptome. RNA sequencing (RNA-Seq) uses the capabilities of high-throughput sequencing methods to provide insight into the transcriptome of a cell.
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