In modern drug discovery and development, the research and development (R&D) process is a difficult task. The biological system is complex, which is one of the purposes. The human genome contains 20,000–25,000 genes. Traditional test methods for measuring gene expression levels or identifying gene isoforms, such as polymerase chain reaction (PCR), are expensive and time-consuming. RNA sequencing (RNA-Seq) is a high-throughput technology that was developed in 2008 for the study of the entire transcriptome. It can simultaneously measure the expression patterns of thousands of genes and provide insight into biological processes' functional pathways and regulation.
The benefits of RNA-Seq overexpression microarray, another compelling tool for transcriptome analysis, are numerous. To begin, expression microarray relies on gene probes on microarrays hybridizing with target genes in biological samples. These probes are based on previously published genome annotation. RNA-Seq, unlike microarray, is not limited to detecting known transcripts, making it more appealing for finding novel gene transcripts and noncoding RNAs. Second, the resolution of RNA-Seq is far superior. RNA-Seq can expose the fine framework of the transcriptome with a single nucleotide resolution, allowing allele-specific expression, alternative splicing, and single nucleotide polymorphisms (SNPs) in the transcribed regions to be identified. Finally, unlike expression microarray, RNA-Seq has a wider dynamic range of expression levels. It allows for the detection of a greater number of differentially expressed genes. Illumina, SOLID, Ion Torrent, and Roche 454 are the four commercial next-generation sequencing (NGS) platforms available for RNA-Seq. As the cost of sequencing decreases, RNA-Seq is expected to supplant expression microarray as the primary method for studying the transcriptome.
Identification of Drug-Related Genes
Drug discovery faces a significant challenge in identifying potential drug target genes. RNA-Seq is a powerful method for studying drug-induced changes in gene expression across the genome. As a result, the method can assess a drug's global transcriptional effects and significantly speed up the procedure of drug target identification.
Identification of Fusion Proteins in Cancer
For detecting fusion genes, RNA-Seq is useful. Several algorithms have been established to recognize fusion proteins based on RNA-Seq data, including Tophat-fusion, Chimerascan, deFuse, FusionMap, SOAPfuse, and FusionQ. Whole-genome sequencing can also detect fusion genes. However, only a small percentage of gene fusions result in fusion mRNA expression. RNASeq, on the other hand, directly recognizes the fusion genes that are translated into proteins. In humans, the transcriptome accounts for less than 5% of the genome. RNA-Seq has much higher coverage than whole-genome sequencing. As a result, RNA-Seq has been widely used to identify fusion genes.
Identification of miRNAs Involved in the Development of Drug Resistance
Figure 1. Pathways that represent potential targets for miRNAs in drug-resistant gastric cancer. (Riquelme, 2016)
For cancer patients undertaking chemotherapy, drug resistance is becoming an increasing issue. The importance of miRNA in the regulation of drug resistance is supported by substantial evidence. MicroRNA sequencing (miRNA-Seq), a type of RNA-Seq that defines millions of small RNA sequence tags, enables researchers to evaluate miRNA expression profiles in cells in a systematic way. By comparing miRNA expression profiles between drug resistance cells and nonresistance cells, it is possible to detect miRNAs involved in the development of drug resistance using miRNA-Seq.