Exploring the Potential of RNA-Seq in Various Fields

What is RNA-Seq?

Regulation of gene expression is fundamental to linking genotypes with phenotypes. RNAs shape complex gene expression networks, which drive biological processes. An in-depth understanding of the underlying mechanisms of how to govern these complex gene expression networks is vital for the treatment of complex diseases such as cancer. Hybridization-based microarrays are used to allow the simultaneous monitoring of expression levels of annotated genes in cell populations. However, genome-wide approaches have proven to provide more valuable insights into transcriptomes. These next- or third-generation sequencing platforms allow the rapid and cost-effective generation of massive amounts of sequence data. The RNA profiling by utilizing high-throughput sequencing technologies is known as RNA-seq.

What are the applications of RNA-Seq?

Since RNA-seq is quantitative, it is useful to determine RNA expression levels. In addition to this basic function, RNA-seq can be used for differential gene expression, variant detection and allele-specific expression, small RNA profiling, characterization of alternative splicing patterns, system biology, and single-cell RNA-seq.

• Differential gene expression

One important way RNA-seq is used is to compare transcriptomes from different stages of development, treatments, or disease states.This analysis, also known as differential gene expression analysis, requires the identification of genes along with their isoforms and a precise assessment of their expression levels. It is important to illustrate functional elements of the genome and uncover the biological mechanisms of development and disease.

The common tools for differential gene expression include Cuffdiff, DESeq, DESeq2, EdgeR, PoissonSeq, LimmaVoom, and MISO.

• Variant detection and allele-specific expression

RNA-seq allows the identification of variants and allele-specific expression. Single-nucleotide polymorphisms (SNPs) refer to the variation in a single nucleotide that occurs at a specific position in the genome, which may lead to allele-specific expression (ASE). ASE means that one of two alleles is highly transcribed into mRNA and the other is lowly transcribed or not transcribed at all. Recent studies have also associated ASE with the susceptibility of a number of human diseases. RNA-seq and whole-genome DNA sequencing(WGS) allow identification of common disease variants, including SNPs and ASE.

The common tools used for variant detection are GATK, ANNOVAR, SNPiR, and SNiPlay3.

• Small RNA profiling

Small RNA species usually include microRNA (miRNA), small interfering RNA (siRNA), and piwi-interacting RNA (piRNA), as well as other types of small RNA, such as small nucleolar RNA (snoRNA) and small nuclear RNA (snRNA). Small RNAs play a role in gene silencing and post-transcriptional regulation of gene expression. Small RNAs have been demonstrated to be involved in biological processes, including development, cell proliferation and differentiation, and apoptosis. Most initial small RNA discovery studies used pyrosequencing and, subsequently, other NGS platforms with higher throughput, which resulted in genome-wide surveys and the discovery of an increasing number of small RNA species.

• Characterization of alternative splicing patterns

Alternative splicing patterns are important to understand human development and disease since altered splicing patterns contribute to development, cell differentiation, and human disease. RNA-seq is a powerful tool for characterizing alternative splicing patterns. Paired-end sequencing enables sequence information from both ends, thereby detecting splicing patterns without a requirement for previous knowledge of transcript annotations. PacBio SMRT sequencing allows examination of splicing patterns and transcript connectivity in an unbiased and genome-scale manner by generating full-length transcript sequences.

TopHat, MapSplice, SpliceMap, SplitSeek, GEM mapper, SpliceR, SplicingCompass, GIMMPS, MATS, and rMATS are all tools that are often used to describe alternative splicing patterns.

 System biology

Creating lists of differentially expressed (DE) genes is not the final step of RNA-seq analysis. Further biological insight into an experimental system can be acquired by looking at the expression changes of sets of genes. This process, known as system biology, is based on the understanding that the whole is greater than the sum of the parts. Pathway analysis and co-expression network analysis are two important included parts.

Single-cell RNA-seq

Single-cell RNA-seq is a way to find out how internal cell processes and outside stimuli work together to decide the fate of a cell.It also contributes to a better understanding of how an ‘outlier cell’ may determine the outcome of an infection. In addition, a majority of living cells cannot be cultivated in vitro, single-cell RNA-seq may discover novel species or regulatory processes of biotechnological or medical relevance. The workflow of single-cell RNA-seq generally involves the following steps: single-cell isolation, cDNA library construction, RNA-seq, and bioinformatics.

Applications of single-cell RNA-seq：

Stem cell differentiation

Embryogenesis

Whole-tissue analysis

Single-cell RNA-seq for whole-organism studies

Disease biology and treatment