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5.2  RNA-SEQ APPLICATIONS

The RNA-Seq emerged as an alternative to microarrays for gene expression research. It

uses the high-throughput sequencing of RNA to examine the quantity of RNA in biologi-

cal samples. The primary application of the RNA-Seq is the gene profiling, which is the

determination of the patterns of genes expressed at the level of transcription under specific

condition or in a specific type of cells to give an overall picture of biological activities.

Simply, it focuses on the transcriptome to tell us which of the genes in the genome of an

organism are turned on or off and to what extent. Compared to microarrays, RNA-Seq

provides an unbiased information about transcripts and it can detect a larger number of

differentially expressed genes. In addition to gene expression profiling, RNA-Seq is also

used in a variety of applications, including complex differential gene expression, single-

cell RNA-Seq, small RNA profiling, variants identification and allele-specific expression,

detection of alternative splicing patterns, system biology, and fusion gene detection.

The differential gene expression is the most important application of RNA-Seq, and it

is the application on which we will focus in detail in this chapter. In differential expres-

sion analysis, we will compare transcriptomes across different conditions of interest. These

conditions can be different samples, development stages, treatments, disease conditions,

etc. The sequencing reads produced from the samples are counted to measure the gene

expression levels. The read counts then are modeled for the statistical significance that

helps us to conclude whether the difference between samples is significant. The differential

analysis can also be followed by annotation using functional annotation database such as

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

RNA-Seq is also used for the single-cell RNA analysis in which the RNA is extracted

from a single cell or a single type of cells. Single-cell RNA sequencing provides transcrip-

tional profiling of individual cells that enable researchers to study the genes expressed

in the single-cell level and how expression level varies across thousands of cells within

a heterogeneous sample. Most of living cells cannot be cultivated in vitro. Therefore, the

single-cell RNA-seq may lead to the discovery of novel species, pathway, or regulatory

processes of biotechnological or medical relevance. The workflow of single-cell RNA-Seq

generally involves single-cell isolation, cDNA library preparation, RNA-Seq, and RNA-Seq

data analysis [3].

The RNA-Seq is also used to study the small non-coding RNA (sRNA-Seq), which is a

type of non-coding RNA that includes microRNA, small interfering RNA (siRNA), and

P-element-induced wimpy testis (Piwi)-interacting RNA, small nucleolar RNA (snoRNA),

and small nuclear RNA (snRNA). The small RNA-Seq data analysis is known to be chal-

lenging due to the short length of the sequence and non-unique genomic origin.

The RNA-Seq can also be used for variant detection, allele-specific expression, and

expression quantitative trait loci (eQTL). The polymorphisms in coding region (exons) are

better to be detected using transcriptomic sequencing. However, the variant calling pipe-

line must be applied. But the basic RNA-Seq pipeline can be used to quantify the allele-

specific expression (ASE) that is affected by single-nucleotide polymorphisms (SNPs). The

ASE analysis will show which one of two alleles is highly transcribed into mRNA and