Gene Expression Analysis Market 2021 Global Industry Analysis By Size,Share, Current & Future Growth

Gene expression analyses, simply put, study the activity or occurrence of a gene product formation from the coding gene. This is an indicator of biological activities where changing gene expression patterns are reflected in the change of the biological process. It is a widely used approach in pharmaceutical and clinical settings and research to have a better understanding of gene pathways, individual genes, or higher gene activity profiles.

The most common usage of gene expression analysis is in comparing expression levels of single or multiple genes from various samples. Some common and interesting comparisons include spatial variation within tissues, organs, or other types of samples, time course across treatment regime or during development, pre and post-treatment, mutant vs. wild type, and normal vs. disease. Various factors are propelling the growth and revenue of the gene expression analyses market, such as growth in personalized medicine, technological advancements, increased government funding for genomics, to name a few.

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Common Methods of Gene Expression Analysis 

Various methods are used for profiling gene expression for analyzing the coding transcriptome and/or selecting targets of interest. These include gene expression microarrays, RNA-Seq, and qRT-PCR. Of these, the RNA-sequencing technique is the most widely used.

What is RNA-seq?

RNA-sequencing of RNA-seq is a method that can help in examining the sequences and the quantity of RNA in any sample utilizing next-generation sequencing. This examines the transcriptome of various gene expression patterns that are encoded within the RNA.

Different Applications

RNA-seq helps to investigate as well as discover the transcriptome, the RNA’s total cellular content, including tRNA, rRNA, and mRNA. Understanding the transcriptome definitely is the key if one is to connect the information on their genome with the functional protein expression. This gene expression analysis method throws light on which genes have been turned on in any cell, their expression level, and when they are shut off or activated. This enables scientists to understand a cell’s biology more deeply and also assess changes that may show disease. Differential gene expression analysis, RNA editing, SNP identification, and transcriptional profiling are the most popular techniques which use RNA-seq. This will provide vital information to researchers regarding the functions of genes. The transcriptome, for instance, can highlight every tissue where a gene of unknown functions is expressed that may highlight its role. This also captures information regarding alternative splicing events that produce various transcripts from a single gene sequence. Such events will not be picked up through DNA sequencing. Besides, it can also help in identifying post-transcriptional modifications that take place at the time of mRNA processing, especially polyadenylation & 5’ capping.

Why is RNA-seq Considered Superior to Microarrays?

Both RNA-seq and microarrays have revolutionized the gene discovery study. In fact, the ability to examine multiple gene transcripts simultaneously using genome-wide transcription profiling methods have put such technologies right at the forefront of superior throughput screening. They have a wide spectrum of applications which includes but is not restricted to identifying differentially expressed gene transcripts amongst diseased and healthy cell, offering new insights into the developmental process, gene regulation examination, and pharmacogenomics. But, RNA-seq is considered superior to microarray hybridization due to the following reasons,

  • Not Restricted to Genomic Sequences- As opposed to approaches that are hybridization-based that may need species-specific probes, the RNA-sequencing can help in detecting transcripts from organisms with genomic sequences that are previously undetermined. It is this factor that makes RNA-seq fundamentally superior to detect SNPs, novel transcripts, or other alterations.
  • More Quantifiable- In microarray hybridization, the data is displayed only as values that are relative to those other signals that are sensed on the array, but in the case of RNA-seq, the data is quantifiable. It also avoids the problems microarrays have while detecting very low or very high expression levels.
  • Low Background Signal- RNA-seq uses cDNA sequences that can be mapped on the genome’s targeted regions that make it easier to remove experimental noise. Besides, problems with sub-standard hybridization or cross-hybridization that may plague microarray experiments will not be an issue when it comes to RNA-seq experiments.

The bottom line is the modern-day RNA-sequencing is well established as an excellent alternative to microarrays, and this trend is here to stay for some time.

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