[ 196 Pages Report] The RNA Analysis Market is projected to reach USD 8.7 billion by 2025 from USD 4.5 billion in 2020, at a CAGR of 13.9% during the forecast period. The growth of this market is majorly driven by the increasing demand for personalized medicine, increasing pharmaceutical and biotechnology R&D expenditure and government funding for omics, and the increasing applications of RNA sequencing in the transcriptomics market.



The increasing focus on biomarker discovery and toxicogenomics, along with the increasing interest in outsourcing services, is expected to offer significant growth opportunities for the players operating in the global RNA analysis/transcriptomics market. On the other hand, the requirement of high capital investments and data management issues are expected to restrain the growth of this market to a certain extent.



With the widespread implementation of novel applications, the use of transcriptomics research extends all the way from the discovery of novel drugs, vaccines, and diagnostic targets, toxicology, and identification of surrogate markers of activity in clinical research to the ability to provide information on the mechanisms of drug action.



Download PDF Brochure @ https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=240344576



db2a8253598cde861632ef1338c31cd6.jpg

Biomarker identification, along with its clinical applications, has simplified drug discovery and development procedures in terms of evaluating the toxicity and efficacy of drugs. Besides, biomarkers for diseases like cancer, CVD, and neurological diseases are playing an integral role in enabling a better understanding of the disease pathway and progression.



The microarray technology used in transcriptomic analysis offers the promise of widespread study of diseases at the genomic level. It also aids in developing novel clinical biomarkers, identifying novel molecular abnormalities, and investigating drug efficacy.



Microarray experiments for transcriptomic studies produce enormous amounts of data. Therefore, researchers typically face problems in the interpretation of results and exploiting available computational power to process said data. Moreover, RNA sequencing data from transcriptomics studies have low genome coverage and high amplification bias, which makes data analysis extremely challenging, especially without effective bioinformatics tools. Many existing tools developed for bulk cell sequencing have not been effective in the analysis of transcriptomics data.