RNA Analysis: Global Market Size, Shares, Trends, Opportunities And Forecast 2020-2030

(MENAFN- GlobeNewsWire - Nasdaq) Dublin, March 31, 2025 (GLOBE NEWSWIRE) -- The "RNA Analysis Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2020-2030F" report has been added to ResearchAndMarkets.com's offering.
The Global RNA Analysis Market was valued at USD 9.68 billion in 2024 and is expected to reach USD 14.38 billion by 2030 with a CAGR of 9.78% during the forecast period.

Improved sequencing accuracy, throughput, and reduced costs are driving adoption in research and clinical applications. RNA analysis is widely used in oncology research, where it aids in identifying cancer biomarkers, studying tumor heterogeneity, and developing targeted therapies. The increasing prevalence of cancer drives the demand for RNA-based diagnostic and therapeutic approaches.
The development of RNA-based therapeutics, including mRNA vaccines and RNA interference (RNAi) therapies, has gained significant momentum. This has led to increased interest in RNA analysis for optimizing therapeutic design and monitoring treatment responses. RNA analysis plays a crucial role in the surveillance and monitoring of infectious diseases. The COVID-19 pandemic highlighted the importance of RNA-based diagnostics and the need for rapid and accurate pathogen detection.

Advancements in RNA Sequencing Technologies
Next-Generation Sequencing (NGS) platforms such as Illumina's HiSeq and NovaSeq, have become the workhorses of RNA-Seq. These platforms offer high-throughput sequencing with massive parallel processing, allowing researchers to analyze thousands to millions of RNA molecules simultaneously. NGS has significantly reduced the cost of sequencing and increased the speed of data generation. Single-Cell RNA Sequencing (scRNA-Seq) enables the analysis of gene expression at the single-cell level, revealing cellular heterogeneity within tissues and organisms. This technology has advanced our understanding of developmental biology, immunology, and disease progression.
Innovations in microfluidics and barcoding have made scRNA-Seq more accessible and efficient. Traditional short-read sequencing platforms can struggle to accurately assemble long transcripts and resolve complex gene structures. Long-read sequencing technologies, such as Pacific Biosciences' SMRT sequencing and Oxford Nanopore Technologies' nanopore sequencing, produce longer sequencing reads. In July 2024, Biostate AI introduced advanced RNA sequencing and analysis tools, including Total RNA Sequencing technology. This innovation utilizes the company's patent-pending Barcode-Integrated Reverse Transcription (BIRT) to analyze all RNA types, including non-coding RNA species. Biostate AI's technology enhances researchers' ability to understand gene expression and regulation, offering valuable insights into disease mechanisms and potential therapeutic targets.
Single-Cell RNA Sequencing Complexity
Single-cell RNA sequencing (scRNA-Seq) is a powerful and transformative technology that has revolutionized our understanding of cellular heterogeneity and gene expression at the single-cell level. scRNA-Seq generates vast amounts of data, with each cell representing a data point. Analyzing and managing this high-dimensional data is computationally intensive and requires specialized bioinformatics tools and expertise. Ensuring data quality is challenging in scRNA-Seq due to potential sources of technical variability, such as cell capture efficiency, library preparation, and sequencing biases. Quality control steps are critical to identify and mitigate these issues.
Normalizing scRNA-Seq data to account for differences in sequencing depth and library size between cells is a complex task. Various normalization methods have been developed, but choosing the appropriate one for a given dataset can be challenging. Batch effects can arise when cells are processed in different batches or on different platforms. These batch effects can confound the analysis and interpretation of scRNA-Seq data. Strategies for batch correction are an ongoing area of research. scRNA-Seq can inadvertently capture more than one cell in a single droplet or well, leading to cell doublets or multiplets. Identifying and removing these artifacts is crucial for accurate analysis.
Not all RNA molecules in a cell are captured during scRNA-Seq. The efficiency of capturing RNA varies which can result in a skewed representation of gene expression levels. Accurate cell type identification and annotation can be challenging, especially in heterogeneous tissues. Defining cell types and subtypes based on gene expression profiles requires careful curation and integration with existing knowledge. Identifying and characterizing rare cell populations can be difficult due to the limited number of cells and transcripts available for analysis.
Bioinformatics and Data Analysis
With the explosion in RNA-Seq data volume, efficient data management and storage solutions are essential. Bioinformatics tools help researchers organize, store, and retrieve large datasets. RNA-Seq data often require preprocessing steps to remove noise, correct for biases, and normalize data. Bioinformatics pipelines are used to perform these essential data preprocessing tasks. In May 2023, ReNAgade Therapeutics emerged after securing $300 million in Series A funding, driven by the potential of RNA technology. The company is led by a team of industry experts, including former Moderna executives, positioning it for innovation and growth in the RNA-based therapeutics sector.
Quality control metrics and algorithms are employed to assess the quality of RNA-Seq data and samples. Identifying and addressing issues early in the analysis process is critical to obtaining reliable results. Bioinformatics tools align sequencing reads to reference genomes or transcriptomes. Accurate alignment is crucial for quantifying gene expression levels and identifying variants. Bioinformatics algorithms are used to quantify gene expression levels and perform differential expression analysis to identify genes that are differentially expressed between conditions (e.g., disease vs. control). Tools and methods are developed to analyze alternative splicing patterns, providing insights into gene regulation and isoform diversity. Specialized bioinformatics pipelines are tailored to the unique challenges of single-cell RNA-Seq data, including cell clustering, dimensionality reduction, and cell type annotation.
Key Market Players Profiled

• Agilent Technologies, Inc.
• F. Hoffmann-La Roche Ltd.
• Illumina, Inc.
• QIAGEN N.V.
• Thermo Fisher Scientific, Inc.
• Eurofins Scientific
• Merck KGaA
• Bio-Rad Laboratories, Inc.
• Pacific Bioscience of California, Inc.
• Affymetrix, Inc.

Key Attributes

Report Attribute	Details
No. of Pages	184
Forecast Period	2026-2030
Estimated Market Value (USD) in 2026	$9.68 Billion
Forecasted Market Value (USD) by 2030	$14.38 Billion
Compound Annual Growth Rate	9.7%
Regions Covered	Global

Key Topics Covered
1. Product Overview
2. Research Methodology
3. Executive Summary
3.1. Overview of the Market
3.2. Overview of Key Market Segmentations
3.3. Overview of Key Market Players
3.4. Overview of Key Regions/Countries
3.5. Overview of Market Drivers, Challenges, Trends
4. Voice of Customer
5. Global RNA Analysis Market Outlook
5.1. Market Size & Forecast
5.1.1. By Value
5.2. Market Share & Forecast
5.2.1. By Product (Kits & Reagents, Services, Instruments)
5.2.2. By Technology (Real Time-PCR (qPCR), Microarray, Sequencing, Others)
5.2.3. By Application (Epigenetics, Infectious Diseases & Pathogenesis, Alternative RNA Splicing, RNA Structure & Molecular Dynamics, Development & Delivery of RNA Therapeutics)
5.2.4. By End-use (Government Institutes & Academic Centers, Pharmaceutical & Biotechnology Companies, Hospitals & Clinics, others)
5.2.5. By Region
5.2.6. By Company (2024)
5.3. Market Map
6. North America RNA Analysis Market Outlook

7. Europe RNA Analysis Market Outlook

8. Asia-Pacific RNA Analysis Market Outlook

9. South America RNA Analysis Market Outlook

10. Middle East and Africa RNA Analysis Market Outlook
11. Market Dynamics
11.1. Drivers
11.2. Challenges
12. Market Trends & Developments
12.1. Mergers & Acquisitions (If Any)
12.2. Product Launches (If Any)
12.3. Recent Developments
13. Porter's Five Forces Analysis
13.1. Competition in the Industry
13.2. Potential of New Entrants
13.3. Power of Suppliers
13.4. Power of Customers
13.5. Threat of Substitute Products
14. Competitive Landscape
14.1. Agilent Technologies, Inc.
14.1.1. Business Overview
14.1.2. Company Snapshot
14.1.3. Products & Services
14.1.4. Financials (As Reported)
14.1.5. Recent Developments
14.1.6. Key Personnel Details
14.1.7. SWOT Analysis
14.2. F. Hoffmann-La Roche Ltd.
14.3. Illumina, Inc.
14.4. QIAGEN N.V.
14.5. Thermo Fisher Scientific, Inc.
14.6. Eurofins Scientific
14.7. Merck KGaA
14.8. Bio-Rad Laboratories, Inc.
14.9. Pacific Bioscience of California, Inc.
14.10. Affymetrix, Inc.
15. Strategic Recommendations
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