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市场调查报告书
商品编码
1691713
RNA 分析市场 - 全球产业规模、份额、趋势、机会和预测,按产品、按技术(即时 PCR、微阵列、定序等)、按应用、按最终用途、按地区和竞争,2020-2030FRNA Analysis Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, By Product, By Technology (Real Time-PCR, Microarray, Sequencing, others), By Application, By End-use, By Region and Competition, 2020-2030F |
2024 年全球 RNA 分析市值为 96.8 亿美元,预计到 2030 年将达到 143.8 亿美元,预测期内复合年增长率为 9.78%。 RNA分析是分子生物学和基因组学的一个广泛领域,涉及研究RNA(核糖核酸)分子以了解其结构、功能、调控和表达模式。 RNA是分子生物学中心法则中的关键分子,扮演DNA和蛋白质合成之间的中介。 RNA 分析涵盖各种技术和方法,旨在阐明 RNA 在细胞和生物体内发挥的各种作用。 RNA 分析可以揭示可变剪接事件,其中不同的外显子被纳入或排除在 mRNA 转录本之外。该过程由单一基因产生多种蛋白质异构体。 RNA-Seq 对于研究可变剪接特别有价值。在临床环境中,RNA 分析用于诊断目的,例如检测传染病中的病毒 RNA 或评估基因表现模式以指导治疗决策。 RNA 定序技术的不断进步,例如次世代定序 (NGS) 和单细胞 RNA 定序 (scRNA-seq),扩展了 RNA 分析的能力。提高定序准确性、吞吐量并降低成本正在推动其在研究和临床应用中的采用。 RNA分析广泛应用于肿瘤学研究,有助于识别癌症生物标记、研究肿瘤异质性和开发标靶疗法。癌症发生率的不断上升推动了对基于 RNA 的诊断和治疗方法的需求。基于 RNA 的治疗方法,包括 mRNA 疫苗和 RNA 干扰 (RNAi) 疗法,的发展已获得显着发展势头。这导致人们对 RNA 分析以优化治疗设计和监测治疗反应的兴趣日益浓厚。 RNA分析在传染病的监测和监控中起着至关重要的作用。 COVID-19 大流行凸显了基于 RNA 的诊断的重要性以及快速且准确地检测病原体的必要性。
市场概况 | |
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预测期 | 2026-2030 |
2024 年市场规模 | 96.8 亿美元 |
2030 年市场规模 | 143.8 亿美元 |
2025-2030 年复合年增长率 | 9.78% |
成长最快的领域 | 实时 PCR (qPCR) |
最大的市场 | 北美洲 |
RNA 定序技术的进展
单细胞 RNA 定序的复杂性
生物资讯学和数据分析
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. RNA analysis is a broad field of molecular biology and genomics that involves the study of RNA (ribonucleic acid) molecules to understand their structure, function, regulation, and expression patterns. RNA is a crucial molecule in the central dogma of molecular biology, serving as an intermediary between DNA and protein synthesis. RNA analysis encompasses various techniques and approaches aimed at elucidating the diverse roles that RNA plays within cells and organisms. RNA analysis can reveal alternative splicing events, where different exons are included or excluded from mRNA transcripts. This process generates multiple protein isoforms from a single gene. RNA-Seq is particularly valuable for studying alternative splicing. In clinical settings, RNA analysis is used for diagnostic purposes, such as detecting viral RNA in infectious diseases or assessing gene expression patterns to guide treatment decisions. Continuous advancements in RNA sequencing technologies, such as next-generation sequencing (NGS) and single-cell RNA sequencing (scRNA-seq), have expanded the capabilities of RNA analysis. 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.
Market Overview | |
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Forecast Period | 2026-2030 |
Market Size 2024 | USD 9.68 Billion |
Market Size 2030 | USD 14.38 Billion |
CAGR 2025-2030 | 9.78% |
Fastest Growing Segment | Real-time-PCR (qPCR) |
Largest Market | North America |
Key Market Drivers
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.
Key Market Challenges
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.
Key Market Trends
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.
In this report, the Global RNA Analysis Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:
Company Profiles: Detailed analysis of the major companies present in the Global RNA Analysis Market.
Global RNA Analysis Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report: