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市場調査レポート
商品コード
1691713
RNA分析市場- 世界の産業規模、シェア、動向、機会、予測、製品別、技術別、用途別、最終用途別、地域別セグメント、競合、2020年~2030年RNA 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 |
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カスタマイズ可能
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RNA分析市場- 世界の産業規模、シェア、動向、機会、予測、製品別、技術別、用途別、最終用途別、地域別セグメント、競合、2020年~2030年 |
出版日: 2025年03月24日
発行: TechSci Research
ページ情報: 英文 184 Pages
納期: 2~3営業日
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RNA分析の世界市場規模は2024年に96億8,000万米ドルとなり、予測期間中のCAGRは9.78%で2030年には143億8,000万米ドルに達すると予測されています。
RNA分析は、RNA(リボ核酸)分子の構造、機能、制御、発現パターンを理解するための研究を含む分子生物学およびゲノミクスの広範な分野です。RNAは分子生物学のセントラルドグマにおいて重要な分子であり、DNAとタンパク質合成の中間的な役割を果たしています。RNA分析は、RNAが細胞や生物の中で果たす多様な役割を解明することを目的とした様々な技術やアプローチを網羅しています。RNA分析は、mRNA転写産物から異なるエクソンが含まれたり除外されたりする代替スプライシングイベントを明らかにすることができます。このプロセスにより、1つの遺伝子から複数のタンパク質アイソフォームが生成されます。RNA-Seqはalternative splicingを調べるのに特に有用です。臨床の場では、RNA分析は、感染症におけるウイルスRNAの検出や、治療決定の指針となる遺伝子発現パターンの評価などの診断目的に使用されます。次世代シーケンシング(NGS)やシングルセルRNAシーケンシング(scRNA-seq)など、RNAシーケンシング技術の絶え間ない進歩により、RNA分析の機能は拡大しています。シーケンス精度、スループットの向上、コスト削減により、研究や臨床アプリケーションでの採用が進んでいます。RNA分析は、がん研究において広く使用されており、がんバイオマーカーの同定、腫瘍の不均一性の研究、標的療法の開発に役立っています。がんの有病率の増加により、RNAベースの診断および治療アプローチの需要が高まっています。mRNAワクチンやRNA干渉(RNAi)療法を含むRNAベースの治療薬の開発は、大きな勢いを得ています。このため、治療デザインの最適化や治療反応のモニタリングのためにRNA分析への関心が高まっています。RNA分析は、感染症のサーベイランスやモニタリングにおいて重要な役割を果たしています。COVID-19のパンデミックは、RNAベースの診断の重要性と迅速かつ正確な病原体検出の必要性を浮き彫りにしました。
市場概要 | |
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予測期間 | 2026-2030 |
市場規模:2024年 | 96億8,000万米ドル |
市場規模:2030年 | 143億8,000万米ドル |
CAGR:2025年~2030年 | 9.78% |
急成長セグメント | リアルタイムPCR(qPCR) |
最大市場 | 北米 |
RNAシーケンス技術の進歩
シングルセルRNAシーケンスの複雑さ
バイオインフォマティクスとデータ解析
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: