NGS-Based RNA-Sequencing Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Product and Services (RNA Sequencing Platforms and Consumables, Sample Preparation Products, RNA Sequencing Services, Data Analysis, Storage and

NGS-Based RNA-Sequencing Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Product and Services (RNA Sequencing Platforms and Consumables, Sample Preparation Products, RNA Sequencing Services, Data Analysis, Storage and Management), By Technology (Sequencing By Synthesis, Ion Semiconductor Sequencing, Single-Molecule Real-Time Sequencing, Nanopore Sequencing), By Application (Expression Profiling Analysis, Small RNA Sequencing, De Novo Transcriptome Assembly, Variant Calling and Transcriptome Epigenetics), By End User (Research and Academia, Hospitals and Clinics, Pharmaceutical and Biotechnology Companies, Others), By Region, and By Competition, 2019-2029F

Global NGS-Based RNA-Sequencing Market was valued at USD 2.67 billion in 2023 and will see an impressive growth in the forecast period at a CAGR of 6.13% through 2029. Next-Generation Sequencing (NGS)-Based RNA-Sequencing, often abbreviated as RNA-Seq, is a powerful technique used to analyze the transcriptome, which refers to the complete set of RNA molecules in a cell or tissue at a specific time point. RNA-Seq allows researchers to investigate gene expression levels, alternative splicing patterns, RNA modifications, and other transcriptomic features with high throughput and resolution. The RNA molecules of interest are extracted from cells or tissues using specialized protocols that preserve RNA integrity and minimize degradation. Total RNA, which includes messenger RNA (mRNA), non-coding RNA (e.g., microRNA, long non-coding RNA), and ribosomal RNA (rRNA), is isolated and purified from the sample. The isolated RNA molecules are converted into a sequencing library through a series of enzymatic reactions. During library preparation, the RNA is fragmented into smaller pieces, reverse transcribed into complementary DNA (cDNA) using reverse transcriptase enzymes, and adapters are ligated to the cDNA fragments to enable sequencing. Various library preparation kits and protocols are available to accommodate different RNA-Seq applications, such as stranded RNA-Seq, poly(A)-enriched RNA-Seq, and total RNA-Seq. The prepared RNA-Seq libraries are sequenced using high-throughput NGS platforms, such as Illumina, Ion Torrent, or PacBio sequencers. During sequencing, fluorescently labeled nucleotides are incorporated into complementary strands of DNA or RNA molecules, generating millions to billions of short sequencings reads in a massively parallel fashion.

Continuous advancements in next-generation sequencing (NGS) technologies have significantly improved the speed, accuracy, and cost-effectiveness of RNA sequencing. Innovations such as long-read sequencing, single-cell RNA sequencing, and real-time sequencing capabilities have expanded the applications and accessibility of RNA sequencing, driving market growth. The growing interest and investment in genomic research, particularly in areas such as transcriptomics and functional genomics, fuel the demand for RNA sequencing technologies. Researchers across various fields, including molecular biology, medicine, agriculture, and biotechnology, rely on RNA sequencing to investigate gene expression, splicing variants, RNA modifications, and regulatory networks. RNA sequencing is increasingly utilized for clinical diagnostics, particularly in oncology and rare diseases. The ability to detect gene fusions, mutations, and expression patterns using RNA sequencing aids in cancer diagnosis, prognosis, and treatment selection. Additionally, RNA sequencing facilitates the identification of causative genetic variants in rare and undiagnosed diseases, driving its integration into clinical practice and molecular pathology.

Key Market Drivers

Advancements in Sequencing Technologies

NGS technologies represent a paradigm shift in DNA sequencing, allowing for high-throughput sequencing of DNA and RNA molecules. NGS platforms, such as Illumina's sequencing systems, enable researchers to sequence millions of DNA fragments or RNA transcripts in parallel, significantly increasing sequencing speed and throughput compared to traditional Sanger sequencing methods. Single-cell sequencing technologies enable the profiling of individual cells' genomes, transcriptomes, and epigenomes with high resolution. These technologies, including single-cell RNA sequencing (scRNA-seq), single-cell DNA sequencing (scDNA-seq), and single-cell ATAC-seq (scATAC-seq), provide insights into cellular heterogeneity, developmental processes, and disease mechanisms at the single-cell level. Long-read sequencing technologies, such as those offered by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies, generate sequencing reads that span thousands to tens of thousands of base pairs. Long-read sequencing facilitates the detection of structural variations, complex genomic rearrangements, and full-length transcripts, overcoming limitations associated with short-read sequencing technologies.

Real-time sequencing platforms, such as nanopore sequencing by Oxford Nanopore Technologies, enable the direct, label-free detection of nucleic acids as they pass through nanopores. Real-time sequencing provides rapid turnaround times, enables dynamic monitoring of biological processes, and supports applications such as pathogen detection, environmental surveillance, and RNA transcript analysis. Epigenetic sequencing technologies, including DNA methylation sequencing (e.g., bisulfite sequencing) and chromatin immunoprecipitation sequencing (ChIP-seq), allow researchers to study epigenetic modifications and chromatin dynamics at genome-wide scales. These technologies provide insights into gene regulation, cell differentiation, and disease mechanisms by profiling DNA methylation patterns, histone modifications, and transcription factor binding sites. Metagenomic sequencing enables the comprehensive analysis of microbial communities and environmental samples without the need for culture-based methods. Metagenomic sequencing technologies, such as shotgun metagenomics and 16S rRNA gene sequencing, facilitate the identification of microbial species, functional gene annotation, and microbiome characterization in diverse habitats, including the human gut, soil, water, and air. This factor will help in the development of the Global NGS-Based RNA-Sequencing Market.

Rapid Growth of Genomic Research

Genomic research encompasses a wide range of applications, including transcriptomics, epigenomics, metagenomics, and comparative genomics. RNA sequencing, specifically, provides insights into gene expression patterns, alternative splicing events, RNA modifications, and regulatory networks. Researchers leverage RNA sequencing data to study development, disease mechanisms, drug responses, and evolutionary relationships across diverse biological systems. Advances in NGS technologies have democratized genomic research by enabling high-throughput sequencing of DNA and RNA molecules at unprecedented speed and scale. NGS platforms, such as Illumina's sequencing systems and those offered by other manufacturers, facilitate the generation of large volumes of sequencing data with high accuracy and resolution. These technological advancements have expanded the accessibility of RNA sequencing to researchers in academia, industry, and clinical settings. The decreasing costs of sequencing technologies and associated reagents have made RNA sequencing more affordable and accessible to research laboratories worldwide. As the price per base pair continues to decline, researchers can conduct large-scale RNA sequencing experiments, population-based studies, and longitudinal analyses without significant financial constraints. The affordability of RNA sequencing drives its widespread adoption across diverse research disciplines and institutions.

Genomic research increasingly integrates RNA sequencing with other omics technologies, such as DNA sequencing, epigenetic profiling, proteomics, and metabolomics. Multi-omics approaches enable comprehensive molecular profiling and systems-level analysis of biological systems, providing a holistic view of gene regulation, signaling pathways, and cellular interactions. RNA sequencing data complement other omics datasets, enhancing our understanding of complex biological processes and disease phenotypes. Genomic research findings have translational implications for healthcare, agriculture, environmental science, and biotechnology. RNA sequencing technologies play a crucial role in translational research and clinical applications, including biomarker discovery, diagnostic assay development, patient stratification, and treatment optimization. RNA sequencing data informs precision medicine approaches, facilitates the identification of therapeutic targets, and support evidence-based decision-making in clinical practice. This factor will pace up the demand of the Global NGS-Based RNA-Sequencing Market.

Expanding Applications in Clinical Diagnostics

NGS-based RNA sequencing enables precise molecular characterization of diseases, aiding in the development of personalized treatment strategies. By profiling RNA expression patterns, identifying genetic mutations, and detecting fusion genes, RNA sequencing helps clinicians tailor therapies to individual patients based on their unique genetic profiles. RNA sequencing is instrumental in cancer diagnostics and prognostics. It allows for the identification of gene expression signatures associated with different cancer types, tumor subtypes, and disease progression stages. RNA sequencing can detect driver mutations, predict treatment responses, monitor minimal residual disease, and identify drug resistance mechanisms, guiding clinical decision-making in oncology. NGS-based RNA sequencing facilitates the diagnosis of rare and undiagnosed diseases by identifying causative genetic variants, including point mutations, insertions/deletions, and copy number variations. RNA sequencing can uncover pathogenic mutations affecting gene expression, splicing, and regulatory elements, providing insights into disease mechanisms, and informing genetic counseling and family planning. RNA sequencing is increasingly used in the diagnosis and surveillance of infectious diseases, including viral infections, bacterial pathogens, and fungal pathogens. RNA sequencing can detect microbial RNA transcripts, viral RNA genomes, and host immune responses, enabling the rapid identification and characterization of infectious agents, monitoring of disease outbreaks, and assessment of antimicrobial resistance patterns.

RNA sequencing plays a crucial role in pharmacogenomics by identifying genetic variants associated with drug metabolism, drug efficacy, and adverse drug reactions. RNA sequencing data can predict individual responses to pharmacotherapy, optimize drug dosing regimens, and minimize adverse drug events, enhancing patient safety and treatment outcomes in clinical practice. RNA sequencing is utilized in non-invasive prenatal testing to detect fetal chromosomal abnormalities, such as trisomy 21 (Down syndrome), trisomy 18 (Edwards syndrome), and trisomy 13 (Patau syndrome). RNA sequencing of cell-free fetal RNA in maternal blood enables early detection of genetic disorders, reducing the need for invasive procedures like amniocentesis and chorionic villus sampling. RNA sequencing enables the analysis of circulating RNA biomarkers in blood, urine, and other body fluids for cancer detection, monitoring treatment response, and assessing disease recurrence. Liquid biopsy-based RNA sequencing offers a minimally invasive alternative to tissue biopsies and facilitates real-time monitoring of disease dynamics and therapeutic interventions. This factor will accelerate the demand of the Global NGS-Based RNA-Sequencing Market.

Key Market Challenges

Data Analysis and Interpretation Complexity

RNA-sequencing generates massive amounts of raw sequencing data that require sophisticated bioinformatics tools and computational expertise for analysis and interpretation. Analyzing transcriptomic data involves multiple steps, including quality control, read alignment, transcript quantification, differential gene expression analysis, pathway analysis, and functional annotation. Researchers often need specialized training in bioinformatics and computational biology to effectively analyze RNA-sequencing data and extract meaningful biological insights. There is a lack of standardized data analysis pipelines for RNA-sequencing data, leading to variability in analysis methodologies and results across different studies and laboratories. Researchers may use different software tools, algorithms, and parameters for data processing and analysis, which can impact the reproducibility and comparability of results. Establishing consensus guidelines and best practices for RNA-sequencing data analysis is essential for promoting consistency and transparency in research findings. RNA-sequencing data are inherently complex, reflecting the dynamic nature of gene expression and alternative splicing events across different biological conditions and cell types. Analyzing transcriptomic data requires accounting for various sources of variability, including technical noise, biological heterogeneity, and experimental confounders. Moreover, identifying biologically relevant signals amidst background noise and false positives poses challenges for data interpretation and validation.

Integrating RNA-sequencing data with other omics data types, such as genomics, proteomics, and metabolomics, adds another layer of complexity to data analysis and interpretation. Integrated multi-omics analyses enable researchers to gain a more comprehensive understanding of biological systems and disease mechanisms. However, integrating heterogeneous data sets from different experimental platforms and data sources requires specialized computational methods and tools for data integration, normalization, and statistical analysis. Ensuring the reproducibility and reliability of RNA-sequencing results is a critical concern in the field. Researchers must implement rigorous quality control measures throughout the experimental workflow to minimize technical artifacts, batch effects, and systematic biases that can confound data analysis and interpretation. Standardizing quality control metrics and reporting guidelines for RNA-sequencing experiments can help improve data reproducibility and facilitate data sharing and meta-analysis efforts.

Sample Heterogeneity and Complexity

Biological samples, particularly tissues and organs, consist of diverse cell populations with distinct gene expression profiles. Studying heterogeneous samples using RNA-sequencing requires methods to capture and analyze gene expression patterns at the single-cell or subpopulation level. Bulk RNA-sequencing may mask cell-specific gene expression signatures, leading to a loss of resolution and biological insights. Tumors are characterized by intratumoral heterogeneity, where different regions of the tumor exhibit distinct molecular profiles and cellular phenotypes. RNA-sequencing studies of tumors must account for spatial and temporal variations in gene expression, as well as the presence of rare cell populations, tumor subclones, and microenvironmental factors. Understanding tumor heterogeneity is critical for identifying therapeutic targets, predicting treatment response, and monitoring disease progression.

Biological systems exhibit dynamic changes in gene expression over time in response to developmental cues, environmental stimuli, and disease processes. Temporal dynamics pose challenges for RNA-sequencing experiments, as gene expression patterns may vary across different time points or experimental conditions. Longitudinal studies and time-series analyses are necessary to capture temporal changes in gene expression and unravel regulatory networks underlying dynamic biological processes. Biological samples are influenced by environmental factors, experimental conditions, and technical artifacts that can introduce variability and confound RNA-sequencing results. Sources of variation include sample processing methods, RNA extraction protocols, library preparation techniques, sequencing platforms, and computational pipelines. Controlling environmental and experimental factors is essential for minimizing batch effects, systematic biases, and false positives in RNA-sequencing experiments. Biological samples may contain rare cell populations or subtypes with unique gene expression profiles that are challenging to detect using bulk RNA-sequencing approaches. Single-cell RNA-sequencing (scRNA-seq) technologies enable the profiling of individual cells within heterogeneous populations, allowing researchers to identify rare cell types, characterize cell-to-cell variability, and dissect cellular heterogeneity at high resolution.

Key Market Trends

Increasing Adoption of NGS in Transcriptomics

NGS-based RNA-sequencing enables researchers to study gene expression patterns across the entire transcriptome in a high-throughput and unbiased manner. Unlike microarray-based methods, which are limited to the detection of predefined probes, RNA-sequencing provides greater sensitivity and dynamic range for detecting transcripts, alternative splicing events, and novel RNA isoforms. The transcriptome is highly complex, consisting of coding and non-coding RNAs with diverse functions and regulatory roles. NGS-based RNA-sequencing allows researchers to profile gene expression at single-nucleotide resolution, identify splice variants, quantify transcript abundance, and characterize RNA modifications with high precision. This resolution enables the discovery of novel transcripts, regulatory elements, and disease-associated biomarkers. NGS-based RNA-sequencing is widely used across various research areas, including basic biology, developmental biology, cancer biology, neuroscience, immunology, and infectious diseases. Transcriptomic studies provide insights into gene regulatory networks, cellular differentiation, disease mechanisms, drug responses, and biomarker discovery, driving the adoption of RNA-sequencing technologies in diverse scientific disciplines.

NGS-based RNA-sequencing is often integrated with other omics data types, such as genomics, epigenomics, proteomics, and metabolomics, to obtain a comprehensive understanding of biological systems and disease processes. Integrated multi-omics approaches enable researchers to correlate gene expression patterns with genetic variations, epigenetic modifications, protein abundance, and metabolic pathways, facilitating systems-level analyses and translational research applications. NGS-based RNA-sequencing is increasingly used in clinical research and diagnostics, particularly in the field of precision medicine. Transcriptomic profiling of patient samples enables the identification of disease-specific gene expression signatures, patient stratification based on molecular subtypes, and prediction of treatment responses. RNA-sequencing data also informs the development of targeted therapies, biomarker-driven clinical trials, and personalized treatment strategies for cancer and other complex diseases.

Segmental Insights

Technology Insights

The Nanopore Sequencing segment is projected to experience rapid growth in the Global NGS-Based RNA-Sequencing Market during the forecast period. Nanopore sequencing technology offers the advantage of producing long read lengths, which enables the direct sequencing of RNA molecules without the need for fragmentation or amplification. Long-read RNA sequencing allows for the characterization of full-length transcripts, including isoforms and splice variants, providing valuable insights into RNA structure, function, and regulation. Researchers and clinicians increasingly recognize the importance of long-read sequencing in accurately capturing complex RNA landscapes, driving the demand for nanopore sequencing platforms. One of the distinctive features of nanopore sequencing is its ability to perform real-time, single-molecule sequencing. This real-time capability allows researchers to observe RNA molecules as they pass through nanopores, enabling dynamic monitoring of RNA modifications, kinetics of RNA processing events, and RNA-protein interactions. Real-time nanopore sequencing offers unprecedented insights into RNA biology and gene expression dynamics, making it an attractive tool for a wide range of research applications. Nanopore sequencing platforms, such as those offered by Oxford Nanopore Technologies, are known for their portability and ease of use. These compact, handheld devices enable RNA sequencing to be performed in various settings, including fieldwork, point-of-care diagnostics, and resource-limited environments. The accessibility and flexibility of nanopore sequencing systems democratize RNA sequencing and empower researchers and clinicians worldwide to conduct studies and diagnostics in diverse settings. Nanopore sequencing is versatile and applicable to a wide range of RNA sequencing applications, including transcriptome profiling, RNA modification analysis, RNA structural characterization, and viral RNA detection. The versatility of nanopore sequencing technology allows researchers to address diverse research questions and explore RNA biology in unprecedented detail, fostering its widespread adoption across academic, clinical, and industrial settings.

Regional Insights

North America emerged as the dominant region in the Global NGS-Based RNA-Sequencing Market in 2023. North America, particularly the United States, is a leading hub for biomedical research and innovation. The region is home to numerous prestigious universities, research institutions, and biotechnology companies that actively invest in genomics and RNA sequencing technologies. This concentration of expertise and resources fosters the development and adoption of next-generation sequencing (NGS) techniques, including RNA sequencing. North America boasts a robust biotechnology and pharmaceutical industry, comprising both established companies and startups. These organizations conduct extensive research and development (R&D) in areas such as drug discovery, diagnostics, and personalized medicine, all of which heavily rely on RNA sequencing technologies. The demand for NGS-based RNA sequencing solutions is driven by the need to understand gene expression patterns, identify therapeutic targets, and develop novel treatments for diseases.

Key Market Players

• Illumina Inc.
• Thermo Fischer Scientific Inc.
• Oxford Nanopore Technologies plc
• Agilent Technologies, Inc.
• PerkinElmer Inc
• QIAGEN N.V.
• Eurofins Scientific SE
• F. Hoffmann-La Roche Ltd
• Takara Bio Inc.
• Azenta Life Sciences

Report Scope:

In this report, the Global NGS-Based RNA-Sequencing Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• NGS-Based RNA-Sequencing Market, By Product and Services:
• RNA Sequencing Platforms and Consumables
• Sample Preparation Products
• RNA Sequencing Services
• Data Analysis
• Storage and Management
• NGS-Based RNA-Sequencing Market, By Technology:
• Sequencing by Synthesis
• Ion Semiconductor Sequencing
• Single-Molecule Real-Time Sequencing
• Nanopore Sequencing
• NGS-Based RNA-Sequencing Market, By Application:
• Expression Profiling Analysis
• Small RNA Sequencing
• De Novo Transcriptome Assembly
• Variant Calling and Transcriptome Epigenetics
• NGS-Based RNA-Sequencing Market, By End User:
• Research and Academia
• Hospitals and Clinics
• Pharmaceutical and Biotechnology Companies
• Others

NGS-Based RNA-Sequencing Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• Germany
• United Kingdom
• France
• Italy
• Spain
• Asia-Pacific
• China
• Japan
• India
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global NGS-Based RNA-Sequencing Market.

Company Information

• Detailed analysis and profiling of additional market players (up to five).

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