PUBLISHER: TechSci Research | PRODUCT CODE: 1796840
PUBLISHER: TechSci Research | PRODUCT CODE: 1796840
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Global Transcriptomics Market was valued at USD 7.53 billion in 2024 and is expected to reach USD 10.28 billion by 2030 with a CAGR of 5.33% during the forecast period. The global market for Transcriptomics is experiencing significant growth, driven by the growing application of RNA sequencing and NGS for the diagnosis of genetic illness in the developing countries. Additionally, growing initiative and investments by government organizations for developing new technology, with the introduction of new microarray technology have significantly increased the demand for transcriptomics across different parts of the globe. Additionally, the rising adoption of new technology and growing demand for personalized medicine for treatments are further expected to increase the demand for transcriptomics, thereby fuelling the market growth.
Market Overview | |
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Forecast Period | 2026-2030 |
Market Size 2024 | USD 7.53 Billion |
Market Size 2030 | USD 10.28 Billion |
CAGR 2025-2030 | 5.33% |
Fastest Growing Segment | Next Generation Sequencing |
Largest Market | North America |
Furthermore, increasing number of biopharmaceutical companies and growing expenditure on research and development are further expected to increase the demand for transcriptomics, thereby supporting the market growth. Every 4 1/2 minutes, a baby is born with a birth defect in United States every year and affects 1 in every 33 babies born in the United States each year. That translates into about 120,000 babies. Transcriptomics is the study of the transcriptome, which is the complete set of RNA transcripts that are produced by the genome.
Key Market Drivers
Advancements in High-Throughput Sequencing Technologies
The rapid evolution and increased affordability of high-throughput sequencing technologies-particularly RNA sequencing (RNA-seq)-have significantly propelled the growth of the global transcriptomics market. RNA-seq enables the precise quantification of transcripts and discovery of novel RNA species across various sample types, thereby facilitating large-scale gene expression profiling in both basic research and clinical applications. Governments across the globe are investing heavily in next-generation sequencing (NGS) infrastructure. For instance, the U.S. National Institutes of Health (NIH) allocated over $1.5 billion in 2023 for initiatives linked to genomic medicine, including transcriptomics. NIH's All of Us Research Program has specifically emphasized the inclusion of RNA expression data to understand disease biology across diverse populations.
In addition, transcriptomics technologies are increasingly integrated into disease biomarker discovery, drug development pipelines, and personalized medicine. Projects such as the Genotype-Tissue Expression (GTEx) Project by the NIH have generated comprehensive gene expression datasets, enabling researchers to map tissue-specific gene expressions and variation across populations. The plummeting costs of RNA-seq-from over $1,000 per sample a decade ago to less than $100 today-have democratized access to these technologies. This price drop is particularly impactful for academic institutions and biotech companies in developing nations, driving adoption at a global scale. Moreover, automation in sample preparation, data analysis, and bioinformatics pipelines further accelerates throughput and reduces labor costs, enhancing scalability.
Key Market Challenges
Complex Data Interpretation and Bioinformatics Bottlenecks
Despite the advances in transcriptomic technologies, data complexity and bioinformatics limitations remain substantial challenges hindering market growth. Transcriptomic analyses-especially using RNA-seq-generate enormous volumes of data, often reaching hundreds of gigabytes per sample. Extracting meaningful insights from these datasets requires sophisticated algorithms, powerful computational infrastructure, and expertise in bioinformatics. The European Bioinformatics Institute (EMBL-EBI) and NIH's National Center for Biotechnology Information (NCBI) host large-scale transcriptomic repositories such as GEO (Gene Expression Omnibus) and ArrayExpress. However, interpreting this data requires integration with proteomics, genomics, and clinical phenotypes, which adds further layers of complexity. For many healthcare providers and small research labs, this technical requirement becomes a major barrier.
According to a 2022 survey by the Global Alliance for Genomics and Health (GA4GH), more than 60% of genomics labs identified a shortage of bioinformatics talent as a significant operational bottleneck. Furthermore, inconsistencies in data normalization, quality control, and analysis pipelines often lead to irreproducible results, limiting the translational utility of transcriptomics in clinical settings. Another related challenge is the need for standardized bioinformatics workflows. While initiatives such as the FDA's SEQC (Sequencing Quality Control) Project aim to establish quality benchmarks, globally accepted standards for transcriptomic data interpretation are still evolving. Until data analysis becomes more user-friendly and standardized across platforms, the complexity of transcriptomic datasets will continue to restrain broader adoption in clinical and research domains.
Key Market Trends
Integration of Multi-Omics Approaches in Disease Research
A growing trend in transcriptomics is its integration with other omics technologies, including genomics, proteomics, epigenomics, and metabolomics, to provide a holistic view of biological systems. This multi-omics approach enhances the understanding of complex disease mechanisms by correlating gene expression profiles with protein activity, epigenetic modifications, and metabolic changes. The U.S. National Cancer Institute (NCI) has launched several multi-omics initiatives, including the Human Tumor Atlas Network (HTAN), which maps tumors using transcriptomics, genomics, and imaging. The goal is to understand how tumors evolve over time and how patients respond to therapy, enabling the development of personalized treatment plans.
Similarly, the UK Biobank, backed by the UK government and Wellcome Trust, is increasingly adding transcriptomic data to its already vast genomic and clinical dataset. This allows researchers to connect gene expression patterns with real-world disease outcomes in a population-scale setting. This trend is not limited to developed countries. India's GenomeIndia project, supported by the Department of Biotechnology, is also moving towards the integration of transcriptomics with proteomic and metabolomic data to understand ethnic-specific disease vulnerabilities. Such integrative research helps in discovering novel drug targets, stratifying patients for clinical trials, and predicting disease progression. With advancements in artificial intelligence (AI) and machine learning (ML), multi-omics data integration is becoming more feasible, driving deeper biological insights and fueling demand for transcriptomic technologies.
In this report, the Global Transcriptomics 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 presents in the Global Transcriptomics Market.
Global Transcriptomics 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: