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NGS-Based Rna-Seq Market Size 2024-2028

The NGS-based RNA-seq market size is forecast to increase by USD 6.66 billion, at a CAGR of 20.52% between 2023 and 2028.

The market is witnessing significant growth, driven by the increased adoption of next-generation sequencing (NGS) methods for RNA-Seq analysis. The advanced capabilities of NGS techniques, such as high-throughput, cost-effectiveness, and improved accuracy, have made them the preferred choice for researchers and clinicians in various fields, including genomics, transcriptomics, and personalized medicine. However, the market faces challenges, primarily from the lack of clinical validation on direct-to-consumer genetic tests. As the use of NGS technology in consumer applications expands, ensuring the accuracy and reliability of results becomes crucial.
The absence of standardized protocols and regulatory oversight in this area poses a significant challenge to market growth and trust. Companies seeking to capitalize on market opportunities must focus on addressing these challenges through collaborations, partnerships, and investments in research and development to ensure the clinical validity and reliability of their NGS-based RNA-Seq offerings.

What will be the Size of the NGS-based RNA-Seq market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by advancements in NGS technology and its applications across various sectors. Spatial transcriptomics, a novel approach to studying gene expression in its spatial context, is gaining traction in disease research and precision medicine. Splice junction detection, a critical component of RNA-seq data analysis, enhances the accuracy of gene expression profiling and differential gene expression studies. Cloud computing plays a pivotal role in handling the massive amounts of data generated by NGS platforms, enabling real-time data analysis and storage. Enrichment analysis, gene ontology, and pathway analysis facilitate the interpretation of RNA-seq data, while data normalization and quality control ensure the reliability of results.

Precision medicine and personalized therapy are key applications of RNA-seq, with single-cell RNA-seq offering unprecedented insights into the complexities of gene expression at the single-cell level. Read alignment and variant calling are essential steps in RNA-seq data analysis, while bioinformatics pipelines and RNA-seq software streamline the process. NGS technology is revolutionizing drug discovery by enabling the identification of biomarkers and gene fusion detection in various diseases, including cancer and neurological disorders. RNA-seq is also finding applications in infectious diseases, microbiome analysis, environmental monitoring, agricultural genomics, and forensic science. Sequencing costs are decreasing, making RNA-seq more accessible to researchers and clinicians.

The ongoing development of sequencing platforms, library preparation, and sample preparation kits continues to drive innovation in the field. The dynamic nature of the market ensures that it remains a vibrant and evolving field, with ongoing research and development in areas such as data visualization, clinical trials, and sequencing depth.

How is this NGS-based RNA-Seq industry segmented?

The NGS-based RNA-seq industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Acamedic and research centers
  Clinical research
  Pharma companies
  Hospitals


Technology

  Sequencing by synthesis
  Ion semiconductor sequencing
  Single-molecule real-time sequencing
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Singapore


  Rest of World (ROW)

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By End-user Insights

The acamedic and research centers segment is estimated to witness significant growth during the forecast period.

The global next-generation sequencing (NGS) market for RNA sequencing (RNA-Seq) is primarily driven by academic and research institutions, including those from universities, research institutes, government entities, biotechnology organizations, and pharmaceutical companies. These institutions utilize NGS technology for various research applications, such as whole-genome sequencing, epigenetics, and emerging fields like agrigenomics and animal research, to enhance crop yield and nutritional composition. NGS-based RNA-Seq plays a pivotal role in translational research, with significant investments from both private and public organizations fueling its growth. The technology is instrumental in disease research, enabling the identification

Accurate and robust somatic mutation detection is essential for cancer treatment, diagnostics and research. Various analysis pipelines give different results and thus should be systematically evaluated. In this study, we benchmarked 5 commonly-used somatic mutation calling pipelines (VarScan, VarDictJava, Mutect2, Strelka2 and FANSe) for their precision, recall and speed, using standard benchmarking datasets based on a series of real-world whole-exome sequencing datasets. All the 5 pipelines showed very high precision in all cases, and high recall rate in mutation rates higher than 10%. However, for the low frequency mutations, these pipelines showed large difference. FANSe showed the highest accuracy (especially the sensitivity) in all cases, and VarScan and VarDictJava outperformed Mutect2 and Strelka2 in low frequency mutations at all sequencing depths. The flaws in filter was the major cause of the low sensitivity of the four pipelines other than FANSe. Concerning the speed, FANSe pipeline was 8.8∼19x faster than the other pipelines. Our benchmarking results demonstrated performance of the somatic calling pipelines and provided a reference for a proper choice of such pipelines in cancer applications.

Galaxy is an open source, web-based platform for data intensive biomedical research. It makes accessible bioinformatics applications to users lacking programming skills, enabling them to easily build analysis workflows for NGS data.

The course "Exome analysis using Galaxy" is aimed at PhD student, biologists, clinicians and researchers who are analysing, or need to analyse in the near future, high throughput exome sequencing data. The aim of the course is to make participants familiarise with the Galaxy platform and prepare them to work independently, using state-of-the art tools for the analysis of exome sequencing data.

The course will be delivered using a mixture of lectures and computer based hands-on practical sessions. Lectures will provide an up-to-date overview of the strategies for the analysis of exome next-generation experiments, starting from the raw sequence data. Analyses include sequence quality control, alignment to a reference genome, refinement of aligned sequences, variant calling, annotation and interpretation, and tools for visual inspection of results. Participants will apply the knowledge gained during the course to the analysis of Illumina’s real exome datasets, and implement workflows to reproduce the complete analysis. After the course, participants will be able to create pipeline for their individual analyses.

Those are the needed datasets for this course.

Preliminary NGS prediction and PCR or ELISA detection.

The importance of next generation sequencing (NGS) rises in cancer research as accessing this key technology becomes easier for researchers. The sequence data created by NGS technologies must be processed by various bioinformatics algorithms within a pipeline in order to convert raw data to meaningful information. Mapping and variant calling are the two main steps of these analysis pipelines, and many algorithms are available for these steps. Therefore, detailed benchmarking of these algorithms in different scenarios is crucial for the efficient utilization of sequencing technologies. In this study, we compared the performance of twelve pipelines (three mapping and four variant discovery algorithms) with recommended settings to capture single nucleotide variants. We observed significant discrepancy in variant calls among tested pipelines for different heterogeneity levels in real and simulated samples with overall high specificity and low sensitivity. Additional to the individual evaluation of pipelines, we also constructed and tested the performance of pipeline combinations. In these analyses, we observed that certain pipelines complement each other much better than others and display superior performance than individual pipelines. This suggests that adhering to a single pipeline is not optimal for cancer sequencing analysis and sample heterogeneity should be considered in algorithm optimization.

Analysis of four samples of GEO accession GSE119855 with the IBU RNA-seq pipeline

The global next-generation sequencing (NGS) software market is projected to reach a value of USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period (2023-2033). The growth of this market is primarily driven by the increasing adoption of NGS technologies in various research areas, including genomics, transcriptomics, and epigenomics. NGS software plays a crucial role in processing and analyzing the vast amount of data generated by NGS platforms, enabling researchers to identify genetic variations, diagnose diseases, and develop targeted therapies. The rising prevalence of chronic diseases, the demand for personalized medicine, and the advancements in computational biology further contribute to the market expansion. Key trends in the NGS software market include the emergence of cloud-based solutions, the adoption of artificial intelligence (AI) and machine learning (ML) algorithms, and the integration of NGS software with bioinformatics pipelines. Cloud-based NGS software offers scalability, flexibility, and reduced infrastructure costs, making it accessible to a wider range of users. AI and ML algorithms enhance the accuracy and efficiency of data analysis, enabling researchers to extract valuable insights from complex NGS datasets. The integration of NGS software with bioinformatics pipelines provides a comprehensive platform for data management, analysis, and interpretation, streamlining the research workflow. Major players in the NGS software market include BGI International, Thermo Fisher Scientific, IBM, PerkinElmer, Illumina, Beckman Coulter Genomics, GATC Biotech Ag, Bina Technologies, DNASTAR, and Genomatix Software. These companies are investing in research and development to introduce innovative software solutions that meet the evolving needs of researchers.

INDELs were called using four variant callers (for each mapping result) using the same settings as for SNV calling. The percentage given in brackets is the fraction of INDELs having an entry in dbSNP version 137.

This dataset contains the TaxaSE bacterial taxonomic annotation pipeline (including its source code and associated data files). Insilico data generated from SILVA Release 123 database is also provided here, consisting of both whole SILVA and Removal of Taxa based validation approaches, which were used to compare Shannon entropy based sequence similarity approach to Percentage Identity (via USEARCH v7.0.1090 32bit, see Edgar 2010). Lastly, the raw FASTQ files as well as processed FASTA files from Sugarcane (Saccharum Spp.) are included, consisting of samples from soil, rhizosphere, root and stem sub-habitats, alongside results generated in QIIME 1.9.1 (Caporaso et.al 2010).

The quality of all Illumina R1 and R2 reads were assessed visually using FASTQC (Andrews 2016), merged using FLASH (Magoč & Salzberg 2011) and converted to FASTA format using QIIME’s “convert_fastaqual_fastq.py” script. Alpha diversity and beta diversity analysis were performed in QIIME, with TaxaSE results converted to QIIME compatible format for comparison. Insilico data was generated using MicroSim simulator from SILVA 123 Release database. Sugarcane leaf, stalk, root and rhizosphere soil samples were collected by Dr. Kelly Hamonts at Hawkesbury Institute for the Environment, Western Sydney University, Australia, in November 2014 from eight sugarcane fields growing three sugarcane varieties (KQ228, MQ239 and Q240) near Ingham, Queensland, Australia.

In each field, 3 stools were randomly selected and samples were collected from 2 plants per stool. Samples were snap-frozen in liquid nitrogen on the field, transported to the laboratory on dry ice and stored at -80C. Frozen sugarcane tissue samples were ground using mortar and pestle and DNA was extracted from the resulting powder using the MoBio PowerPlant DNA extraction kit, following the manufacturer’s instructions. The MoBIO PowerSoil DNA extraction kit was used to extract DNA from the soil samples. Bacterial 16S rRNA amplicon sequencing was performed by the NGS facility at Western Sydney University using Illumina Miseq (2x 301 bp PE) and the 341F/805R primer set.

The global microbiome sequencing services market is experiencing robust growth, driven by the increasing understanding of the microbiome's role in human health and disease. Advancements in sequencing technologies, such as next-generation sequencing (NGS), are significantly reducing costs and increasing throughput, making microbiome analysis more accessible to researchers, pharmaceutical companies, and healthcare providers. The pharmaceutical and biotech sectors are major drivers, leveraging microbiome sequencing to identify novel drug targets and develop personalized therapies for various conditions, including gastrointestinal disorders, autoimmune diseases, and even cancer. Academic institutions are also contributing significantly to the market's expansion through fundamental research and the development of innovative analytical tools. Regulatory support and increased funding for microbiome research further bolster market growth. While the market is currently dominated by sequencing by synthesis (SBS) methods, other technologies like sequencing by ligation are gaining traction due to their potential for specific applications. The market exhibits significant regional variations, with North America and Europe currently holding the largest market share due to the presence of well-established research infrastructure and a high concentration of key players. However, the Asia-Pacific region is projected to witness the fastest growth in the coming years, driven by increasing healthcare spending and rising awareness of microbiome-related health issues. Challenges remain, primarily related to data analysis and interpretation. The sheer volume of data generated by microbiome sequencing requires sophisticated bioinformatics tools and expertise for accurate and meaningful insights. Furthermore, standardization of protocols and data analysis pipelines is crucial for ensuring reproducibility and comparability of results across different studies and laboratories. Despite these hurdles, the market is poised for sustained growth, propelled by ongoing technological innovation, the increasing adoption of microbiome-based diagnostics and therapeutics, and a growing understanding of the complex interplay between the microbiome and human health. The diverse applications across research, diagnostics, and therapeutics suggest a broad and expanding market with significant future potential, particularly in personalized medicine and precision healthcare.

Datasets linked to publication "Revealing viral and cellular dynamics of HIV-1 at the single-cell level during early treatment periods", Otte et al 2023 published in Cell Reports Methods pre-ART (antiretroviral therapy) cryo-conserved and and whole blood specimen were sampled for HIV-1 virus reservoir determination in HIV-1 positive individuals from the Swiss HIV Study Cohort. Patients were monitored for proviral (DNA), poly-A transcripts (RNA), late protein translation (Gag and Envelope reactivation co-detection assay, GERDA) and intact viruses (golden standard: viral outgrowth assay, VOA). In this dataset we deposited the pipeline for the multidimensional data analysis of our newly established GERDA method, using DBScan and tSNE. For further comprehension NGS and Sanger sequencing data were attached as processed and raw data (GenBank).

Resubmitted to Cell Reports Methods (Jan-2023), accepted in principal (Mar-2023)

GERDA is a new detection method to decipher the HIV-1 cellular reservoir in blood (tissue or any other specimen). It integrates HIV-1 Gag and Env co-detection along with cellular surface markers to reveal 1) what cells still contain HIV-1 translation competent virus and 2) which marker the respective infected cells express. The phenotypic marker repertoire of the cells allow to make predictions on potential homing and to assess the HIV-1 (tissue) reservoir. All FACS data were acquired on a LSRFortessa BD FACS machine (markers: CCR7, CD45RA, CD28, CD4, CD25, PD1, IntegrinB7, CLA, HIV-1 Env, HIV-1 Gag) Raw FACS data (pre-gated CD4CD3+ T-cells) were arcsin transformed and dimensionally reduced using optsne. Data was further clustered using DBSCAN and either individual clusters were further analyzed for individual marker expression or expression profiles of all relevant clusters were analyzed by heatmaps. Sequences before/after therapy initiation and during viral outgrowth cultures were monitored for individuals P01-46 and P04-56 by Next-generation sequencing (NGS of HIV-1 Envelope V3 loop only) and by Sanger (single genome amplification, SGA)

data normalization code (by Julian Spagnuolo) FACS normalized data as CSV (XXX_arcsin.csv) OMIQ conText file (_OMIQ-context_XXX) arcsin normalized FACS data after optsne dimension reduction with OMIQ.ai as CSV file (XXXarcsin.csv.csv) R pipeline with codes (XXX_commented.R) P01_46-NGS and Sanger sequences P04_56-NGS and Sanger sequences

The single-cell sequencing market is experiencing robust growth, driven by advancements in technology, increasing research funding in life sciences, and the rising need for personalized medicine. The market's ability to analyze individual cells unlocks unprecedented insights into cellular heterogeneity, offering critical advantages in various fields like oncology, immunology, and neuroscience. This allows researchers to understand disease mechanisms at a much deeper level, paving the way for more effective diagnostics and therapeutics. The market is fragmented, with numerous key players including 10x Genomics, Illumina, and Fluidigm Corporation, each contributing to innovation and competition. While the exact market size for 2025 is unavailable, based on a conservative estimate assuming a CAGR of 15% (a reasonable figure given the industry's rapid pace of innovation) and considering a 2019 market size of approximately $1 billion, the market size in 2025 is projected to be around $2 billion. This substantial growth is anticipated to continue through 2033, further fueled by the decreasing cost of sequencing and the expanding applications of single-cell analysis. The key trends shaping the market include the development of more efficient and cost-effective platforms, the integration of single-cell sequencing with other omics technologies (e.g., genomics, transcriptomics, proteomics), and the growing adoption of cloud-based bioinformatics solutions for data analysis. However, challenges remain, primarily concerning the high cost of instrumentation and data analysis, the complexity of experimental workflows, and the need for standardized data analysis pipelines. Despite these challenges, the market's potential to revolutionize biological research and clinical applications ensures its sustained growth and continuous development. Addressing these challenges will be crucial for further market expansion and accessibility to researchers and clinicians globally.

ASA³P is an automatic and highly scalable assembly, annotation and higher-level analyses pipeline for closely related bacterial isolates. https://github.com/oschwengers/asap

ASA³P is a fully automatic, locally executable and scalable assembly, annotation and higher-level analysis pipeline creating results in standard bioinformatics file formats as well as sophisticated HTML5 documents. Its main purpose is the automatic processing of NGS WGS data of multiple closely related isolates, thus transforming raw reads into assembled and annotated genomes and finally gathering as much information on every single bacterial genome as possible. Per-isolate analyses are complemented by comparative insights. Therefore, the pipeline incorporates many best-in-class open source bioinformatics tools and thus minimizes the burden of ever-repeating tasks. Envisaged as a preprocessing tool it provides comprehensive insights as well as a general overview and comparison of analysed genomes along with all necessary result files for subsequent deeper analyses. All results are presented via modern HTML5 documents comprising interactive visualizations.

Schwengers et al, 2020 PLOS Comp Bio DOI:10.1371/journal.pcbi.1007134

Currently, there are many publicly available Next Generation Sequencing tools developed for variant annotation and classification. However, as modern sequencing technology produces more and more sequencing data, a more efficient analysis program is desired, especially for variant analysis. In this study, we updated SNPAAMapper, a variant annotation pipeline by converting perl codes to python for generating annotation output with an improved computational efficiency and updated information for broader applicability. The new pipeline written in Python can classify variants by region (Coding Sequence, Untranslated Regions, upstream, downstream, intron), predict amino acid change type (missense, nonsense, etc.), and prioritize mutation effects (e.g., synonymous > non-synonymous) while being faster and more efficient. Our new pipeline works in five steps. First, exon annotation files are generated. Next, the exon annotation files are processed, and gene mapping and feature information files are produced. Afterward, the python scrips classify the variants based on genomic regions and predict the amino acid change category. Lastly, another python script prioritizes and ranks the mutation effects of variants to output the result file. The Python version of SNPAAMapper accomplished the overall speed by running most annotation steps in a substantially shorter time. The Python script can classify variants by region in 53 s compared to 166 s for the Perl script in a test sample run on a Latitude 7480 Desktop computer with 8GB RAM and an Intel Core i5-6300 CPU @ 2.4Ghz. Steps of predicting amino acid change type and prioritizing mutation effects of variants were executed within 1 s for both pipelines. SNPAAMapper-Python was developed and tested on the ClinVar database, a NCBI database of information on genomic variation and its relationship to human health. We believe our developed Python version of SNPAAMapper variant annotation pipeline will benefit the community by elucidating the variant consequence and speed up the discovery of causative genetic variants through whole genome/exome sequencing. Source codes, test data files, instructions, and further explanations are available on the web at https://github.com/BaiLab/SNPAAMapper-Python.

The data included here are part of a collection of different types of reference data used in the bioinformatic analysis pipeline called Twist Solid GMS560. The pipeline is based on the Hydra-genetics framework and analyses NGS short read data from the GMS560 Twist panel which is used on solid cancer samples. The data in this specific item include panel of normals and artifact filer files generated for the Twist GMS560 panel. Panel of normals and artifacts are specific to panel used and sequencing machine. Programs using these files include MSIsensor-Pro, GATK CNV, CNVkit, SVDB, PureCN, and small variant filtering.

The Chromatin Immunoprecipitation Sequencing (ChIP-seq) market is experiencing robust growth, driven by the increasing adoption of next-generation sequencing technologies in life sciences research and drug discovery. The market, estimated at $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors, including the rising prevalence of chronic diseases necessitating advanced diagnostic tools, the growing demand for personalized medicine, and increasing investments in genomic research globally. Furthermore, technological advancements leading to higher throughput and lower costs for ChIP-seq are accelerating its adoption across various research areas, including oncology, immunology, and neuroscience. The market's growth is further bolstered by the rising availability of skilled professionals and the growing number of collaborations between academic institutions and pharmaceutical companies. Despite the significant growth potential, certain challenges restrain market expansion. These include the high initial investment costs associated with equipment and reagents, the complex experimental procedures requiring specialized expertise, and potential limitations related to data analysis and interpretation. However, ongoing innovation in areas like automation and streamlined data analysis pipelines is mitigating some of these restraints. The market is segmented based on technology, application, and end-user, with key players such as Creative Diagnostics, Profacgen, Merck, Bio-Techne, Abcam, Bio-Rad, Cell Signaling Technology, BioLegend, Active Motif, and Thermo Fisher Scientific competing intensely for market share. Geographical growth is expected to be robust across North America and Europe, followed by Asia-Pacific, driven by a surge in research activities and funding in these regions.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 29, 2019. A cloud platform for next-generation sequencing analysis and storage. Services include: * g-Analysis: Automated genome analysis pipelines at your fingertips * g-Cluster: Easy-of-use and cost-effective genome research infrastructure * g-Storage: A simple way to store, share and protect data * g-Insight: Accurate analysis and interpretation of biological meaning of genome data

Open source environment for sharing, processing and analyzing stem cell data bringing together stem cell data sets with tools for curation, dissemination and analysis. Standardization of the analytical approaches will enable researchers to directly compare and integrate their results with experiments and disease models in the Commons. Key features of the Stem Cell Commons * Contains stem cell related experiments * Includes microarray and Next-Generation Sequencing (NGS) data from human, mouse, rat and zebrafish * Data from multiple cell types and disease models * Carefully curated experimental metadata using controlled vocabularies * Export in the Investigation-Study-Assay tabular format (ISA-Tab) that is used by over 30 organizations worldwide * A community oriented resource with public data sets and freely available code in public code repositories such as GitHub Currently in development * Development of Refinery, a novel analysis platform that links Commons data to the Galaxy analytical engine * ChIP-seq analysis pipeline (additional pipelines in development) * Integration of experimental metadata and data files with Galaxy to guide users to choose workflows, parameters, and data sources Stem Cell Commons is based on open source software and is available for download and development.

Overview of the parameters investigated for the variant calling pipeline with GLM.

The methylation chip market is experiencing robust growth, driven by the increasing prevalence of epigenetic research and its applications in oncology, diagnostics, and personalized medicine. The market's expansion is fueled by advancements in chip technology leading to higher throughput, increased sensitivity, and reduced costs. This allows for more comprehensive methylation profiling, facilitating earlier disease detection and more effective treatment strategies. The growing understanding of the role of methylation in various diseases, coupled with the rising adoption of next-generation sequencing (NGS) technologies, further accelerates market growth. Key segments, such as the 850K and 935K methylation chips, are witnessing significant demand, owing to their comprehensive coverage of CpG sites crucial for epigenetic studies. While the market is currently dominated by established players like Illumina and Roche, the presence of several emerging companies in regions like China indicates a competitive and evolving landscape. Despite the promising growth trajectory, certain challenges remain. The high cost of methylation chip analysis can limit accessibility, particularly in resource-constrained settings. Furthermore, the complexities associated with data interpretation and bioinformatics analysis can pose hurdles for widespread adoption. However, ongoing technological advancements, coupled with decreasing costs and streamlined analysis pipelines, are progressively addressing these limitations. The market's future trajectory is poised for continued expansion, driven by burgeoning research activities, increased investments in genomic research, and the growing demand for precise diagnostic tools within the healthcare sector. This makes methylation chip technology a vital tool for advancing our understanding of disease mechanisms and improving patient outcomes.