Supplementary Material 7.

Discover the booming single-cell analysis software market! Our in-depth report reveals key trends, growth drivers, leading companies (Cellenics, BioTuring Browser, 10x Genomics Loupe Browser, etc.), and future projections through 2033. Learn about market segmentation and regional analysis to gain a competitive edge.

The global gene expression software market is booming, projected to reach $8.17 billion by 2033 with a 15% CAGR. Driven by NGS advancements and personalized medicine, key players like Agilent and Illumina are shaping this rapidly evolving landscape. Discover market trends, growth drivers, and competitive insights in this comprehensive analysis.

Codes and processed data to reproduce the analysis discussed in:

Wegmann et Al., CellSIUS provides sensitive and specific detection of rare cell
populations from complex single cell RNA-seq data, Genome Biology 2019 (Accepted)

This page includes the data and code necessary to reproduce the results of the following paper: Yang Liao, Dinesh Raghu, Bhupinder Pal, Lisa Mielke and Wei Shi. cellCounts: fast and accurate quantification of 10x Chromium single-cell RNA sequencing data. Under review. A Linux computer running an operating system of CentOS 7 (or later) or Ubuntu 20.04 (or later) is recommended for running this analysis. The computer should have >2 TB of disk space and >64 GB of RAM. The following software packages need to be installed before running the analysis. Software executables generated after installation should be included in the $PATH environment variable.

R (v4.0.0 or newer) https://www.r-project.org/ Rsubread (v2.12.2 or newer) http://bioconductor.org/packages/3.16/bioc/html/Rsubread.html CellRanger (v6.0.1) https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome STARsolo (v2.7.10a) https://github.com/alexdobin/STAR sra-tools (v2.10.0 or newer) https://github.com/ncbi/sra-tools Seurat (v3.0.0 or newer) https://satijalab.org/seurat/ edgeR (v3.30.0 or newer) https://bioconductor.org/packages/edgeR/ limma (v3.44.0 or newer) https://bioconductor.org/packages/limma/ mltools (v0.3.5 or newer) https://cran.r-project.org/web/packages/mltools/index.html

Reference packages generated by 10x Genomics are also required for this analysis and they can be downloaded from the following link (2020-A version for individual human and mouse reference packages should be selected): https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest After all these are done, you can simply run the shell script ‘test-all-new.bash’ to perform all the analyses carried out in the paper. This script will automatically download the mixture scRNA-seq data from the SRA database, and it will output a text file called ‘test-all.log’ that contains all the screen outputs and speed/accuracy results of CellRanger, STARsolo and cellCounts.

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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)

.

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 of nov

Explore the growing Gene Expression Software market, projected to reach $132 million with a 7.8% CAGR. Discover key drivers, trends, restraints, and regional insights for genomic data analysis solutions.

GitHub repository containing the analysis code....

Single-Nucleus RNA-Seq Market Outlook

According to our latest research, the global Single-Nucleus RNA-Seq market size reached USD 368 million in 2024, reflecting a robust expansion driven by technological advancements and increasing research applications. The market is expected to grow at a CAGR of 17.2% from 2025 to 2033, with the market size projected to reach USD 1.57 billion by 2033. This impressive growth is fueled by the rising adoption of single-nucleus RNA sequencing (snRNA-seq) in various fields such as neuroscience, oncology, and developmental biology, as well as the increasing availability of high-throughput sequencing platforms and advanced bioinformatics tools.

The growth of the Single-Nucleus RNA-Seq market is primarily propelled by the increasing need to understand cellular heterogeneity at a granular level, particularly in complex tissues such as the brain and tumors. The limitations of traditional bulk RNA sequencing, which averages gene expression across heterogeneous cell populations, have underscored the value of single-nucleus approaches. Researchers and clinicians are leveraging snRNA-seq to unravel disease mechanisms, identify novel therapeutic targets, and develop precision medicine strategies. The adoption of this technology is further accelerated by the emergence of automated instruments, improved sample preparation protocols, and the expanding availability of high-quality consumables, which collectively enhance throughput, reproducibility, and scalability of single-nucleus transcriptomic studies.

Another significant growth driver for the Single-Nucleus RNA-Seq market is the increasing investment in genomics and transcriptomics research by both public and private sectors. Major funding agencies and governments across North America, Europe, and Asia Pacific are allocating substantial resources to support large-scale single-cell and single-nucleus sequencing projects. Pharmaceutical and biotechnology companies are integrating snRNA-seq into their drug discovery and development pipelines to better understand disease pathogenesis and patient stratification. The proliferation of collaborative initiatives between academic institutions, industry players, and clinical research organizations is also fostering innovation and expanding the application landscape of single-nucleus RNA sequencing across various domains, including immunology and developmental biology.

The market is also benefiting from the rapid evolution of bioinformatics and data analysis tools tailored specifically for single-nucleus RNA-Seq data. The complexity and volume of data generated by snRNA-seq experiments necessitate sophisticated computational pipelines for quality control, normalization, clustering, and downstream analysis. The development of user-friendly software platforms and cloud-based solutions has democratized access to advanced analytics, enabling researchers with varying levels of computational expertise to derive meaningful insights from their data. This trend is expected to continue as more commercial and open-source solutions emerge, further driving the adoption of single-nucleus RNA sequencing technologies in both research and clinical settings.

From a regional perspective, North America currently dominates the Single-Nucleus RNA-Seq market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. This leadership is attributed to the presence of leading genomics research centers, robust funding infrastructure, and early adoption of cutting-edge sequencing technologies. However, Asia Pacific is anticipated to witness the fastest growth during the forecast period, supported by increasing investments in life sciences research, expanding biotechnology industry, and growing awareness of precision medicine. Europe is also expected to maintain a significant market share due to strong academic research output and collaborative initiatives in genomics and transcriptomics.

Product Type Analysis

The Single-Nucleus RNA-Seq market by product type is segmented into

BackgroundÂ

RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.

Results

With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to...

Reference is regularly made to the power of new genomic sequencing approaches. Using powerful technology, however, is not the same as having the necessary power to address a research question with statistical robustness. In the rush to adopt new and improved genomic research methods, limitations of technology and experimental design may be initially neglected. Here, we review these issues with regard to RNA sequencing (RNA-seq). RNA-seq adds large-scale transcriptomics to the toolkit of ecological and evolutionary biologists, enabling differential gene expression (DE) studies in non-model species without the need for prior genomic resources. High biological variance is typical of field-based gene expression studies and means that larger sample sizes are often needed to achieve the same degree of statistical power as clinical studies based on data from cell lines or inbred animal models. Sequencing costs have plummeted, yet RNA-seq studies still underutilise biological replication. Finite research budgets force a trade-off between sequencing effort and replication in RNA-seq experimental design. However, clear guidelines for negotiating this trade-off, while taking into account study-specific factors affecting power, are currently lacking. Study designs that prioritise sequencing depth over replication fail to capitalise on the power of RNA-seq technology for DE inference. Significant recent research effort has gone into developing statistical frameworks and software tools for power analysis and sample size calculation in the context of RNA-seq DE analysis. We synthesise progress in this area and derive an accessible rule-of-thumb guide for designing powerful RNA-seq experiments relevant in eco-evolutionary and clinical settings alike.

Dataset Description

This dataset contains RNA-seq data from human cells. The data was collected using the Illumina HiSeq 2500 platform. The data includes raw sequencing reads, gene annotations, and phenotypic data for the samples.

Files and Folders

Files can be downloaded using the following command:

wget ftp://ftp.ccb.jhu.edu/pub/RNAseq_protocol/chrX_data.tar.gz

Once the file has been downloaded, it can be extracted using the following command:

tar xvzf chrX_data.tar.gz

This will create a directory called chrX_data containing the following files:

genes/chrX.gtf
genome/chrX.fa
geuvadis_phenodata.csv
indexes/
mergelist.txt
samples/

Here are some additional details about the files in the chrX_data directory:

• genes/chrX.gtf - This file contains gene annotations for the human X chromosome. It is in the GTF format, which is a standard format for gene annotations. The GTF file contains information about the start and end positions of genes, as well as their transcripts.
• genome/chrX.fa - This file contains the reference genome sequence for the human X chromosome. It is in the FASTA format, which is a standard format for storing DNA sequences.
• geuvadis_phenodata.csv - This file contains phenotypic data for the samples in the dataset. The phenotypic data includes information such as the age, sex, and disease status of the samples.
• indexes/ - This directory contains index files for HISAT2. Index files are used to speed up the alignment of sequencing reads to a reference genome.
• mergelist.txt - This file lists the samples to be merged. The samples in the samples/ directory can be merged using a variety of tools, such as BEDTools and STAR.
• samples/ - This directory contains the raw sequencing data. The raw sequencing data is in the FASTQ format, which is a standard format for storing sequencing reads.

Usage

This dataset can be used to perform RNA-seq analysis using a variety of tools, such as HISAT2, StringTie, and Ballgown.

Here are some examples of how this dataset can be used:

• To identify differentially expressed genes between two groups of samples.
• To build a gene expression atlas for a particular tissue or cell type.
• To study the expression of genes involved in a particular disease.

source: ftp://ftp.ccb.jhu.edu/pub/RNAseq_protocol/chrX_data.tar.gz

Datasets produced during the validation of CWL-based pipelines, designed for the analysis of data from RNA-Seq, ChIP-Seq and germline variant calling experiments. Specifically, the workflows were tested using publicly available High-throughput (HTS) data from published studies on Chronic Lymphocytic Leukemia (CLL) (accession numbers: E-MTAB-6962, GSE115772) and Genome in a Bottle (GIAB) project samples (accession numbers: SRR6794144, SRR22476789, SRR22476790, SRR22476791).

The supporting data include:

• Differential transcript and gene expression results produced during the analysis with the CWL-based RNA-Seq pipeline
• Bigwig and narrowPeak files, differential binding results, table of consensus peaks and read counts of EZH2 and H3K27me3, produced during the analysis with the CWL-based ChIP-Seq pipeline
• VCF files containing the detected and filtered variants, along with the respective hap.py () results regarding comparisons against the GIAB golden standard truth sets for both CWL-based germline variant calling pipelines

This dataset contains RNA-Seq data preprocessing and differential gene expression (DGE) analysis.

It is designed for researchers, bioinformaticians, and students interested in transcriptomics.

The dataset includes raw count data and step-by-step preprocessing instructions.

It demonstrates quality control, normalization, and filtering of RNA-Seq data.

Differential expression analysis using popular tools and methods is included.

Results include differentially expressed genes with statistical significance.

It provides visualizations like PCA plots, heatmaps, and volcano plots.

The dataset is suitable for learning and reproducing RNA-Seq workflows.

Both human-readable explanations and code snippets are included for guidance.

It can serve as a reference for new RNA-Seq projects and research pipelines.

RNA Sequencing Market Outlook

According to our latest research and industry analysis, the global RNA Sequencing (RNA-Seq) market size in 2024 stands at USD 3.2 billion, driven by the surging demand for advanced genomics solutions in biomedical research and clinical diagnostics. The market is experiencing a robust growth trajectory with a CAGR of 17.6% from 2025 to 2033, projecting the market size to reach USD 11.1 billion by 2033. This rapid expansion is primarily fueled by the escalating adoption of next-generation sequencing (NGS) technologies, increased focus on precision medicine, and the growing prevalence of complex diseases that require comprehensive transcriptomic profiling.

One of the primary growth factors propelling the RNA Sequencing market is the increasing application of RNA-Seq in the discovery and development of novel therapeutics, particularly in oncology, neurology, and rare genetic disorders. The ability of RNA-Seq to deliver high-throughput, unbiased, and quantitative analysis of transcriptomes has revolutionized the way researchers understand gene expression, alternative splicing, and transcript variants. This has facilitated more accurate biomarker identification, drug target validation, and patient stratification, leading to enhanced personalized medicine approaches. Moreover, the integration of artificial intelligence and machine learning with RNA-Seq data analytics is further accelerating the extraction of actionable insights, thereby amplifying the utility and value proposition of RNA sequencing in both research and clinical settings.

Another significant growth driver is the continuous technological advancements in sequencing platforms and library preparation protocols, which have substantially improved the accuracy, speed, and cost-effectiveness of RNA-Seq workflows. Innovations such as single-cell RNA sequencing, long-read sequencing, and nanopore-based technologies are enabling researchers to unravel cellular heterogeneity and complex transcriptomic landscapes with unprecedented resolution. Additionally, the decreasing cost of sequencing and the proliferation of user-friendly bioinformatics tools have democratized access to RNA-Seq, empowering academic institutions, hospitals, and even smaller biotech firms to leverage these powerful tools for a wide array of applications, from basic research to translational and clinical studies.

A third pivotal factor contributing to the market's expansion is the rising investment from both public and private sectors in genomics research and precision healthcare infrastructure. Governments across North America, Europe, and Asia Pacific are launching large-scale genomics initiatives, funding biobanks, and fostering collaborations between academia, industry, and healthcare providers. These efforts are not only expanding the installed base of sequencing instruments but are also driving demand for consumables and sequencing services. Furthermore, the COVID-19 pandemic underscored the critical role of RNA sequencing in pathogen surveillance and vaccine development, which has further entrenched RNA-Seq as an indispensable tool in modern life sciences.

From a regional standpoint, North America currently dominates the RNA Sequencing market, accounting for the largest share in 2024, owing to its advanced healthcare infrastructure, high R&D expenditure, and presence of leading genomics companies. Europe follows closely, driven by strong government support and a vibrant biotech ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by increasing investments in genomics, rising healthcare awareness, and expanding research capabilities in countries like China, Japan, and India. Latin America and the Middle East & Africa are gradually catching up, supported by growing collaborations and capacity-building initiatives. The global landscape is thus characterized by dynamic regional trends, evolving regulatory frameworks, and a rapidly expanding user base for RNA sequencing technologies.

Product & Service Analysis

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RNA Velocity Tools Market Outlook

According to our latest research, the global RNA Velocity Tools market size reached USD 215.7 million in 2024, demonstrating robust momentum in single-cell transcriptomics and computational biology. The market is anticipated to expand at a CAGR of 19.4% from 2025 to 2033, with projections estimating the market to reach USD 1,154.8 million by 2033. This remarkable growth is driven by the increasing adoption of advanced bioinformatics tools in life sciences research, the expanding utility of single-cell RNA sequencing, and the rising demand for innovative solutions that enable dynamic transcriptomic analysis at unprecedented resolution.

A primary growth factor for the RNA Velocity Tools market is the rapid advancement in single-cell omics technologies. The integration of RNA velocity analytics with single-cell RNA sequencing (scRNA-seq) has revolutionized cell fate mapping, developmental trajectory prediction, and lineage tracing. Researchers and clinicians now have access to tools that provide temporal information about gene expression changes, which is invaluable for understanding complex biological processes such as differentiation, disease progression, and tissue regeneration. As more laboratories and research institutes invest in high-throughput sequencing platforms, the demand for sophisticated computational tools that can interpret this data continues to surge, further propelling the RNA Velocity Tools market.

Another significant contributor to market growth is the increasing prevalence of chronic and genetic diseases, which necessitates a deeper understanding of cellular mechanisms at the single-cell level. RNA velocity tools are increasingly employed in disease research, particularly in oncology, neurology, and immunology, where understanding the dynamic behavior of cells can uncover novel therapeutic targets and inform personalized medicine strategies. The ability to predict cellular responses to external stimuli or treatment interventions in real time is transforming drug discovery and development pipelines, making RNA velocity analysis an essential component of translational research in both academic and commercial settings.

Furthermore, the expanding ecosystem of bioinformatics and computational biology services is facilitating broader adoption of RNA velocity tools. The availability of user-friendly software, cloud-based analytics platforms, and professional services for data processing and interpretation has lowered the barriers to entry for researchers with limited computational expertise. This democratization of technology is enabling a wider range of end-users, including hospitals, diagnostic centers, and pharmaceutical companies, to leverage RNA velocity analyses in their workflows. The increased collaboration between software developers, sequencing technology providers, and end-users is creating a virtuous cycle of innovation, further accelerating market growth.

Regionally, North America maintains its dominance in the RNA Velocity Tools market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. The United States, in particular, benefits from a well-established biotechnology sector, significant government funding for genomics research, and a high concentration of academic and research institutions. Europe’s strong regulatory framework and collaborative research initiatives are fostering innovation, while Asia Pacific is experiencing the fastest growth due to increasing investments in life sciences infrastructure and a burgeoning biotechnology industry. Latin America and the Middle East & Africa are emerging as promising markets, albeit at a slower pace, driven by gradual improvements in healthcare infrastructure and research capabilities.

Product Type Analysis

The RNA Velocity Tools market by product type is segmented into software and services, each playing a pivotal role in the market’s overall expansion. Software solutions comprise standalone applications, integrated platforms, and plug-ins designed to analyze RNA velocity data generated from single-cell RNA sequencing experiments. These tools offer advanced algorithms for modeling gene expression dynamics, visualizing cellular trajectories, and integrating multi-omics datasets. The continuous evolution of software platforms, including the incorporation of machine learning and artificial intelligence, is enhancing the accuracy and scalability of RNA velocity analysis

Pseudotime Analysis Software Market Outlook

According to our latest research, the Pseudotime Analysis Software market size was valued at $256 million in 2024 and is projected to reach $812 million by 2033, expanding at a CAGR of 13.7% during 2024–2033. One major factor driving the global growth of the pseudotime analysis software market is the rapid adoption of single-cell RNA sequencing (scRNA-seq) technologies in genomics research, which has significantly increased the demand for advanced computational tools capable of reconstructing cellular trajectories and developmental timelines. As biological research becomes increasingly data-intensive and complex, the need for robust, scalable, and user-friendly software platforms to interpret high-dimensional single-cell datasets is fueling the expansion of this market. The integration of machine learning algorithms and cloud-based deployment options further enhances accessibility and analytical power, making pseudotime analysis software indispensable for cutting-edge research in developmental biology, cancer, and regenerative medicine.

Regional Outlook

North America currently holds the largest share of the Pseudotime Analysis Software market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the region’s mature biotechnology and pharmaceutical sectors, robust funding for life sciences research, and widespread adoption of advanced genomics technologies. The presence of leading academic and research institutions, along with a strong ecosystem of software developers and bioinformatics companies, has facilitated early adoption and continuous innovation in pseudotime analysis tools. Additionally, favorable government policies and grant programs supporting precision medicine and single-cell analytics have further solidified North America’s leadership position. The region also benefits from a high concentration of skilled bioinformaticians and a collaborative environment that fosters the integration of computational and experimental biology, driving sustained market growth.

The Asia Pacific region is emerging as the fastest-growing market for pseudotime analysis software, projected to expand at a CAGR of 16.2% through 2033. Key investment drivers include increasing government support for genomics research, expanding biopharmaceutical manufacturing capabilities, and a surge in collaborations between academic institutions and industry. Countries such as China, Japan, South Korea, and India are investing heavily in life sciences infrastructure, with a focus on precision medicine, stem cell research, and oncology. The rapid adoption of next-generation sequencing technologies and the establishment of large-scale single-cell research centers are accelerating demand for sophisticated analytical tools. Furthermore, the growing pool of skilled computational biologists and the proliferation of cloud-based solutions are lowering barriers to entry, enabling broader access to advanced pseudotime analysis platforms across the region.

Emerging economies in Latin America, the Middle East, and Africa are witnessing a gradual increase in the adoption of pseudotime analysis software, though market penetration remains limited due to challenges such as lower research funding, skill shortages, and infrastructure constraints. However, localized demand is rising as universities and hospitals in these regions participate in international genomics consortia and seek to address region-specific health challenges, such as endemic diseases and cancer. Policy reforms aimed at strengthening research capabilities, coupled with international collaborations and technology transfer initiatives, are expected to gradually improve market accessibility. Nevertheless, the pace of adoption is hindered by budgetary limitations, limited access to high-performance computing resources, and the need for tailored training programs to build local expertise in bioinformatics and data science.

Report Scope

Background: mRNA interactions with each other and other signaling molecules define different biological pathways and functions. Researchers have been investigating various tools to analyze these types of interactions. In particular gene co-expression network methods have proved useful in finding and analyzing these molecular interactions. Many different analytical pipelines to identify these interactions networks have been proposed with the aim of identifying an optimal partition of the network where the individual modules are neither too small to make any general inference or too large to be biologically interpretable. Results: In this study we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline uses WGCNA a widely used software to perform different aspects of gene co-expression network analysis and modularity maximization algorithm to analyze novel RNA-Seq data to understand the effects of low-dose 56Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results along with experimental validation show that using WGCNA combined with Modularity provide a more biologically interpretable network in our dataset. Our pipeline showed better performance than the existing clustering algorithm in WGCNA in finding modules and identified a module with mitochondrial subunits that are supported by mitochondrial complex assay. Conclusions: We present a pipeline that can reduce the problem of parameter selection with the existing algorithm in WGCNA for comparable RNA-Seq datasets which may assist in future research to discover novel mRNA interactions and their downstream molecular effects. C57BL16 males were placed into 2 treatment groups and received the following irradiation treatments at Brookhaven National Laboratories (Long Island NY): 600 MeV/n 56Fe (0.2 Gy) and no irradiation. Left liver lobes were collected at 30 60 120 270 and 360 days post-irradiation flash frozen and stored at -80 xc2 xb0C until they could be processed for RNA-Seq. Livers were sampled by taking two 40-micron thick slices using a cryotome at -20 xc2 xb0C. This allowed multiple sampling of the tissue without the tissue going through multiple freeze/thaw cycles. Total RNA was isolated from the liver slices using RNAqueousTM Total RNA Isolation Kit (ThermoFisher Scientific Waltham MA) and rRNA was removed via Ribo-ZeroTM rRNA Removal Kit (Illumina San Diego CA) prior to library preparation with the Illumina TruSeq RNA Library kit. Samples were sequenced in a paired-end 50 base format on an Illumina HiSeq 1500. Reads were aligned to the mouse GRCm38 reference genome using the STAR alignment program version 2.5.3a with the recommended ENCODE options. The -quantMode GeneCounts option was used to obtain read counts per gene based on the Gencode release M14 annotation file. Total number of reads used in analysis varies between 23-35 millions of reads.

Aims and Methods:To identify the potential genes or pathways involved in ROS generation following 1-d SD, we collected tissues containing the head and gut from WT Drosophila for RNA sequencing (RNA-seq). And to gain more insights into the processes involved in RET-ROS and humoral response of hemocytes during SD, we conducted RNA-seq of hemocytes harvested from non-SD and 1-day SD groups. Hemocyte samples were collected from approximately 60 adult female flies (Figure 5E). For hemocyte isolation, 20 flies were loaded onto a 30 μm filter in a Mobicol spin column (MOBITEC), covered with glass beads, and centrifuged at 10,000 rpm for 20 min at 4°C. This procedure was repeated twice using a total of 40 flies, with recovered hemolymph pooled in 300 μl TRIzol reagent. Head-gut samples consisted of 50 head-gut complexes in 500 μl TRIzol (Figure 2A). Total RNA was extracted using the UNlQ-10 Column Trizol Total RNA Isolation Kit (Sangon Biotech) followed by DNaseI (BBI) treatment. RNA purification employed Oligo dT magnetic beads, with final samples stored at -80°C until analysis. For RNA-seq, cDNA libraries were prepared with the Illumina TruSeq stranded mRNA library prep kit and sequenced on an Illumina platform using paired-end (PE) sequencing. Raw reads were processed to obtain clean reads by removing contaminants, duplicates, and low-quality sequences. Transcriptome analysis included: (1) quality control, (2) alignment analysis, and (3) in-depth annotation including Multi-platform Gene Ontology (GO). Differential expression was determined using volcano plot filtering |log2-fold change| ≥ 0.58 (≥1.5-fold changes) and false discovery rate (FDR) < 0.05.Files: The file names started with Non-SD or SD are the data file from hemocytes (12 files in total, with 6 Non-SD and 6 SD samples), while the others are from head+gut tissues (12 files in total, with 6 Non-SD and 6 SD samples). Software: Initially, in the quality control (QC) phase, Cutadapt (version V1.9.1) and FastQC (version V0.10.1) are employed. Moving on to the mapping stage, HISAT2 (version V2.2.1) is used for sequence alignment. Gene expression analysis is conducted with HTSEQ (version V0.6.1). Transcript assembly is performed using Stringtie (version V1.3.3b). For differential expression analysis, three different software tools are utilized: DESeq2 (version V1.26.0), DESeq (version V1.38.0), and EdgeR (version V3.28.1). Additionally, Cuffdiff (version V2.2.1) is also employed for this analysis. Finally, in the GO enrichment analysis, GOSeq (version V1.34.1) and TopGO (version V2.18.0) are the software tools used.

RNA Sequencing Technologies Market Outlook

The global RNA sequencing technologies market size was valued at $2.3 billion in 2023 and is poised to grow to $9.7 billion by 2032, exhibiting a robust CAGR of 16.9% during the forecast period. This impressive growth can be attributed to the increasing demand for personalized medicine and advancements in biotechnology, which have propelled the adoption of RNA sequencing technologies across various sectors.

The primary growth factor driving the RNA sequencing technologies market is the increasing focus on personalized medicine. As healthcare moves towards more targeted and individualized treatment plans, RNA sequencing enables a deeper understanding of the genetic and molecular underpinnings of diseases. This, in turn, facilitates the development of more effective treatments and therapies tailored to individual patients. Additionally, technological advancements in sequencing methods and bioinformatics tools have significantly lowered the costs and increased the accuracy and efficiency of RNA sequencing, further boosting its adoption.

Another significant growth factor is the rising prevalence of chronic diseases and conditions such as cancer, cardiovascular diseases, and neurological disorders. These complex diseases require detailed molecular and genetic profiling for effective diagnosis and treatment. RNA sequencing provides a comprehensive view of the transcriptome, making it an invaluable tool in the detection and understanding of disease mechanisms. This has led to increased investments in RNA sequencing applications by pharmaceutical and biotechnology companies, as well as academic and research institutions.

Furthermore, the expanding scope of RNA sequencing in drug discovery and development is a crucial driver of market growth. By offering insights into gene expression and regulation, RNA sequencing helps identify potential drug targets and biomarkers, accelerating the drug development process. This has led to a surge in collaborative research efforts and partnerships between sequencing technology providers and pharmaceutical companies. As the demand for novel therapeutics continues to rise, the role of RNA sequencing in the drug discovery pipeline is expected to become even more significant.

mRNA Sequencing has emerged as a pivotal component within the broader RNA sequencing technologies landscape. This method focuses on capturing the messenger RNA molecules present in a sample, providing insights into the actively expressed genes at any given moment. The precision of mRNA Sequencing allows researchers to explore the dynamic nature of gene expression, making it invaluable for understanding cellular responses to environmental changes, disease states, and developmental processes. As the demand for personalized medicine grows, mRNA Sequencing offers the potential to tailor treatments based on an individual's unique gene expression profile, thus enhancing therapeutic efficacy and minimizing adverse effects.

Regionally, North America holds a dominant position in the RNA sequencing technologies market, attributed to the presence of major biotechnology firms and advanced research infrastructures. Additionally, favorable regulatory environments and substantial government funding for genomics research further support market growth in this region. However, the Asia Pacific region is anticipated to exhibit the highest CAGR during the forecast period, driven by increasing healthcare investments, growing awareness of personalized medicine, and a burgeoning biotech sector.

Technology Analysis

Single-cell RNA Sequencing Analysis

Single-cell RNA sequencing (scRNA-seq) is a powerful technology that enables the analysis of gene expression at the individual cell level, providing a high-resolution view of cellular heterogeneity. This technology has revolutionized our understanding of complex biological systems, including cancer, immune responses, and developmental biology. The ability to profile thousands of cells simultaneously has led to significant advancements in identifying rare cell populations and understanding cellular functions within tissues. As a result, scRNA-seq is increasingly being adopted by academic and research institutions for basic and translational research.

The market for scRNA-seq is driven by the continuous innovations in sequencing platforms and data analysis tools, which have made the technology more