Supplementary Material 7.

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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

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)

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.

GitHub repository containing the analysis code....

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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

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

Background

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 aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.

Conclusion

RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

Methods Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt: mean values calculated from raw reads of replicates, downloaded from gene expression omnibus (dataset GSE143430 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143430).

Haering_etal_extendedDatatable_1a_Tabulamurissenis_3vs12m_DEA.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_1b_Tabulamurissenis_3vs27m_DEA.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_1c_Tabulamurissenis_12vs27m_DEA.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_1d_Tabulamurissenis_3vs12m_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_1e_Tabulamurissenis_3vs27m_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_1f_Tabulamurissenis_12vs27m_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_2a_Tabulamurissenis_cluster1_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_2b_Tabulamurissenis_cluster2_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_2c_Tabulamurissenis_cluster3_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_2d_Tabulamurissenis_cluster4_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_2e_Tabulamurissenis_cluster5_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3a_DmLeg_cluster1_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3b_DmLeg_cluster2_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3c_DmLeg_cluster3_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3d_DmLeg_cluster4_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3e_DmLeg_cluster5_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3f_DmLeg_cluster6_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3g_DmLeg_cluster7_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3h_DmLeg_cluster8_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3i_DmLeg_cluster9_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3j_DmLeg_cluster10_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3k_DmLeg_cluster11_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3l_DmLeg_cluster12_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

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.

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.

The associated p-value, false discovery rate (q), and ratio for each pathway, as well as each gene fold change (in parenthesis) are provided.

RNA Sequencing Analysis Market Outlook

The RNA Sequencing Analysis market size is projected to grow significantly from USD 2.5 billion in 2023 to an estimated USD 9.5 billion by 2032, with a robust CAGR of 15.6% over the forecast period. This exponential growth is primarily driven by advancements in sequencing technologies, increasing application of RNA sequencing in drug discovery and development, and the growing demand for personalized medicine.

Several key factors contribute to the growth of the RNA Sequencing Analysis market. Firstly, the continuous advancements in sequencing technologies have dramatically reduced the cost of sequencing, making it more accessible to a broader range of researchers and institutions. This has spurred widespread adoption in both academic and clinical settings. Secondly, RNA sequencing provides unparalleled insights into gene expression, regulation, and mutation, which are critical for understanding complex diseases and developing targeted therapies. Consequently, pharmaceutical and biotechnology companies are increasingly investing in RNA sequencing to accelerate drug discovery and development processes.

Moreover, the growing emphasis on personalized medicine is fueling the demand for RNA sequencing. Personalized medicine aims to tailor medical treatment to individual characteristics, such as genetic profile, and RNA sequencing is crucial for identifying patient-specific biomarkers and therapeutic targets. In addition, the rising prevalence of chronic diseases, cancer, and rare genetic disorders necessitates advanced diagnostic and therapeutic solutions, further driving the adoption of RNA sequencing. The integration of RNA sequencing with other omics technologies, such as proteomics and metabolomics, is also expanding its application scope and enhancing its utility in systems biology and precision medicine.

Another growth factor is the increasing investment in research and development by governments and private organizations. Numerous national and international funding programs support genomic research, fostering innovations in RNA sequencing technologies and applications. Additionally, collaborations between academic institutions, research organizations, and industry players are propelling the development of novel RNA sequencing methodologies and tools, thereby enhancing the market's growth potential. The supportive regulatory environment and growing awareness about the benefits of genomic research further contribute to market expansion.

Short-read Sequencing has become an integral part of RNA sequencing analysis, offering high throughput and cost-effective solutions for various genomic studies. This technology is particularly advantageous in applications where large volumes of data are required, such as transcriptome profiling and small RNA sequencing. The ability to generate millions of short reads quickly and accurately makes it a preferred choice for many researchers. Additionally, the advancements in short-read sequencing platforms have significantly improved the accuracy and speed of sequencing, enabling more detailed and comprehensive analysis of gene expression and regulation. As the demand for personalized medicine and targeted therapies continues to grow, short-read sequencing is expected to play a crucial role in identifying genetic variations and understanding complex biological processes.

From a regional perspective, North America is expected to dominate the RNA Sequencing Analysis market due to the presence of leading market players, advanced healthcare infrastructure, and substantial investment in genomic research. Europe is also anticipated to witness significant growth, driven by increasing funding for research and the adoption of advanced sequencing technologies. The Asia Pacific region is emerging as a lucrative market, owing to the rising healthcare expenditure, growing biotechnology sector, and increasing focus on precision medicine in countries like China and India.

Product Type Analysis

The RNA Sequencing Analysis market can be segmented based on product type into instruments, reagents, software, and services. Instruments play a crucial role in RNA sequencing, providing the necessary hardware for sequencing procedures. The growing demand for high-throughput sequencing and the development of next-generation sequencing (NGS) platforms are driving the market for sequencing instruments. Companies are continuously innovating to produce more efficient and user-friendly sequencing m

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

This workflow adapts the approach and parameter settings of Trans-Omics for precision Medicine (TOPMed). The RNA-seq pipeline originated from the Broad Institute. There are in total five steps in the workflow starting from:

1. Read alignment using STAR which produces aligned BAM files including the Genome BAM and Transcriptome BAM.
2. The Genome BAM file is processed using Picard MarkDuplicates. producing an updated BAM file containing information on duplicate reads (such reads can indicate biased interpretation).
3. SAMtools index is then employed to generate an index for the BAM file, in preparation for the next step.
4. The indexed BAM file is processed further with RNA-SeQC which takes the BAM file, human genome reference sequence and Gene Transfer Format (GTF) file as inputs to generate transcriptome-level expression quantifications and standard quality control metrics.
5. In parallel with transcript quantification, isoform expression levels are quantified by RSEM. This step depends only on the output of the STAR tool, and additional RSEM reference sequences.

For testing and analysis, the workflow author provided example data created by down-sampling the read files of a TOPMed public access data. Chromosome 12 was extracted from the Homo Sapien Assembly 38 reference sequence and provided by the workflow authors. The required GTF and RSEM reference data files are also provided. The workflow is well-documented with a detailed set of instructions of the steps performed to down-sample the data are also provided for transparency. The availability of example input data, use of containerization for underlying software and detailed documentation are important factors in choosing this specific CWL workflow for CWLProv evaluation.

This dataset folder is a CWLProv Research Object that captures the Common Workflow Language execution provenance, see https://w3id.org/cwl/prov/0.5.0 or use https://pypi.org/project/cwl

Steps to reproduce

To build the research object again, use Python 3 on macOS. Built with:

• Processor 2.8GHz Intel Core i7
• Memory: 16GB
• OS: macOS High Sierra, Version 10.13.3
• Storage: 250GB

1. Install cwltool

   pip3 install cwltool==1.0.20180912090223

2. Install git lfs
   The data download with the git repository requires the installation of Git lfs:
   https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs

3. Get the data and make the analysis environment ready:

   git clone https://github.com/FarahZKhan/cwl_workflows.git
   cd cwl_workflows/
   git checkout CWLProvTesting
   ./topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/download_examples.sh

4. Run the following commands to create the CWLProv Research Object:

   cwltool --provenance rnaseqwf_0.6.0_linux --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/Dockstore.json
   
   zip -r rnaseqwf_0.5.0_mac.zip rnaseqwf_0.5.0_mac
   sha256sum rnaseqwf_0.5.0_mac.zip > rnaseqwf_0.5.0_mac_mac.zip.sha256

The https://github.com/FarahZKhan/cwl_workflows repository is a frozen snapshot from https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL commit 027e8af41b906173aafdb791351fb29efc044120

Discover the booming Molecular Biology Software Modules market! Our in-depth analysis reveals a $500M+ market in 2025, growing at a rapid pace due to genomics advancements and personalized medicine. Explore key trends, restraints, and leading companies shaping this dynamic sector. Learn more about regional market shares and future growth projections.

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