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.

Supplementary Material 7.

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)

The Nucleic Acid Sequence Analysis Software market is booming, projected to reach $8 billion by 2033 with a 15% CAGR. Discover key trends, drivers, and regional insights in this comprehensive market analysis, including leading companies like Illumina and PacBio. Explore the impact of cloud-based solutions and the growing demand for personalized medicine.

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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

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.

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

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.

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

miRanda files for package mirTarRnaSeq

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.

The data was updated on March 13, 2022.

We have uploaded the complete data analysis as a txt.file. In addition, we uploaded information on age, sex, hospital room, and mapping rate as supplemental data.

Changes from last time! Use salmon's --validateMapping option for quantification. In addition, the --quantmerge option was used in place of tximport.

126 RNA-Seq datasets of COVID19 ( 100 COVID19, 26 Control cases ) Let’s find important transcripts in COVID-19 by using machine learning👍

RNA-Seq is a particular technology-based sequencing technique which uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample, analyzing the continuously changing cellular transcriptome.

https://en.wikipedia.org/wiki/RNA-Seq

We download SRA and FASTQ files and prepared 126 RNA-Seq datasets. The known transcriptome (GRCh38) was used as a reference for quantitative analysis. The abundances of individual transcripts were quantified by Salmon. Finally, We applied tximport to create an output file for analysis.

Content Source : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157103

Let’s find important transcripts in COVID-19 by using machine learning👍

Single-Cell RNA Sequencing Market Outlook

According to our latest research, the global Single-Cell RNA Sequencing (scRNA-seq) market size reached USD 4.2 billion in 2024, driven by rapid technological advancements and increasing adoption in biomedical research. The market is experiencing robust expansion, registering a strong CAGR of 18.9% from 2025 to 2033. By the end of 2033, the Single-Cell RNA Sequencing market is projected to attain a value of USD 13.6 billion, reflecting a significant rise in demand for precise cellular analysis. As per our latest research, the market’s growth is primarily fueled by the increasing focus on personalized medicine and the need for high-resolution transcriptomic profiling in diverse research and clinical applications.

One of the primary growth factors for the Single-Cell RNA Sequencing market is the escalating demand for personalized and precision medicine. Researchers and clinicians are increasingly relying on scRNA-seq technologies to unravel the complexities of cellular heterogeneity, enabling them to identify rare cell populations, understand disease mechanisms at the single-cell level, and develop targeted therapies. This trend is particularly pronounced in oncology, where single-cell transcriptomics is revolutionizing cancer research by providing insights into tumor microenvironments, drug resistance, and immune cell interactions. The integration of scRNA-seq with advanced bioinformatics tools is further enhancing its utility, allowing for deeper data interpretation and actionable insights that are critical for translational medicine and biomarker discovery.

Another significant driver for the Single-Cell RNA Sequencing market is the continuous technological innovation in sequencing platforms and workflow automation. The emergence of high-throughput, cost-effective sequencing instruments and reagents has made scRNA-seq more accessible to a broader range of laboratories, including academic, clinical, and pharmaceutical settings. Automation of sample preparation and improvements in sequencing chemistry have reduced technical variability and increased reproducibility, making large-scale single-cell studies feasible. Furthermore, the development of sophisticated data analysis software has addressed the computational challenges associated with massive single-cell datasets, enabling researchers to extract meaningful biological information efficiently. These technological advancements are collectively accelerating the adoption of scRNA-seq across multiple disciplines.

The growing investment in life sciences research, coupled with supportive government initiatives and funding, is also propelling the Single-Cell RNA Sequencing market. Governments and private organizations worldwide are recognizing the importance of single-cell technologies in advancing biomedical research and healthcare innovation. This is reflected in increased funding for genomics and cell biology projects, establishment of collaborative research consortia, and the launch of national precision medicine programs. Additionally, the expansion of biopharmaceutical pipelines and the need for novel drug development approaches are driving pharmaceutical and biotechnology companies to incorporate scRNA-seq into their R&D workflows. These factors are creating a favorable environment for market growth and fostering the development of next-generation single-cell analysis solutions.

Regionally, North America remains the dominant market for Single-Cell RNA Sequencing, attributed to the presence of leading research institutions, advanced healthcare infrastructure, and significant investments in genomics research. Europe follows closely, benefiting from robust funding frameworks and a strong academic network. The Asia Pacific region is emerging as a high-growth market, driven by increasing research activities, expanding healthcare expenditure, and rising adoption of cutting-edge genomic technologies. Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness and investments in life sciences. Each region exhibits unique growth dynamics, shaped by local regulatory landscapes, research priorities, and healthcare needs.

Product Type Analysis

The Product Type segment in the Single-Cell RNA Sequencing market is categorized into Instruments, Consumables, and Software, each playing a pivotal role in the overall workflow and market dynamics. Instruments,

RNA from roots and leaves of twenty eight bread wheat genotypes was extracted, purified, quantified and sent to Beijing Genomics Institute (BGI) for sequencing. Briefly, mRNAs were isolated and fragmented from total RNA using the oligo (dT) method for cDNA synthesis. The 75-bp single-end sequencing libraries were constructed, and sequencing was performed on HiSeq X Ten (Illumina, San Diego, USA) using standard protocols. Adapters with unknown bases (N’s > 5%) and low quality were removed from raw reads to produce ‘clean data’ as FastQ data files using a quality control software, SOAPnuke version 2.1.6. High quality single-end reads were mapped to the bread wheat reference genome (IWGSC, INSDC Assembly GCA_900519105.1) using HISAT2 (Hierarchical Indexing for Spliced Alignment of Transcripts) software version 2.2.1. Alignment of the reference sequence with reads were performed using Bowtie. Quantification of the reads was performed using featureCounts software program. Differentially e...

12 files (6 sets of paired Illumina FASTQ files) transcriptomes (expressed genes) of the fourth (final) larval and early (white) pupal stages of Solenopsis invicta from Mississippi Delta. Resources in this dataset:Resource Title: RNA-seq Larva sample Colony 1. File Name: A01L4_1.zipResource Description: This is the first of the paired samples; the total set includes 3 paired Illumina files from larvae, and 3 paired Illumina files from pupae. Colony 1 samples are from 4th instar larvae: paired Illumina fastq files A01L4_1 and A01L4_2; and white pupae: X01wp_1 and X01wp_2.Resource Software Recommended: A01L4_1,url: www.cyverse.org Resource Title: RNA-seq Larva sample Colony 1. File Name: A01L4_2.zipResource Description: This is the second of the paired samples for larvae from Colony 1. Colony 1 samples are from 4th instar larvae: paired Illumina fastq files A01L4_1 and A01L4_2; and white pupae: X01wp_1 and X01wp_2.Resource Software Recommended: Any assembly program that handles paired Illumina files,url: www.cyverse.org Resource Title: RNA-seq pupa sample Colony 1. File Name: X01wp_1.zipResource Description: This is the first of the paired samples. Colony 1 samples are from 4th instar larvae: paired Illumina fastq files A01L4_1 and A01L4_2; and white pupae: X01wp_1 and X01wp_2.Resource Software Recommended: Any assembly program that handles paired Illumina files,url: www.cyverse.org Resource Title: RNA-seq pupa sample Colony 1.. File Name: X01wp_2.zipResource Description: This is the second of the paired samples. Colony 1 samples are from 4th instar larvae: paired Illumina fastq files A01L4_1 and A01L4_2; and white pupae: X01wp_1 and X01wp_2.Resource Software Recommended: Any assembly program that can use Illumina paired files,url: www.cyverse.org Resource Title: RNA-seq larva sample Colony 2. File Name: B02L4_2.zipResource Description: This is the second of the paired samples from Colony 2.Resource Software Recommended: Any assembly program that handles Illumina paired files for input,url: www.cyverse.org Resource Title: RNA-seq larva sample Colony 2. File Name: B02L4_1.zipResource Description: This is the first of the paired larva samples. Colony 2 samples include larva paired Illumina files B02L4_1 and B02L4_2, and paired pupa files Y02wp_1 and Y02wp_2.Resource Software Recommended: Any assembly program that handles paired Illumina files,url: www.cyverse.org Resource Title: RNA-seq pupa sample Colony 2. File Name: Y02wp_1.zipResource Description: This is the first of the paired pupa files. The set includes Y02wp_1 and Y02wp_2.Resource Software Recommended: Any assembly program that accepts Illumina paired reads,url: www.cyverse.org Resource Title: RNA-seq pupa sample Colony 2. File Name: Y02wp_2.zipResource Description: This is the second of the paired samples; the Colony 2 pupa samples are Y02wp_1 and Y02wp_2.Resource Software Recommended: Any assembly program that accepts Illumina paired data files,url: www.cyverse.org Resource Title: RNA-seq pupa sample Colony 3. File Name: Z03wp_1.zipResource Description: This is the first of paired sample files: the set includes Z03wp_1 and Z03wp_2.Resource Title: RNA-seq pupa sample Colony 3. File Name: Z03wp_2.zipResource Description: This is the second of the paired sample files: the set includes Z03wp_1 and Z03wp_2Resource Title: RNA-seq larva sample Colony 3. File Name: C03L4_1.zipResource Description: This is the first of the paired sample files: the set includes C03L4_1 and C03L4_2.Resource Title: RNA-seq larva sample Colony 3. File Name: C03L4_2.zipResource Description: This is the second of the paired sample files: the set includes C03L4_1 and C03L4_2

This repository contains the real and simulation datasets used in the paper "Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data". Please check the full text published on Cell Systems or our preprint.

1. real_datasets.zip: 16 real scRNA-seq datasets with experimentally annotated doublets. The name of each file corresponds to the names in the benchmark paper.

2. simulation_datasets.zip: simulation datasets used in the benchmark, including different experimental conditions, scalability, stability, running time, and the effects of doublet detection on DE gene analysis, highly variable gene identification, cell clustering, and trajectory inference.

3. result.xlsx: a tabular file that saves benchmarking results, including AUPRC, AUROC, precision, recall, TNR, and cell clustering. It is also the data source for drawing figures in the paper "Protocol for Benchmarking Computational Doublet-Detection Methods in Single-Cell RNA Sequencing Data Analysis".

Additional file 3: Supplementary Table S3. Detailed comparison of multiple single-cell RNA-seq data visualization software.

We propose a novel method and software tool, Strawberry, for transcript reconstruction and quantification from RNA-Seq data under the guidance of genome alignment and independent of gene annotation. Strawberry consists of two modules: assembly and quantification. The novelty of Strawberry is that the two modules use different optimization frameworks but utilize the same data graph structure, which allows a highly efficient, expandable and accurate algorithm for dealing large data. The assembly module parses aligned reads into splicing graphs, and uses network flow algorithms to select the most likely transcripts. The quantification module uses a latent class model to assign read counts from the nodes of splicing graphs to transcripts. Strawberry simultaneously estimates the transcript abundances and corrects for sequencing bias through an EM algorithm. Based on simulations, Strawberry outperforms Cufflinks and StringTie in terms of both assembly and quantification accuracies. Under the evaluation of a real data set, the estimated transcript expression by Strawberry has the highest correlation with Nanostring probe counts, an independent experiment measure for transcript expression. Availability: Strawberry is written in C++14, and is available as open source software at https://github.com/ruolin/strawberry under the MIT license.

Explore the dynamic Molecular Biology Software Modules market, driven by genomics, personalized medicine, and AI integration. Discover market size, CAGR, regional growth, and key trends shaping the future of life sciences.