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NGS-based RNA-seq Market Size 2024-2028

The NGS-based RNA-seq market is estimated to grow by USD 6.66 billion at a CAGR of 20.52% between 2023 and 2028. The market is poised for significant growth, driven by key factors reshaping the landscape of genetic analysis. With the adoption of next-generation sequencing (NGS) techniques, bolstered by their unparalleled precision and throughput, the market is witnessing a transformative shift. The market is expanding rapidly with advances in genomics and the growing adoption of RNA sequencing projects across research applications and clinical diagnostics. Technological advancements in sequencing platforms are enabling researchers to explore RNA dynamics with unprecedented depth and accuracy. Moreover, the market is thriving due to the diverse range of NGS-based RNA-seq products, catering to varied research needs across multiple domains.

What will the Size of the Market be During the Forecast Period?

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Market Dynamics and Customer Landscape

Technologies like Illumina's platforms offer significant advantages in genomic projects, enabling precise pricing analysis and patent analysis to optimize buying behaviour. RNA-seq provides deeper insights into cancer cases through NGS in cancer research, offering 10X coverage for comprehensive human genome sequencing and targeted studies on specific organisms. The cost of genomic sequencing continues to decrease, enhancing affordability and accessibility for standardizing tests and improving data quality in genomic studies. Conference and webinars disseminate webinar materials on conventional technologies versus NGS, highlighting the advantages of RNA and driving continuous innovation in genomic research methodologies.

Key Market Driver

The increased adoption of next-generation sequencing methods is the key factor driving the global market. Rapid developments in next-generation sequencing techniques and the creation of a human genome database have allowed Companies to offer rapid diagnostic services and the capability to diagnose mutations and disorders in human gene sequences by using the complete human genome to study its structure, function, and organization.

Moreover, it offers a significant reduction of cost in the performance of sequential studies and bears higher variant detection power and sensitivity by enabling the sequencing of millions of DNA fragments per run simultaneously, compared with conventional Sanger sequencing technology. The techniques provide high processing speed and throughput that can generate a vast number of sequences with many applications in research, as well as in the diagnostic field. Researchers are thoroughly studying and developing further prospectus, which is expected to improve the performance of these techniques as a reliable solution and augment the growth of the global market during the forecast period.

Significant Market Trends

The advances in next-generation sequencing techniques are major market trends. The advent of these techniques and the significant contribution of HGP have provided companies and researchers with a critical resource on the function, structure, and organization of a complete set of human genomes. Technological innovation in the field of genomics has significantly reduced the cost of sequencing, making next-generation sequencing available to many smaller laboratories. This has further boosted the growth of genomic research.

The rising number of research activities and discoveries in genetic testing for determining genetic variants has enabled companies, such as Bio-Rad Laboratories and Eurofins, to offer a wide variety of prediction tests for blood sugar regulation, cancer, vision loss, and autoimmune disorders. The development of advanced technologies has helped to reduce the cost of testing as well as the turnaround time. Furthermore, the development of portable technologies by companies such as Oxford Nanopore Technologies, hybridization of available technologies such as SMRT sequencing and reversible semiconductor sequencing, and technological advances in bioinformatics software are expected to augment the growth of the global market during the forecast period.

Major Market Challenge

The lack of clinical validation on direct-to-consumer genetic tests is a major challenge to the global market. The clinical validity of direct-to-consumer genetic tests has been consistently questioned due to the presence of limited scientific evidence. This negatively impacts the commercialization of pre-disposition tests. Moreover, disease risk prediction provided by these tests does not include the overall context for risk assessment as it excludes the environmental and lifestyle factors, which play a critical role in increasing the risk of getting a disease.

Direct-to-consumer genetic tests have limited accuracy and can often generate false-positive or

RNA sequencing (RNA-seq) is widely used for RNA quantification in the environmental, biological and medical sciences. It enables the description of genome-wide patterns of expression and the identification of regulatory interactions and networks. The aim of RNA-seq data analyses is to achieve rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite variation in levels of noise and inherent biases in sequencing data. This can be especially challenging for datasets in which gene expression differences are subtle, as in the behavioural transcriptomics test dataset from D. melanogaster that we used here. We investigated the power of existing approaches for quality checking mRNA-seq data and explored additional, quantitative quality checks. To accommodate nested, multi-level experimental designs, we incorporated sample layout into our analyses. We employed a subsampling without replacement-based normalization and an identification of DE that accounted for the hierarchy and amplitude of effect sizes within samples, then evaluated the resulting differential expression call in comparison to existing approaches. In a final step to test for broader applicability, we applied our approaches to a published set of H. sapiens mRNA-seq samples, The dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. The proposed approaches have the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments.

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.

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

Deep sequencing of transcriptomes allows quantitative and qualitative analysis of many RNA species in a sample, with parallel comparison of expression levels, splicing variants, natural antisense transcripts, RNA editing and transcriptional start and stop sites the ideal goal. By computational modeling, we show how libraries of multiple insert sizes combined with strand-specific, paired-end (SS-PE) sequencing can increase the information gained on alternative splicing, especially in higher eukaryotes. Despite the benefits of gaining SS-PE data with paired ends of varying distance, the standard Illumina protocol allows only non-strand-specific, paired-end sequencing with a single insert size. Here, we modify the Illumina RNA ligation protocol to allow SS-PE sequencing by using a custom pre-adenylated 3’ adaptor. We generate parallel libraries with differing insert sizes to aid deconvolution of alternative splicing events and to characterize the extent and distribution of natural antisense transcription in C. elegans. Despite stringent requirements for detection of alternative splicing, our data increases the number of intron retention and exon skipping events annotated in the Wormbase genome annotations by 127 % and 121 %, respectively. We show that parallel libraries with a range of insert sizes increase transcriptomic information gained by sequencing and that by current established benchmarks our protocol gives competitive results with respect to library quality. Sequencing of mRNA from C. elegans with libraries of four differing insert sizes

Mussel Growth Data - UCSB - Mussel body size data for RNA-Seq project

63h D-hinge larvae reared under 350µatm and 1300 µatm, described in: High pCO2 affects body size, but not gene expression in larvae of the California mussel (Mytilus californianus)

Sample Id: \"11-L-un\" or \"12-H-un\"
Where H refers to high CO2
L= low CO2
The number is bucket number
And 'un' refers to the fact the cultures were unfiltered

Related Reference:
Kelly, M.W., J. L. Padilla-Gamiño, G. E. Hofmann (2015) High pCO₂ affects body size, but not gene expression in larvae of the California mussel (Mytilus californianus). ICES Journal of Marine Science. doi: 10.1093/icesjms/fsv184.

RNA sequencing analysis of atrial cardiac tissue from patients undergoing coronary artery bypass grafting (CABG) or aortic valve replacement (AVR) was performed and compared with atrial cardiac tissue from organ donors and purchased human atrial cardiac RNA. RNA sequencing analysis was performed at the Genomics Core Facility at University of Gothenburg, Sweden. All samples (ten controls, ten CABG patients and ten AVR patients) were quality checked by the RNA integrity number (RIN) using Tapestation 2200 RNA screenTape (Agilent Technologies, Santa Clara, CA) and RNA concentration was measured by NanoDrop (Thermo Fisher, Waltham, MA). RIN values ranged between 6.6 and 9.0 for all samples. TruSeq Total Stranded RNA kit with RiboZero (Gold) Sample Preparation Guide (15,031,048 Rev. E) was used for RNA sample preparation (Illumina, San Diego, CA). A total of 10 μl (~1 μg) RNA from each sample was used for library preparation. Directly after depletion, a cleanup step was performed using 110 ul of the RNAClean XP beads (Beckman Coulter, USA) for each sample. The fragmentation step was performed for 8 min. 12 PCR cycles were run for all samples. Libraries were quantified and normalized with Qubit DNA HS Assay kit (Life Technologies, Carlsbad, CA) and fragment size determined by Tapestation 2200 (Agilent Technologies, Santa Clara, CA). The libraries were pooled together by using the Illumina protocol for pooling and sequenced with NovaSeq 6000 S1 (Illumina, San Diego, CA) for the read length of 2 × 100 bp.

The RNA-Seq market is experiencing robust growth, driven by advancements in sequencing technologies, increasing research funding in genomics, and the rising prevalence of various diseases requiring advanced diagnostic tools. The market, segmented by type (sRNA-Seq, targeted RNA-Seq, long-read RNA-Seq) and application (research institutes, hospitals & clinics, biotechnology companies), is witnessing a significant shift towards the adoption of high-throughput sequencing technologies offering improved accuracy and cost-effectiveness. Long-read RNA-Seq, while currently a smaller segment, is projected to experience substantial growth due to its ability to resolve complex transcript isoforms and structural variations, crucial for understanding gene regulation and disease mechanisms. The increasing availability of bioinformatics tools and analytical software further fuels market expansion, enabling researchers to extract valuable insights from the vast datasets generated by RNA-Seq. Competition is fierce, with established players like Illumina and Thermo Fisher Scientific alongside emerging companies vying for market share. Geographic regions such as North America and Europe currently dominate the market, but the Asia-Pacific region, particularly China and India, is anticipated to showcase significant growth driven by expanding research infrastructure and increasing healthcare investments. The restraints to market growth include the high cost associated with RNA-Seq, the need for specialized expertise in data analysis, and potential ethical concerns related to genomic data privacy. However, the ongoing technological advancements, decreasing sequencing costs, and the increasing awareness of the clinical applications of RNA-Seq are expected to mitigate these challenges. The forecast period (2025-2033) will likely see continued market expansion, with substantial contributions from both established and emerging players across diverse geographical regions. The market’s future trajectory is promising, underscored by the relentless pursuit of breakthroughs in genomic research and personalized medicine. This ongoing demand, along with a steady increase in funding for life sciences research, will likely drive further expansion of the RNA-Seq market.

The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.

Companion Repositories:

1. Long-Read-Proteogenomics Workflow GitHub Repository Release
2. Long-Read-Proteogenomics Analysis GitHub Repository Release

Companion Datasets

1. Long-Read-Proteogenomics Workflow Sample and Reference Data
2. TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions

This Repository contains the complete output from the execution of the Long-Read-Proteogenomics Workflow, using the input from Jurkat Samples and Reference Data.

The file jurkat.flnc.bam was 6.5 GB had to be split into 13 separate files and for use should be rejoined -- here are the steps that were used to split the file up.

1. Convert jurkat.flnc.bam (binary format) to sam file (text format) without header: samtools view jurkat.flnc.bam > jurkat.flnc.sam

2. Capture the header: samtools view -H jurkat.flnc.bam > jurkat.flnc.header.sam

3. Split jurkat.flnc.sam into smaller files (aim to get final size under 2GB): split -l 400000 jurkat.flnc.sam jurkat.flnc.chunk.

4. Convert each of these files back to bam for uploading: samtools view -b jurkat.flnc.chunk.a* -o jurkat.flnc.chunk.a*.bam (*=a,b,c,d,e,f,g,h,i,j,k,l,m)

After downloading, reverse this process including using the header file which is found in the LRPG-Manuscript-Results-results-results-jurkat-isoseq3-companion-files.tar.gz file>

1. Convert the bam files back to sam files: samtools view jurkat.flnc.chunk.a*.bam > jurkat.flnc.chunk.a*.sam (*=a,b,c,d,e,f,g,h,i,j,k,l,m)

2. Combine the header together with the sam files: cat jurkat.flnc.chunk.a*sam > jurkcat.flnc.sam (verified the same number of lines of the sam files is identical to the number of lines of the original without header: 4,956,761. Header file is 13 lines.

3. Convert to bam files if desired: samtools view -b jurkat.flnc.sam -o jurkat.flnc.bam

4. Rehead with the header file: samtools reheader -P -i jurkat.flnc.header.sam jurkat.flnc.bam

The global Genomic Data Analysis Service market is experiencing robust growth, projected to reach $4192.3 million in 2025. While the provided CAGR is missing, considering the rapid advancements in genomics technologies and increasing demand for personalized medicine, a conservative estimate of 15% CAGR from 2025-2033 seems reasonable. This implies significant market expansion, driven by factors such as decreasing sequencing costs, growing adoption of next-generation sequencing (NGS) technologies, and the increasing need for efficient and accurate analysis of large genomic datasets. The market is segmented by application (humanity, plant, animal, microorganism, virus) and by type of analysis (whole genome sequence analysis, whole exome sequence analysis, and others). The growth is fueled by the expanding application of genomic analysis across diverse sectors like healthcare, agriculture, and environmental science. Whole genome sequencing is expected to dominate the market due to its comprehensive nature, providing a complete picture of an organism's genetic makeup. However, whole exome sequencing remains a significant segment due to its cost-effectiveness and ability to target specific protein-coding regions. Key players such as Illumina, QIAGEN, and BGI Genomics are leading the market through continuous innovation in software and analytical tools. The market's geographical spread is substantial, with North America and Europe currently holding the largest market shares due to well-established research infrastructure and technological advancements. However, the Asia-Pacific region is projected to witness significant growth driven by rising investments in healthcare infrastructure and increasing adoption of genomic technologies. The market is expected to continue its upward trajectory throughout the forecast period (2025-2033), driven by ongoing technological innovations that enhance data analysis speed and accuracy. The increasing availability of large genomic datasets, fueled by large-scale genomics initiatives, provides a fertile ground for the development of advanced analytical tools. Furthermore, the increasing demand for personalized medicine and precision agriculture is further accelerating the adoption of genomic data analysis services. However, challenges remain, including the need for standardized data formats, data security concerns associated with handling sensitive genomic data, and the need for skilled professionals to interpret and utilize the complex data generated. Addressing these challenges will be critical for continued market growth and widespread adoption of genomic data analysis services.

Data scarcity presents a significant obstacle in the field of biomedicine, where acquiring diverse and sufficient datasets can be costly and challenging. Synthetic data generation offers a potential solution to this problem by expanding dataset sizes, thereby enabling the training of more robust and generalizable machine learning models. Although previous studies have explored synthetic data generation for cancer diagnosis, they have predominantly focused on single-modality settings, such as whole-slide image tiles or RNA-Seq data. To bridge this gap, we propose a novel approach, RNA-Cascaded-Diffusion-Model or RNA-CDM, for performing RNA-to-image synthesis in a multi-cancer context, drawing inspiration from successful text-to-image synthesis models used in natural images. In our approach, we employ a variational auto-encoder to reduce the dimensionality of a patient’s gene expression profile, effectively distinguishing between different types of cancer. Subsequently, we employ a cascad..., , , # RNA-CDM Generated One Million Synthetic Images

https://doi.org/10.5061/dryad.6djh9w174

One million synthetic digital pathology images were generated using the RNA-CDM model presented in the paper "RNA-to-image multi-cancer synthesis using cascaded diffusion models".

Description of the data and file structure

There are ten different h5 files per cancer type (TCGA-CESC, TCGA-COAD, TCGA-KIRP, TCGA-GBM, TCGA-LUAD). Each h5 file contains 20.000 images. The key is the tile number, ranging from 0-20,000 in the first file, and from 180,000-200,000 in the last file. The tiles are saved as numpy arrays.

Code/Software

The code used to generate this data is available under academic license in https://rna-cdm.stanford.edu .

Manuscript citation

Carrillo-Perez, F., Pizurica, M., Zheng, Y. et al. Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models...

The success of mRNA vaccines has been realised, in part, by advances in manufacturing that enabled billions of doses to be produced at sufficient quality and safety. However, mRNA vaccines must be rigorously analysed to measure their integrity and detect contaminants that reduce their effectiveness and induce side-effects. Currently, mRNA vaccines and therapies are analysed using a range of time-consuming and costly methods. Here we describe a streamlined method to analyse mRNA vaccines and therapies using long-read nanopore sequencing. Compared to other industry-standard techniques, VAX-seq can comprehensively measure key mRNA vaccine quality attributes, including sequence, length, integrity, and purity. We also show how direct RNA sequencing can analyse mRNA chemistry, including the detection of nucleoside modifications. To support this approach, we provide supporting software to automatically report on mRNA and plasmid template quality and integrity. Given these advantages, we antici..., Data are analyses of mRNA vaccines, sequenced using Oxford Nanopore sequencing. These include alignment files (.bam and .bai) of a control eGFP mRNA vaccine and its plasmid template. Additionally there analyses of poly(A) tail length, performed using tailfindr software. , The .bam and .bai files can be opened using IGV. The .csv files can be opened using a text editor or Excel.

Accurate quantification of gene and transcript-specific expression, with the underlying knowledge of precise transcript isoforms, is crucial to understanding many biological processes. Analysis of RNA sequencing data has benefited from the development of alignment-free algorithms which enhance the precision and speed of expression analysis. However, such algorithms require a reference transcriptome. Here we present a reference transcript dataset (LsRTDv1) for lettuce, combining long- and short-read sequencing with publicly available transcriptome annotations, and filtering to keep only transcripts with high-confidence splice junctions and transcriptional start and end sites. LsRTDv1 is a valuable resource for the investigation of transcriptional and alternative splicing regulation in lettuce. Methods We generated a lettuce Reference Transcript Dataset (LsRTDv1) by integrating transcript assemblies from short- and long-read RNA sequencing data with existing lettuce genome annotations. RNA sequencing data was generated from 23 different lettuce samples capturing different tissues, ages of plant and treatments. The 23 samples, all from Lactuca sativa cv. Saladin (synonymous with cv. Salinas) were combined equally into 7 samples prior to sequencing. Short-read assembly The RNA-seq reads of the seven pooled samples were pre-processed with Fastp (Chen et al., 2018) to remove adapters and filter low-quality reads (quality score <20, length <30). Trimmed reads were mapped to the latest lettuce reference genome assembly in NCBI (Lsat_Salinas_v11) using STAR aligner in the 2-pass mode to increase the mapping sensitivity at splice junctions (SJs)(Dobin and Gingeras, 2015). Mismatch was set to 1 with minimum and maximum intron sizes of 60 and 15,000 bp respectively. Two transcript assemblers, StringTie (Pertea et al., 2015) and Scallop (Shao and Kingsford, 2017), were used to assemble transcripts for each sample. The assemblies were then merged and refined using RTDmaker (https://github.com/anonconda/RTDmaker) to remove low-quality transcripts, including redundant transcripts with identical intron combinations to longer transcripts, fragmented transcripts with length <70% of gene length, transcripts with non-canonical SJs, transcripts with SJs only supported by <5 spliced reads in <2 samples and low expressed transcripts with <1 transcript per million reads (TPM) in <2 samples. Long-read assembly We employed the IsoSeq pipeline (https://github.com/PacificBiosciences/IsoSeq) to pre-process the Iso-seq data from the seven samples. The CCS method was used to generate circular consensus sequences (CCS) from raw subreads and reads with minimum predicted accuracy <90% were discarded (--min-rq=0.9). Barcodes associated with the CCS reads were eliminated using the lima method. To further refine the reads, Isoseq3 was applied to trim poly(A) tails and identify and remove concatemers. The output of full-length, non-concatemer (FLNC) reads was mapped to the reference genome using Minimap2 (Li, 2018). TAMA-collapse was used to collapse redundant transcript models in each sample with variation at the 5’ and 3’ ends and at SJs not allowed (-a = 0, -m = 0 and -z = 0) to ensure high accuracy of boundaries. Reads with errors within the 10 bp up- or down-stream of a SJ were removed. TAMA-merge was used to merge transcript models from the seven samples (Kuo et al., 2020). To improve the quality of the assembly, we implemented well-established methods for SJ and transcript start site (TSS) and end site (TES) analyses previously used for Arabidopsis AtRTD3 and barley BaRTv2 (Zhang et al., 2022b; Coulter et al., 2022). We removed low-quality transcripts that exhibited non-canonical SJs and low quality SJs unless they were also present in the short-read assembly. We applied a binomial test to distinguish high-confidence TSS and TES with a false discovery rate (FDR) <0.05. For genes with limited read support, statistical testing becomes challenging, hence we also kept TSS/TES if they were supported by at least 2 Iso-seq reads. Redundancy merge was applied to transcripts if they only differed ±50 nucleotides at their TSS/TES. In addition, transcripts only supported by a single Iso-seq read were removed from the final dataset. Integration of multiple annotations We integrated four transcript annotations: the long-read assembly, short-read assembly and two versions of Lsat_Salinas_v11 genome annotations GenBank (GCA_002870075.4) and RefSeq (GCF_002870075.4). The Iso-seq long-read assembly served as the reliable backbone, while the other three annotations were incorporated in a step-wise manner to improve the RTD completeness. Firstly, the transcripts in the short-read assembly that introduce novel SJs and/or novel gene loci were integrated into the long-read assembly. Subsequently, we added transcripts from GenBank and RefSeq annotations that contributed novel SJs or gene loci to build the lettuce RTD (LsRTDv1). In cases where two transcripts from GenBank and RefSeq had identical SJ combinations or were mono-exonic transcripts with overlapping regions exceeding 30% of both transcripts, we collapsed them to a single transcript, and the longest TSS and TES were used as the start and end point of the collapsed transcript. In LsRTDv1, the overlapped transcripts were assigned the same gene ID. However, if a set of overlapped transcripts entirely resided within the intron region of other transcripts, they were treated as intronic transcripts and assigned with a different gene ID. Where the overlapped transcripts can be divided into multiple groups and the adjacent groups overlapped less than 5% of the group lengths, they were assigned separate gene IDs.

The global genomic data analysis service market is experiencing robust growth, projected to reach $1769.8 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 13.1% from 2025 to 2033. This expansion is fueled by several key factors. Advances in next-generation sequencing (NGS) technologies are generating massive datasets, driving the demand for sophisticated analytical tools and services. The increasing affordability of genomic sequencing, coupled with expanding applications across healthcare, agriculture, and environmental research, further contributes to market growth. Specifically, the human application segment dominates, driven by personalized medicine initiatives and the growing understanding of the role of genomics in disease diagnosis and treatment. The rise of cloud-based data analysis platforms, offering scalability and cost-effectiveness, is also a significant driver. While data security and privacy concerns present a challenge, the development of robust data management and security protocols is mitigating this risk. Furthermore, the growing adoption of AI and machine learning in genomic data analysis enhances accuracy and efficiency, accelerating market growth. Segmentation within the market reveals strong performance across various application areas. Whole Genome Sequence Analysis and Whole Exome Sequence Analysis segments are major contributors, reflecting the comprehensive nature of the data generated and the insights derived. Geographically, North America currently holds a significant market share, driven by early adoption of advanced technologies and strong funding for research and development. However, Asia Pacific is anticipated to experience rapid growth, fueled by increasing investments in healthcare infrastructure and expanding genomics research activities in countries like China and India. Competition is intense, with established players like Illumina and QIAGEN alongside emerging companies offering specialized solutions. The continuous innovation in sequencing technologies and analytical methods ensures the ongoing evolution and expansion of this dynamic market.

Test data for The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.

Companion Repositories:

1. Long-Read-Proteogenomics Workflow GitHub Repository Release
2. Long-Read-Proteogenomics Analysis GitHub Repository Release

Companion Datasets

1. Jurkat Samples and Reference Data
2. Long-Read-Proteogenomics Workflow Results using Jurkat Sample data

This Repository contains the test data, specifically:

TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions

Full transcriptome (RNA-sequencing) from bulk CD34+ bone marrow mononuclear cells from MDS patients with ring sideroblasts. CD34+ cells were isolated from the MNC using AUTO-MACS with double-separation option (Miltenyi Biotec, Germany) and submitted for RNA extraction. RNA was extracted with RNeasy Microkit (Qiagen, Hilden, Germany) and treated with DNase, according to manufacturer instruction. RNA integrity number was estimated using Agilent RNA 6000 Pico (Agilent Technologies, Palo Alto, CA) and was greater than 6.5 for all the samples (median 8.2). The RNA-sequencing (RNA-seq) libraries were prepared from total RNA using SMARTer Stranded Total RNA-Seq Kit v2 Pico Input Mammalian with enzymatic ribosomal depletion (Takara Bio, Japan). Libraries were sequenced using the Novaseq 6000 with paired-end 150bp configuration. The molecular data were integrated with clinical information aiming to improve prognosis prediction in this hematologic malignancy. The dataset consists of 2 files: - FASTQ_RS.tar.gz: compressed folder that includes 258 fastq files - metadata_RS.xlsx The total size of the dataset is approximately 1 TB. Heltranskriptom-sekvensering (RNA-seq) från CD34-uttryckande mononukleära benmärgsceller från patienter med myelodysplastisk syndrom med ringsideroblaster (MDS-RS). CD34-uttryckande celler isolerades från mononukleära benmärgsceller via instrumentet AUTO-MACS med dubbelseparation (Miltenyi Biotec, Germany). RNA extraherades från CD34-uttryckande celler via RNeasy Microkit (Qiagen, Hilden, Germany) och behandlades därefter med DNase i enlighet med tillverkarens instruktion. RNA integritetsnumret uppskattades sedan via Agilent RNA 6000 Pico (Agilent Technologies, Palo Alto, CA) och var högre än 6.5 i alla prover (median 8.2). RNA sekvenseringsbiblioteken sattes upp från allt RNA via SMARTer Stranded Total RNA-Seq Kit v2 Pico Input Mammalian med enzymatisk degradering av ribosomalt RNA (Takara Bio, Japan). RNA-biblioteken sekvenserades sedan på Novaseq 6000 med ”paired-end 150bp” inställning. Slutligen kombinerade vi molekylära och kliniska data i syfte att hitta nya prognostiska markörer och förbättra karaktärisering av sjukdomen hos patienter med MDS. Datasetet består av två filer: - FASTQ_RS.tar.gz: komprimerad mapp innehållande 258 fastq-filer - metadata_RS.xlsx Datasetets totala storlek är ca 1 TB. We studied 129 MDS patients with ring sideroblasts within a population of 834 myeloid neoplasms evaluated at Karolinska University Hospital in Stockholm between February 2004 and August 2020. CD34+ cells were isolated from the MNC using AUTO-MACS with double-separation option (Miltenyi Biotec, Germany) and submitted for RNA extraction for all cases and controls. The RNA-sequencing (RNA-seq) libraries were prepared from total RNA using SMARTer Stranded Total RNA-Seq Kit v2 Pico Input Mammalian with enzymatic ribosomal depletion (Takara Bio, Japan). Libraries were sequenced using the Novaseq 6000 with paired-end 150bp configuration.

To evaluate RNA recovery, transcripts in reference genome GRCH37.p13[hg19] [37] were used to identify the sequenced transcripts in the three data files. The lists of 19,608 mRNA and 6,725 lncRNA transcripts used to identify sequenced mRNA and lncRNA transcripts in the three data set are provided. (XLSX)

Glucocorticoid steroid hormones play essential roles for maturation and growth of many fetal organs including the lung and heart, yet the kidney-specific roles are not well characterised. Glucocorticoids activate the intracellular glucocorticoid receptor (GR) that acts primarily as nuclear transcriptional regulators. We analysed the effect of loss of GR expression on the fetal kidney transcriptome at E18.5 by RNA sequencing. Total RNA was extracted from control (n=4) and GR-null (n=3) kidneys. Loss of GR expression resulted in 2473 differentially expressed genes (FDR < 0.05), 288 genes with absolute LogFC > 1 & FDR < 0.05, which identified 16 upregulated and 25 downregulated primary ciliary genes (FDR < 0.05). Primary cilia are cell signalling and environment sensing organelles that protrude from cell membranes and play important roles during embryogenesis and tissue homeostasis. Little is known of the cellular pathways regulating ciliogenesis. Our findings indicate a role of glucocorticoid signalling in primary cilia formation in renal tubular cells of the developing mouse kidney.

Total RNA was isolated from embryonic kidneys using TRIzolTM reagent (Invitogen, USA) according to the manufacturer’s instructions. Total RNA was analysed using a Bioanalyzer 2100 (Agilent Technologies, USA) and Next generation RNA sequencing (NGS RNA-seq) was performed by Genewiz Biotechnology, Suzhou, China. RNA sequencing (20 million reads) was performed on the Illumina Hiseq platform, in a 2 x 150 bp paired-end format.

Total RNA of each sample was extracted using TRIzol reagent (Invitrogen) following the manufacturer's instructions.Next generation sequencing library preparations were constructed according to the manufacture's protcol. The The poly(A) mRNA isolation was performed using Poly(A) mRNA Magnetic Isolation Module or rRNA removal Kit. The mRNA fragmentation and priming was performed using First Strand Synthesis Reaction Buffer and Random Primers. First strand cDNA was synthesized using ProtoScript II Reverse Transcriptase and the second-strand cDNA was synthesized using Second Strand Synthesis Enzyme Mix. The purified double stranded cDNA by beads was then treated with End Prep Enzyme Mix to repair both ends and add a dA tailing in one reaction, followed by a T-A ligation to add adaptors to both ends. Size selection of Adaptor ligated DNA was then performed using beads, and fragments of ~420 bp (with the approximate insert size of 300 bp) were recovered. Each sample was then amplified by PCR for 13 cycles using P5 and P7 primers, with both primers carrying sequences which can anneal with flow cell to perform bridge PCR and P7 primer carrying a six-base index allowing for multiplexing. The PCR products were cleaned up using beads, validated using an Qsep100 (Bioptic, Taiwan, China), and quantified by Qubit3.0 Fluorometer (Invitrogen, Carlsbad, CA, USA).

In order to remove technical sequences, including adapters, polymerase chain reaction (PCR) primers, or fragments thereof, and quality of bases lower than 20, pass filter data of fastq format were processed by Cutadapt (V1.9.1) to be high quality clean data.

Firstly, reference genome sequences and gene model annotation files of relative species (GRm39.97) were downloaded from genome website, such as UCSC, NCBI, ENSEMBL. Secondly, Hisat2 (v2.0.1) was used to index reference genome sequence. Finally, clean data were aligned to reference genome via software Hisat2 (v2.0.1).

In the beginning transcripts in fasta format are converted from known gff annotation file and indexed properly. Then, with the file as a reference gene file, HTSeq (v0.6.1) estimated gene and isoform expression levels from the pair-end clean data.

The data provided here are part of a Galaxy Training Network tutorial that analyzes small RNA-seq (sRNA-seq) data from a study published by Harrington et al. (DOI:10.1186/s12864-017-3692-8) to detect differential abundance of various classes of endogenous short interfering RNAs (esiRNAs). The goal of this study was to investigate "connections between differential retroTn and hp-derived esiRNA processing and cellular location, and to investigate the potential link between mRNA 3' end cleavage and esiRNA biogenesis." To this end, sRNA-seq libraries were constructed from triplicate Drosophila tissue culture samples under conditions of either control RNAi or RNAi knockdown of a factor involved in mRNA 3' end processing, Symplekin. This dataset (GEO Accession: GSE82128) consists of single-end, size-selected, non-rRNA-depleted sRNA-seq libraries. Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to a subset of interesting transcript features including: (1) transposable elements, (2) Drosophila piRNA clusters, (3) Symplekin, and (4) genes encoding mass spectrometry-defined protein binding partners of Symplekin from Additional File 2 in the indicated paper by Harrington et al. More details on features 1 and 2 can be found here: https://github.com/bowhan/piPipes/blob/master/common/dm3/genomic_features (piRNA_Cluster, Trn). All features are from the Drosophila genome Apr. 2006 (BDGP R5/dm3) release.

The goldfish (carassius auratus) is a valuable ornamental fish with the most diverse pheotypes, which is originally demoesticated from wild grey cruician carp in China over 1,000 yeas ago. Red color skin is the first trait fixed in goldfish and distinguished from its ancestor. However, the genomic resources about the speices is limited when performing the study, which heavely hampers our understanding of the genetic basis of the diverse phenotypes. To quickly provide a large amount of genomic resources and decipher the possible mechanism underlying diverse color skin in goldfish, We performed a large scale of transcriptome sequencing on 13 tissues and 4 typical color skins through combinng PacBio long-read sequencing and Illumina short-read sequencing. A full-length transcriptome with 137,674 transcripts was generated with mean length and N50 length of 2,956 bp and 4,017 bp, respectively. A total of 108,122 (78.53%) novel isoforms from known genes and 17,622 novel genes were identified compared to the annotation of recently published goldfish reference genome. Moreover, 59,014 alternative splicing events were present in 15,380 genes. A total of 162 differentially expressed genes (DEGs) were identified among the four different color skins, which were mainly involved in the pathways of Melanogenesis, Tyrosine metabolism, Riboflavin metabolism, Folate biosynthesis, and alpha-Linolenic acid metabolism. Fourteen DEGs may have function in pigmentation, including Melanophilin genes, solute carrier family 2 member 11 and solute carrier family 2 member 9. In conclusion, the genomic resources provided in study significantly improved the gene annotation of the published referecne genome and enhanced the understanding of the goldfish pigmentation pathways and will finally facilitate future studies on genetic basis of goldfish diverse phenotypes.