Next-generation RNA-sequencing is an incredibly powerful means of generating a snapshot of the transcriptomic state within a cell, tissue, or whole organism. As the questions addressed by RNA-sequencing (RNA-seq) become both more complex and greater in number, there is a need to simplify RNA-seq processing workflows, make them more efficient and interoperable, and capable of handling both large and small datasets. This is especially important for researchers who need to process hundreds to tens of thousands of RNA-seq datasets. To address these needs, we have developed a scalable, user-friendly, and easily deployable analysis suite called RMTA (Read Mapping, Transcript Assembly). RMTA can easily process thousands of RNA-seq datasets with features that include automated read quality analysis, filters for lowly expressed transcripts, and read counting for differential expression analysis. RMTA is containerized using Docker for easy deployment within any compute environment [cloud, local, or high-performance computing (HPC)] and is available as two apps in CyVerse's Discovery Environment, one for normal use and one specifically designed for introducing undergraduates and high school to RNA-seq analysis. For extremely large datasets (tens of thousands of FASTq files) we developed a high-throughput, scalable, and parallelized version of RMTA optimized for launching on the Open Science Grid (OSG) from within the Discovery Environment. OSG-RMTA allows users to utilize the Discovery Environment for data management, parallelization, and submitting jobs to OSG, and finally, employ the OSG for distributed, high throughput computing. Alternatively, OSG-RMTA can be run directly on the OSG through the command line. RMTA is designed to be useful for data scientists, of any skill level, interested in rapidly and reproducibly analyzing their large RNA-seq data sets.

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

Next-generation RNA-sequencing is an incredibly powerful means of generating a snapshot of the transcriptomic state within a cell, tissue, or whole organism. As the questions addressed by RNA-sequencing (RNA-seq) become both more complex and greater in number, there is a need to simplify RNA-seq processing workflows, make them more efficient and interoperable, and capable of handling both large and small datasets. This is especially important for researchers who need to process hundreds to tens of thousands of RNA-seq datasets. To address these needs, we have developed a scalable, user-friendly, and easily deployable analysis suite called RMTA (Read Mapping, Transcript Assembly). RMTA can easily process thousands of RNA-seq datasets with features that include automated read quality analysis, filters for lowly expressed transcripts, and read counting for differential expression analysis. RMTA is containerized using Docker for easy deployment within any compute environment [cloud, local, or high-performance computing (HPC)] and is available as two apps in CyVerse's Discovery Environment, one for normal use and one specifically designed for introducing undergraduates and high school to RNA-seq analysis. For extremely large datasets (tens of thousands of FASTq files) we developed a high-throughput, scalable, and parallelized version of RMTA optimized for launching on the Open Science Grid (OSG) from within the Discovery Environment. OSG-RMTA allows users to utilize the Discovery Environment for data management, parallelization, and submitting jobs to OSG, and finally, employ the OSG for distributed, high throughput computing. Alternatively, OSG-RMTA can be run directly on the OSG through the command line. RMTA is designed to be useful for data scientists, of any skill level, interested in rapidly and reproducibly analyzing their large RNA-seq data sets.

The output files and log files generated by the workflow executions for RNA-Seq workflow benchmark by CWL-metrics, from the manuscript "Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection" (https://doi.org/10.1101/456756).

Snapshot img

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

Even though high-throughput transcriptome sequencing is routinely performed in many laboratories, computational analysis of such data remains a cumbersome process often executed manually, hence error-prone and lacking reproducibility. For corresponding data processing, we introduce Curare, an easy-to-use yet versatile workflow builder for analyzing high-throughput RNA-Seq data focusing on differential gene expression experiments. Data analysis with Curare is customizable and subdivided into preprocessing, quality control, mapping, and downstream analysis stages, providing multiple options for each step while ensuring the reproducibility of the workflow. For a fast and straightforward exploration and visualization of differential gene expression results, we provide the gene expression visualizer software GenExVis. GenExVis can create various charts and tables from simple gene expression tables and DESeq2 results without the requirement to upload data or install software packages.

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

Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived flower development transcriptome profiling (RNA-seq) of two subspecies Methods: Flower mRNA profiles of wild-type (WT) four developmental stages and the same stages of Vitis vinifera subp vinifera were generated by deep sequencing using Illumina. Initial quality assessment was based on data passing the Illumina Chastity filtering. Subsequently, reads containing adapters and/or PhiX control signal were removed using an in-house filtering protocol. The second quality assessment was based on the remaining reads using the FASTQC quality control tool version 0.10.0. qRT–PCR validation was performed using EvaGreen assays. Results: Using an optimized data analysis workflow, we mapped about 13 to 19 million sequence reads per Vitis sample, 50 bp in length equivalent to 1.5 Gb of total sequence data by each sample. The exception was male stage G (M_G) were only 7 to 8 million sequence reads were obtained. Five genes (VvTFL1, VvLFY, VvAP1, Vv AP3, VvPI), related to flowering development, were used to validate RNA-Seq data and to test for data reproducibility through qRT–PCR. The coefficient of correlation (r) obtained between the log2 of RPKM (RNA-Seq) versus log2 of mRNA average number (RT-qPCR), varied from ≈ 0.97 (VvTLF) to ≈ 0.73 (VvPI) indicating a good correlation between both techniques and thus validating our RNA-Seq results. Conclusions: Our study represents the first detailed transcriptome analysis of four Vitis flower developmental stages, with the same individual, in three genders, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and accurate quantitative and qualitative evaluation of mRNA contentper developmental stage. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions. Flowering mRNA profiles of four developmental stages of Vitis wild type (WT) and the domesticated Vitis were generated by deep sequencing using Illumina HiSeq 2500.

Simulated RNA-seq data shows that histograms from p value sets with around one hundred true effects out of 20,000 features can be classified as 'uniform'. RNA-seq data was simulated with polyester R package (Frazee, 2015) on 20,000 transcripts from human transcriptome using grid of 3, 6, and 10 replicates and 100, 200, 400, and 800 effects for two groups. Fold changes were set to 0.5 and 2. Differential expression was assessed using DESeq2 R package (Love, 2014) using default settings and group 1 versus group 2 contrast. Effects denotes in facet labels the number of true effects and N denotes number of replicates. Red line denotes QC threshold used for dividing p histograms into discrete classes. Workflow and code used to run this simulation is available on rstats-tartu/simulate-rnaseq.

Files

• de_simulation_results.csv -- merged and processed DE analysis results of simulated data.
• simulate-reads-2021-01-25.tar.gz -- raw DE analysis results on 20,000 transcripts from human transcriptome using grid of 3, 6, and 10 replicates and 100, 200, 400, and 800 effects for two groups. Fold changes were set to 0.5, 1, and 2. Differential expression was assessed using DESeq2 with default settings.
• simulate-rnaseq.tar.gz -- snakemake workflow and input fasta file to simulate RNA-seq data with polyester and analyse results with DESeq2. Adjust settings in config.yaml to customise simulation. Includes software to run workflow on Linux, given that Conda and snakemake are installed.

The simulate-rnaseq.tar.gz archive can be re-executed on a vanilla machine that only has Conda and Snakemake installed via:

tar -xf simulate-rnaseq.tar.gz
snakemake --use-conda -n

RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, a full RNA-seq Bioinformatics workflow is time-consuming and costly in a clinical setting where rapid detection and accurate reporting of clinically relevant alterations are essential. To accelerate the identification of ALL-specific alterations (including gene fusions, single nucleotide variants and focal gene deletions), we developed the rapid screening tool RaScALL, capable of identifying more than 100 prognostically significant lesions directly from raw sequencing reads. RaScALL uses the k-mer based targeted detection tool km and known ALL variant information to achieve a high degree of accuracy for reporting subtype defining genomic alterations compared to standard alignment-based pipelines. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% (164 samples) of reported cases in a 180-patient Australian study cohort and 95% (n=63) of samples in a North American validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n=12) and IKZF1 p.N159Y (n=4). Accurate detection of intragenic IKZF1 deletions resulting in aberrant transcript isoforms was also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches, ensuring accelerated risk-stratification and therapeutic triage.

The data uploaded herein represents the transcriptomic sequencing results from the human esophageal squamous cell carcinoma cell line KYSE450, specifically comparing SLC8A1-knockout cells (KYSE450SLC8A1KO) with wild-type controls (KYSE450WT). The data generation process began with the separate cultivation of KYSE450SLC8A1KO and KYSE450WT cells until they reached a specific growth state. Qualified RNA samples were used to construct sequencing libraries, involving steps such as RNA fragmentation, cDNA synthesis, end repair, addition of A-tails, and adapter ligation. Finally, high-throughput sequencing was performed using the Illumina platform, generating a large volume of short-read sequence data.The data processing and analysis workflow started with quality control of the raw sequencing data (in .fastq format). Tools such as Fastp were employed to remove low-quality bases, adapter sequences, and ambiguous reads, thereby obtaining high-quality clean reads. Subsequently, these clean reads were aligned to the human reference genome using alignment software. After alignment, tools like featureCounts or HTSeq-count were used to count the number of reads mapping to each gene or transcript, thereby quantifying gene expression levels and generating a gene expression matrix. This matrix records the read counts (Read Count) for each gene within each sample and was ultimately organized and saved in .xls format. The uploaded data files primarily consist of this gene expression matrix, encompassing expression data from two samples (KYSE450SLC8A1KO and KYSE450WT). The data covers changes in gene expression levels across the entire genome. Temporal and spatial resolution are not applicable, as this is an in vitro cell line study. This dataset provides a foundation for studying the regulatory role of the SLC8A1 gene in esophageal squamous cell carcinoma and facilitates subsequent analyses such as differential expression analysis and pathway enrichment.

Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.

This dataset includes only the 10X Flex time course data and analysis.

The data uploaded herein represents the transcriptomic sequencing results from the human esophageal squamous cell carcinoma cell line KYSE450, specifically comparing SOX2-knockout cells (KYSE450SOX2KO) with wild-type controls (KYSE450WT). The data generation process began with the separate cultivation of KYSE450SOX2KO and KYSE450WT cells until they reached a specific growth state. Total RNA was then extracted using commercially available RNA extraction kits. Following extraction, the quality of the isolated total RNA was assessed to ensure it met the requirements for library construction. Qualified RNA samples were used to construct sequencing libraries, involving steps such as RNA fragmentation, cDNA synthesis, end repair, addition of A-tails, and adapter ligation. Finally, high-throughput sequencing was performed using the Illumina platform, generating a large volume of short-read sequence data.The data processing and analysis workflow started with quality control of the raw sequencing data (in .fastq format). Tools such as Fastp were employed to remove low-quality bases, adapter sequences, and ambiguous reads, thereby obtaining high-quality clean reads. Subsequently, these clean reads were aligned to the human reference genome using alignment software. After alignment, tools like featureCounts or HTSeq-count were used to count the number of reads mapping to each gene or transcript, thereby quantifying gene expression levels and generating a gene expression matrix. This matrix records the read counts (Read Count) for each gene within each sample and was ultimately organized and saved in .xls format. The uploaded data files primarily consist of this gene expression matrix, encompassing expression data from two samples (KYSE450SOX2KO and KYSE450WT). The data covers changes in gene expression levels across the entire genome. Temporal and spatial resolution are not applicable, as this is an in vitro cell line study. This dataset provides a foundation for studying the regulatory role of the SOX2 gene in esophageal squamous cell carcinoma and facilitates subsequent analyses such as differential expression analysis and pathway enrichment.

Conventional (bulk) RNA-sequencing was performed on unfractionated cell suspension or snap frozen whole tissue material. Total RNA was isolated with TRIzol reagent followed by purification over PureLink RNA Mini Kit columns (Invitrogen). RNA-seq was performed using a polyA-enriched strand-specific library construction protocol (doi: 10.1016/j.ccell.2016.02.009) and paired-end 75bp sequencing on an Illumina HiSeq 2500 instrument. Raw reads were aligned to the reference human genome assembly GRCh37 (hg19) using STAR (v2.5.2.a). To improve spliced alignment, STAR was provided with exon junction coordinates from the reference annotations (Gencode v19). We applied a modified version of a bioinformatics workflow for normalization of raw read counts and differential gene expression analysis (doi: 10.12688/f1000research.9005.3). Gene-level read counts were quantified using HTSEQ-count (v0.11.0; intersection-strict, reverse mode) (doi: 10.1093/bioinformatics/btu638). Genes showing low read counts (i.e., genes not showing counts per million (cpm) > 1.0 in at least 10% of samples) were removed from further analysis. Raw counts from expressed genes were then TMM-normalized and scaled to counts per million (CPM) using the edgeR (v3.22.2) package (doi: 10.1093/bioinformatics/btp616). Sample IDs correspond to those referenced in Wang X et al, Nature Communications (2022).

The Next-Generation Sequencing (NGS)-based RNA sequencing (RNA-seq) market is experiencing robust growth, driven by advancements in sequencing technologies, increasing research funding for genomic studies, and the expanding application of RNA-seq in various fields. The market's value in 2025 is estimated to be around $5 billion, with a Compound Annual Growth Rate (CAGR) projected at approximately 15% from 2025 to 2033. This substantial growth is fueled by the rising demand for personalized medicine, the increasing prevalence of chronic diseases necessitating improved diagnostics, and the growing adoption of RNA-seq in drug discovery and development. High-throughput sequencing currently dominates the market due to its cost-effectiveness and high throughput, but third-generation sequencing technologies are gaining traction due to their longer read lengths and potential for improved accuracy and reduced bias. Key market segments include hospitals and clinics, biopharmaceutical companies, and academic research organizations, with North America and Europe representing the largest regional markets. The market is highly competitive, with key players such as Illumina, Thermo Fisher Scientific, and Pacific Biosciences leading the innovation and market share. Despite the significant growth, the market faces certain restraints including the high cost of NGS platforms and data analysis, the complexity of RNA-seq workflows requiring specialized expertise, and ethical considerations related to data privacy and informed consent. However, these challenges are being actively addressed through technological advancements, the development of user-friendly software, and the establishment of clear ethical guidelines. Ongoing research into novel RNA biomarkers and their application in disease diagnosis and treatment is expected to further drive market growth. The diverse applications of NGS-based RNA-seq, ranging from cancer research and personalized oncology to infectious disease surveillance and agricultural genomics, ensures the market's long-term sustainability and future potential. The continued investment in research and development, coupled with a growing awareness of the clinical and research applications of this technology, will ensure sustained expansion in this rapidly evolving market segment.

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

Purpose: Next-generation sequencing (NGS) has been utilized for systems-based analysis of all liver samples. The goals of this study are to use NGS-derived mouse CAR and human CAR initiated transcriptome profiling (RNA-seq) and find out similarity and difference drug processing gene (DPG) pattern after CAR activation in different genotype include WT (C57BL/6 and human CAR transgenic mice with C57BL/6 background)Methods: Liver mRNA profiles of wild-type (WT) and human CAR knockin (hCAR-TG) mice at the age of day 5 and day 60 treated with mouse CAR activator (TCPOBOP) and human CAR activator (CITCO) respectively were generated by deep sequencing, in triplicate, using HiSeq 2000 sequencer. The sequence reads that passed quality filters were analyzed at the transcript level with followed method: HISAT followed by Cufflinks.Results: Using an optimized data analysis workflow,RNA-Seq generated approximately 47 to 68 million reads per sample, among which approximately 40 to 60 million reads were uniquely mapped to the mouse reference genome (NCBI GRCm/38/mm10). And we identified 393 drug processing genes in the livers of WT and hCAR-TG with with HISAT workflow. RNA-seq data confirmed that among all the 393 DPGs with known important functions in xenobiotic biotransformation, 90 DPGs were not expressed in livers of any groups (threshold: average FPKM < 1 in all treatment groups); whereas a total of 303 genes were expressed in livers of at least one groups, among which 258 DPGs were differentially regulated by mCAR or hCAR activation in either Day 5 or Day 60 (FDR-BH<0.05), and 45 genes were stably expressed among all treatment groups.Conclusions: Our study represents the first detailed analysis of drug processing genes, with 3 biologic replicates, generated by RNA-seq technology. The optimized data analysis reported here should provide a framework for comparative investigations of expression profiles by mouse CAR activation and human CAR activation. Our results show that NGS offers a comprehensive and accurate quantitative and qualitative evaluation of mRNA content within tissues. Liver mRNA profiles of wild type (WT) and human CAR knockin (hCAR-TG) mice at the age of day 5 and day 60 treated with TCPOBOP and CITCO respectively were generated by deep sequencing, in triplicate, using HiSeq 2000 sequencer.

This dataset details the scRNASeq and TCR-Seq analysis of sorted PD-1+ CD8+ T cells from patients with melanoma treated with checkpoint therapy (anti-PD-1 monotherapy and anti-PD-1 & anti-CTLA-4 combination therapy) at baseline and after the first cycle of therapy. A major publication using this dataset is accessible here: (reference)

*experimental design

Single-cell RNA sequencing was performed using 10x Genomics with feature barcoding technology to multiplex cell samples from different patients undergoing mono or dual therapy so that they can be loaded on one well to reduce costs and minimize technical variability. Hashtag oligomers (oligos) were obtained as purified and already oligo-conjugated in TotalSeq-C format from BioLegend. Cells were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.

*extract protocol

PBMCs were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions.

*library construction protocol

Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.

*library strategy

scRNA-seq and scTCR-seq

*data processing step

Pre-processing of sequencing results to generate count matrices (gene expression and HTO barcode counts) was performed using the 10x genomics Cell Ranger pipeline.

Further processing was done with Seurat (cell and gene filtering, hashtag identification, clustering, differential gene expression analysis based on gene expression).

*genome build/assembly

Alignment was performed using prebuilt Cell Ranger human reference GRCh38.

*processed data files format and content

RNA counts and HTO counts are in sparse matrix format and TCR clonotypes are in csv format.

Datasets were merged and analyzed by Seurat and the analyzed objects are in rds format.