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.

For methodological details, see S1 Text, paragraph "RNA-Seq Analysis". (XLSX)

The data provided here are part of a Galaxy Training Network tutorial that analyzes RNA-Seq data from a study published by Brooks et al. 2011 to identify genes and exons that are regulated by Pasilla gene.

RNA sequencing (RNA-Seq) is a high-throughput sequencing approach that enables comprehensive quantification of transcriptomes at a genome-wide scale. As a result, RNA-Seq has become a routine component of molecular biology research, and more researchers are now expected to analyze RNA-Seq data as part of their projects. However, unlike the largely experimental nature of benchwork, RNA-Seq analysis demands proficiency with computational and statistical approaches to manage technical issues and large data sizes. Although numerous manuals and reviews on RNA-Seq data analysis are available, many are either highly specialized, fragmented, or overly superficial, leaving beginners to use tools without understanding the underlying principles. To address this gap, we provide a decision-oriented guide tailored for molecular biologists encountering RNA-Seq analysis for the first time. This review is designed for readers to enable to decide which tools and statistical approaches to use based on their data, goals, and constraints. We aim to equip beginners with the knowledge required to perform RNA-Seq analysis rigorously and with confidence.

Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev (Scanpy is not always reliable for cell cycle analysis ).

https://scanpy.readthedocs.io/en/stable/

Scanpy – Single-Cell Analysis in Python

Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.

Single cell RNA sequencing data - count matrices: rows - correspond to cells, columns to genes, value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

SCANPY is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with SCANPY, we present ANNDATA, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).

Paper:

Wolf, F., Angerer, P. & Theis, F. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19, 15 (2018). https://doi.org/10.1186/s13059-017-1382-0 https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1382-0

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6 Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

The peripheral blood immune cell (PBMC) samples were collected from patients infected with dengue virus (DENV) at four time points: two and one day(s) before defervescence (febrile phase), at defervescence (critical phase), and two-week convalescence. The raw and filtered matrix files were generated using CellRanger version 3.0.2 (10x Genomics, USA) with the reference human genome GRCh38 1.2.0. Potential contamination of ambient RNAs was corrected using SoupX. Low quality cells, including cells expressing mitochondrial genes higher than 10% and doublets/multiplets, were excluded using Seurat and doubletFinder, respectively. The individual samples were then integrated using the SCTransform method with 3,000 gene features. Principal component analysis (PCA) and clustering were performed with the Louvain algorithm applying multi-level refinement algorithm. The gene expression level of each cell was normalized using the LogNormalize method in Seurat. Cell types were annotated using the canonical marker genes described in the original paper, see related link below.

This dataset contains a curated collection of RNA sequences, a fundamental component of biological data analysis. RNA (Ribonucleic Acid) is a vital molecule involved in various biological processes, including protein synthesis and gene regulation. Researchers, bioinformaticians, and data scientists can utilize this dataset for tasks such as gene expression analysis, functional genomics, and evolutionary studies.

RNA-seq gene count datasets built using the raw data from 18 different studies. The raw sequencing data (.fastq files) were processed with Myrna to obtain tables of counts for each gene. For ease of statistical analysis, they combined each count table with sample phenotype data to form an R object of class ExpressionSet. The count tables, ExpressionSets, and phenotype tables are ready to use and freely available. By taking care of several preprocessing steps and combining many datasets into one easily-accessible website, we make finding and analyzing RNA-seq data considerably more straightforward.

The aim of this work is to determine whether mycobacteria have enhanced virulence during space travel and what mechanisms they use to adapt to microgravity. M. marinum and LHM4 were grown in high aspect ratio vessels (HARV) in a rotary cell culture system (RCCS) under normal gravity (NG) or low shear simulated microgravity (MG). To determine the effect of MG on the stress responses activated by the growth conditions, we used RNAseq to examine what genes were expressed. For RNAseq, the bacteria are harvested, RNA isolated and converted DNA (cDNA), and the cDNA sequenced. Using bioinformatics, the amount of expression of the different M. marinum genes were compared between the NG and MG samples. To make sure that we were examining only gene expression changes due to MG, only bacteria in early exponential growth were used in the RNAseq studies. Triplicate NG and MG cultures were used to generate samples of bacteria grown for ~40 hrs. We also grew triplicate cultures for 4 days and then diluted them again and grew them for another ~40 hrs so we could examine gene expression from bacteria exposed for a longer time. In summary, this study determined that waterborne mycobacteria alter their growth, expression of stress responses, and their sensitivity to oxidizing conditions when subjected to growth under MG.

Summary of small RNA sequencing data analysis.

Data analysis of RNA-Seq FASTQ files for RCC4-EV cells (DRR100656) and RCC4-VHL cells (DRR100657) were obtained from the Sequence Read Archive (https://trace.ddbj.nig.ac.jp/dra/index_e.html). The quality of sequence data was evaluated by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) after the trimming process by fastx_toolkit v 0.0.14 (http://hannonlab.cshl.edu/fastx_toolkit/). The human reference sequence file (hs37d5.fa) was downloaded from the 1000 genome ftp site (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/), and the annotated general feature format (gff) file was downloaded from the Illumina iGenome ftp site (ftp://igenome:G3nom3s4u@ussd-ftp.illumina.com/Homo_sapiens/NCBI/build37.2/). The human genome index was constructed with bowtie-build in Bowtie v.2.2.9. The fastq files were aligned to the reference genomic sequence by TopHat v.2.1.1 with default parameters. Bowtie2 v2.2.9 and Samtools v.1.3.1 was used with the TopHat program47. Estimation of transcript abundance was calculated, and the count values were normalized to the upper quartile of the fragments per kilobase of transcript per million fragments mapped reads (FPKM) using Cufflinks (cuffdiff) v2.1.1. cuffdiff output (gene_exp. diff) was presentated (gene_exp.diff.txt).

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)

BackgroundÂ

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

Results

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

Analysis of four samples of GEO accession GSE119855 with the IBU RNA-seq pipeline

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.
Request Free Sample

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

Data Set Description

These data are collected from a total of 70 participants (47 adult; 23 pediatric), all of which had relapsed or primary resistant acute myeloid leukemia. The data, which here are separated into an adult and a pediatric dataset, were generated as part of a study by Stratmann et. al. (https://doi.org/10.1182/bloodadvances.2021004962). The Stratmann et. al. study is currently pre-published here: https://ashpublications.org/bloodadvances/article/doi/10.1182/bloodadvances.2021004962/477210/Transcriptomic-analysis-reveals-pro-inflammatory Please note that separate applications are necessary for the adult and pediatric dataset, respectively. When applying for access, please indicate which of the datasets that the application applies for. The adult dataset contains transcriptome sequencing (RNA-seq) data from 25 diagnosis (D), 45 relapse (R1/R2/R3) and five (5) primary resistant (PR) leukemic samples from 47 patients, as well as five (5) normal CD34+ bone marrow control samples. The pediatric dataset contains RNA-seq data from 18 diagnosis (D), 22 relapse (R1/R2), six (6) persistent relapse (R1/2-P) and one (1) primary resistant (PR) leukemic samples from 23 patients, as well as five (5) normal CD34+ bone marrow control samples. The leukemic samples originate from bone marrow or peripheral blood. The normal RNA samples originate from purified CD34+ bone marrow cells from five different healthy individuals. Further details regarding the samples are available in the Supplemental Information part of Stratmann et. al. (https://doi.org/10.1182/bloodadvances.2021004962). RNA-seq libraries and associated next-generation sequencing were carried out by the SNP&SEQ Technology platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. Libraries were prepared using the TruSeq stranded total RNA library preparation kit with ribosomal depletion by RiboZero Gold (Illumina). Sequencing of adult samples was carried out on the Illumina HiSeq2500 platform, generating paired-end 125bp reads using v4 sequencing chemistry. Sequencing of pediatric samples was carried out on the Illumina NovaSeq6000 platform (S2 flowcell), generating paired-end 100bp reads using the v1 sequencing chemistry. The CD34+ bone marrow control samples were sequenced using both platforms (Illumina HiSeq2500 and NovaSeq6000). Further, all of these acute myeloid leukemia samples have also been characterized by whole genome sequencing or whole exome sequencing, with the datasets available under controlled access through doi.org/10.17044/scilifelab.12292778. Terms for accessThe adult and pediatric datasets are only to be used for research that is seeking to advance the understanding of the influence of genetic and transcriptomic factors on human acute myeloid leukemia etiology and biology. Use of the protected pediatric dataset is only for research projects that can merely be conducted using pediatric acute myeloid leukemia data, and for which the research objectives cannot be accomplished using data from adults. Applications intending various method development would thus not be considered as acceptable for use of the pediatric dataset. Further, the pediatric dataset may not be used for research investigating predisposition for acute myeloid leukemia based on germline variants.

For conditional access to the adult and/or pediatric dataset in this publication, please contact datacentre@scilifelab.se

Normalization is an essential step with considerable impact on high-throughput RNA sequencing (RNA-seq) data analysis. Although there are numerous methods for read count normalization, it remains a challenge to choose an optimal method due to multiple factors contributing to read count variability that affects the overall sensitivity and specificity. In order to properly determine the most appropriate normalization methods, it is critical to compare the performance and shortcomings of a representative set of normalization routines based on different dataset characteristics. Therefore, we set out to evaluate the performance of the commonly used methods (DESeq, TMM-edgeR, FPKM-CuffDiff, TC, Med UQ and FQ) and two new methods we propose: Med-pgQ2 and UQ-pgQ2 (per-gene normalization after per-sample median or upper-quartile global scaling). Our per-gene normalization approach allows for comparisons between conditions based on similar count levels. Using the benchmark Microarray Quality Control Project (MAQC) and simulated datasets, we performed differential gene expression analysis to evaluate these methods. When evaluating MAQC2 with two replicates, we observed that Med-pgQ2 and UQ-pgQ2 achieved a slightly higher area under the Receiver Operating Characteristic Curve (AUC), a specificity rate > 85%, the detection power > 92% and an actual false discovery rate (FDR) under 0.06 given the nominal FDR (≤0.05). Although the top commonly used methods (DESeq and TMM-edgeR) yield a higher power (>93%) for MAQC2 data, they trade off with a reduced specificity (

The global RNA Analysis and Transcriptomics market size was valued at USD 12.38 billion in 2021 and is projected to reach USD 29.73 billion by 2028, exhibiting a CAGR of 12.5% during the forecast period (2023-2028). The market growth is primarily attributed to the increasing prevalence of chronic diseases, the growing demand for precision medicine, and the technological advancements in RNA sequencing techniques. The key market drivers include the rising adoption of RNA sequencing in clinical research, the increasing demand for personalized medicine, and the growing number of government initiatives to support RNA analysis research. Challenges such as the high cost of RNA sequencing, the complexity of data analysis, and regulatory hurdles could hinder the market growth. Key trends in the market include the development of single-cell RNA sequencing technologies, the integration of RNA analysis with artificial intelligence (AI), and the increasing application of RNA analysis in precision medicine. North America dominated the market in 2021 and is expected to maintain its dominance throughout the forecast period. The Asia-Pacific region is expected to grow at the highest CAGR during the forecast period due to the rising prevalence of chronic diseases, the growing demand for personalized medicine, and the increasing government initiatives to support RNA analysis research.

Space environment is suspected to generate reactive oxygen species (ROS) and induce oxidative stress in plants, however, little is known about the gene expression of ROS gene network in plants grown in long-term space flight. RNA-Seq was used to define the large-scale gene expression profiles of Mizuna harvested after 27 days cultivation in the international space station to understand the molecular response and adaptation to space environment.Results: Total reads of transcripts from the Mizuna grown in the international space station as well as on the ground by RNA-Seq using next generation sequencing technology showed 8,258 and 14,170 transcripts up- and down-regulated in the space-grown Mizuna, respectively, when compared with those from the ground-grown Mizuna. A total of 20 in 32 ROS oxidative marker genes were up-regulated, including high expression of 4 hallmarks, and preferentially expressed gene associated with ROS-scavenging genes was thioredoxin, glutaredoxin, and alternative oxidase genes. In the transcription factors of ROS gene network , MEKK1-MKK4-MPK3, OXI1-MKK4-MPK3, and OXI1-MPK3 of MAP cascades, induction of WRKY22 by MEKK1-MKK4-MPK3 cascade, induction of WRKY25 and repression of ZAT7 by Zat12 were suggested. RbohD and RbohF genes were up-regulated preferentially in NADPH oxidase genes, which produce ROS.Conclusions: Our large-scale transcriptome analysis demonstrated that the space environment induced oxidative stress and ROS gene network was activated in the space-grown Mizuna, some of which were common genes up-regulated by abiotic and biotic stress and were preferentially up-regulated genes by the space environment, even though Mizuna grew in the space as well as on the ground, showing that plants could acclimate to the space environment by reprograming the expression of ROS gene network.

Long non-coding RNAs (lncRNAs) are an emerging class of RNA species that may play a critical regulatory role in gene expression. However, the association between lncRNAs and atrial fibrillation (AF) is still not fully understood. In this study, we used RNA sequencing data to identify and quantify the both protein coding genes (PCGs) and lncRNAs. The high enrichment of these up-regulated genes in biological functions concerning response to virus and inflammatory response suggested that chronic viral infection may lead to activated inflammatory pathways, thereby alter the electrophysiology, structure, and autonomic remodeling of the atria. In contrast, the downregulated GO terms were related to the response to saccharides. To identify key lncRNAs involved in AF, we predicted lncRNAs regulating expression of the adjacent PCGs, and characterized biological function of the dysregulated lncRNAs. We found that two lncRNAs, ETF1P2, and AP001053.11, could interact with protein-coding genes (PCGs), which were implicated in AF. In conclusion, we identified key PCGs and lncRNAs, which may be implicated in AF, which not only improves our understanding of the roles of lncRNAs in AF, but also provides potentially functional lncRNAs for AF researchers.