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.

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.

The size of the RNA-Seq market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

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 data in this item includes raw RNA-sequencing data from post-43°C exposure and during the recovery period to assess transcript-level effect.Sample preparation: Overall, 18 samples included 3 replicates of Pseudomonas aeruginosa after exposure to 37 °C (control) or 43 °C (heat shock), at 3 time points (T=0hr, T=18hr, and T=54hr). Samples from the T=0 groups are simply labeled “37” or “43”. For RNA sequencing, 2 ug of total RNA was used for the RiboMinus™ Bacteria Transcriptome Isolation Kit (Invitrogen). The library was constructed with NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina (NEB) according to the manufacturer's instructions using 30 ng of depleted RNA. The final quality was evaluated by TapeStation High Sensitivity D1000 Assay (Agilent Technologies, CA, USA). Sequencing was performed based on Qubit values and loaded onto an Illumina MiSeq using the MiSeq V2 (50- cycles) Kit (Illumina, CA, USA). Paired-end RNA-seq protocol was used, yielding about 3.4-6.5 million paired-end reads per sample. FastQC (v0.11.2) was used to assess the quality of raw reads.Analysis: Reads were aligned to Pseudomonas aeruginosa PAO1 strain (assembly GCF_000006765.1 ) using the bowtie2 aligner software (v2.3.2) with default parameters. GTF annotation file for the PAO1 strain was downloaded from NCBIPseudomonas Genome DB ( www.pseudomonas.com). Raw read counts for gene-level features were determined using HTSeq-count with the intersection-strict mode. Differentially expressed genes were determined with the R Bioconductor package DESeq2 (Release 3.14). The p-values were corrected with the Benjamini-Hochberg FDR procedure. Genes with adjusted p-values; 0.05.

Dataset Description

This dataset contains RNA-seq data from human cells. The data was collected using the Illumina HiSeq 2500 platform. The data includes raw sequencing reads, gene annotations, and phenotypic data for the samples.

Files and Folders

Files can be downloaded using the following command:

wget ftp://ftp.ccb.jhu.edu/pub/RNAseq_protocol/chrX_data.tar.gz

Once the file has been downloaded, it can be extracted using the following command:

tar xvzf chrX_data.tar.gz

This will create a directory called chrX_data containing the following files:

genes/chrX.gtf
genome/chrX.fa
geuvadis_phenodata.csv
indexes/
mergelist.txt
samples/

Here are some additional details about the files in the chrX_data directory:

• genes/chrX.gtf - This file contains gene annotations for the human X chromosome. It is in the GTF format, which is a standard format for gene annotations. The GTF file contains information about the start and end positions of genes, as well as their transcripts.
• genome/chrX.fa - This file contains the reference genome sequence for the human X chromosome. It is in the FASTA format, which is a standard format for storing DNA sequences.
• geuvadis_phenodata.csv - This file contains phenotypic data for the samples in the dataset. The phenotypic data includes information such as the age, sex, and disease status of the samples.
• indexes/ - This directory contains index files for HISAT2. Index files are used to speed up the alignment of sequencing reads to a reference genome.
• mergelist.txt - This file lists the samples to be merged. The samples in the samples/ directory can be merged using a variety of tools, such as BEDTools and STAR.
• samples/ - This directory contains the raw sequencing data. The raw sequencing data is in the FASTQ format, which is a standard format for storing sequencing reads.

Usage

This dataset can be used to perform RNA-seq analysis using a variety of tools, such as HISAT2, StringTie, and Ballgown.

Here are some examples of how this dataset can be used:

• To identify differentially expressed genes between two groups of samples.
• To build a gene expression atlas for a particular tissue or cell type.
• To study the expression of genes involved in a particular disease.

source: ftp://ftp.ccb.jhu.edu/pub/RNAseq_protocol/chrX_data.tar.gz

The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation. High-throughput transcriptome sequencing (RNA-Seq) has become the main option for these studies. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq data also increased rapidly. However, there is no consensus about the most appropriate pipeline or protocol for identifying differentially expressed genes from RNA-Seq data. This work presents an extended review on the topic that includes the evaluation of six methods of mapping reads, including pseudo-alignment and quasi-mapping and nine methods of differential expression analysis from RNA-Seq data. The adopted methods were evaluated based on real RNA-Seq data, using qRT-PCR data as reference (gold-standard). As part of the results, we developed a software that performs all the analysis presented in this work, which is freely available at https://github.com/costasilvati/consexpression. The results indicated that mapping methods have minimal impact on the final DEGs analysis, considering that adopted data have an annotated reference genome. Regarding the adopted experimental model, the DEGs identification methods that have more consistent results were the limma+voom, NOIseq and DESeq2. Additionally, the consensus among five DEGs identification methods guarantees a list of DEGs with great accuracy, indicating that the combination of different methods can produce more suitable results. The consensus option is also included for use in the available software.

DDX3XRNA helicase affects breast cancer cell cycle progression by regulating expression of KLF4 Ester Cannizzaro1, Andrew John Bannister1, Namshik Han, Andrej Alendar and Tony Kouzarides*. Gurdon Institute and Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK 1Equal contribution*Corresponding Author: t.kouzarides@gurdon.cam.ac.uk AbstractDDX3X is a multifunctional RNA helicase with documented roles in different cancer types. Here, we demonstrate that DDX3X plays an oncogenic role in breast cancer cells by modulating the cell cycle. Depletion of DDX3X in MCF7 cells slows cell proliferation by inducing a G1 phase arrest. Notably, DDX3X inhibits expression of KLF4, a transcription factor and cell cycle repressor. Moreover, DDX3X directly interacts with KLF4mRNA and regulates its splicing. We show that DDX3X-mediated repression of KLF4 promotes expression of S-phase inducing genes in MCF7 breast cancer cells. These findings provide evidence for a novel function of DDX3X in regulating expression and downstream functions of KLF4, a master negative regulator of the cell cycle.RNAseq analysis upon DDX3X knockdown in MCF7 breast cancer cells.Total RNA was extracted (as described above) from MCF7 cells transfected with either a scrambled siRNA or DDX3X targeting siRNA (#6 or #8) and harvested 72 h after transfection. Three independent biological replicates were produced for each condition. Ribosomal RNA was depleted using a Ribo-Zero rRNA removal kit (Human/Mouse/Rat) from Illumina(R), following the manufacturer’s instructions. RNA-seq libraries were produced using NEXTfex RNA-Seq kit from Bio Scientifc, following the manufacturer’s instructions. Before multiplexing, excess primer was removed with AMPure XP beads (Beckman Coulter). Before and after multiplexing, libraries were tested for both size and quantity of DNA using a Qubit dsDNA HS assay kit and a high sensitivity D1000 ScreenTape system following the manufacturer’s instructions. For differential gene expression analysis in DDX3X knockdown MCF7 cells, trimmed reads were mapped in paired end mode to the h38 human genome using tophat with the following parameters (--no-coverage- search --max-multihits 300 --report-secondary-alignments --read- mismatches 2 --library-type fr-frststrand). Multihits (reads mapping to multi loci) were fltered, along with reads mapping with quality score less than 20. Reads were counted across gene models taken from the Ensembl v86 gtf gene model list using the summarizeOverlaps function from the GenomicAlignments package in R. The strand of each read was inverted prior to counting to account for the fact that libraries represent the frst strand of synthesised cDNA. Read counts were converted into normalised fragments per kilobase mapped (FPKM) values for quality control plots. Differential expression analysis was conducted on the raw count data using the DESeq2 package in R. P values were corrected for multiple testing using the Benjamini and Hochberg FDR correction. Signifcantly changing genes were identifed based on a fold change greater than 2-fold (up or down) and an adjusted p value less than 0.05. In addition, signifcant genes were fltered to remove genes where both the control and mutant samples had an average FPKM score less than 1.

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.

The RNA sequencing (RNA-Seq) market is experiencing robust growth, driven by the increasing adoption of next-generation sequencing (NGS) technologies in various life science applications. The market's expansion is fueled by several factors, including the rising prevalence of chronic diseases necessitating advanced diagnostic tools, the accelerating demand for personalized medicine approaches, and the growing investments in research and development within the pharmaceutical and biotechnology sectors. Technological advancements, such as improved sequencing accuracy and reduced costs, are further stimulating market growth. Furthermore, the expanding applications of RNA-Seq in oncology, infectious disease research, and agriculture are contributing to its significant market value. The competitive landscape is characterized by a mix of large established players and emerging innovative companies, leading to continuous product development and market diversification. While challenges remain, such as the complexity of data analysis and the need for skilled professionals, the overall outlook for the RNA-Seq market remains highly positive, with substantial growth potential in the coming years. Despite the positive trajectory, market penetration in developing nations remains limited due to factors such as high costs and infrastructure limitations. Furthermore, stringent regulatory approvals and the need for standardized data analysis protocols pose some hurdles. Nevertheless, the continuous innovation in sequencing technologies, coupled with declining costs, is likely to overcome these challenges. The increasing accessibility of bioinformatics tools and the emergence of cloud-based data analysis platforms are also expected to accelerate market growth. Segmentation by technology (e.g., Illumina, PacBio), application (e.g., oncology, transcriptomics), and end-user (e.g., research institutions, pharmaceutical companies) reveals specific opportunities and market niches. Focusing on these segment-specific needs will be crucial for market players aiming to capitalize on the market's future potential. The long-term forecast projects a sustained high growth rate, indicating RNA-Seq's pivotal role in advancing biomedical research and healthcare.

The root apex is an important section of the plant root involved in environmental sensing and cellular development. Analyzing the gene profile of root apex in diverse environments is important and challenging especially when the samples are limiting and precious such as in spaceflight. The feasibility of using tiny root sections for transcriptome analysis was examined in this study. To understand the gene expression profiles of the root apex Arabidopsis thaliana Col-0 roots were sectioned into Zone-I (0.5 mm root cap and meristematic zone) and Zone-II (1.5 mm transition elongation and growth terminating zone). Gene expression was analyzed using microarray and RNA seq. Both the techniques arrays and RNA-Seq identified 4180 common genes as differentially expressed (with > two-fold changes) between the zones. In addition 771 unique genes and 19 novel TARs were identified by RNA-Seq as differentially expressed which were not detected in the arrays. Single root tip zones can be used for full transcriptome analysis; further the root apex zones are functionally very distinct from each other. RNA-Seq provided novel information about the transcripts compared to the arrays. These data will help optimize transcriptome techniques for dealing with small rare samples.

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

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

Gravity regulates the magnitude and direction of a trans-cell calcium current in germinating spores of Ceratopteris richardii. Blocking this current with nifedipine blocks the spore's downward polarity alignment, a polarization that is fixed by gravity 10 h after light induces the spores to germinate. RNA-seq analysis at 10 h was used to identify genes potentially important for the gravity response. The data set will be valuable for other developmental and phylogenetic studies.

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)

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.

BioXpress is a gene expression and cancer association database in which the expression levels are mapped to genes using RNA-seq data obtained from The Cancer Genome Atlas, International Cancer Genome Consortium, Expression Atlas and publications. BioXpress can be searched by gene name or cancer type. To search the database by gene name, select the appropriate identifier type from the dropdown menu and type in the corresponding identifier in the adjacent text box. The results are computed and presented to the user with information such as variable expression levels and tumor expression. To search by cancer type, select the desired type from the dropdown menu, such as "Cancer Type", "Significant", "Expression", "Adjusted p-value" and "p-value". Results are shown in a graph displaying the top 10 differentially expressed genes for the specified cancer type in terms of the frequency of significant altered expression between the tumor and normal pairs.