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

The spreadsheets include pairwise comparisons of gene expression levels under specified conditions. Abbreviations used are as follows: PV for Parvalbumin; Sst for Somatostatin; CFC for Contextual Fear Conditioning; PTZ for Pentylenetetrazol; WT for wild type; cKO for conditional Nr4a1 knockouts; and HC for home cage.

Summary of RNA-seq differential expression analysis.

This dataset contains RNA-Seq data preprocessing and differential gene expression (DGE) analysis.

It is designed for researchers, bioinformaticians, and students interested in transcriptomics.

The dataset includes raw count data and step-by-step preprocessing instructions.

It demonstrates quality control, normalization, and filtering of RNA-Seq data.

Differential expression analysis using popular tools and methods is included.

Results include differentially expressed genes with statistical significance.

It provides visualizations like PCA plots, heatmaps, and volcano plots.

The dataset is suitable for learning and reproducing RNA-Seq workflows.

Both human-readable explanations and code snippets are included for guidance.

It can serve as a reference for new RNA-Seq projects and research pipelines.

The data show circular Manhattan overview of genome-wide alternative splicing and gene expression changes regulated by each SF.

This dataset contains a Differential Gene Expression (DGE) analysis of GSE44076.

The analysis compares tumor versus normal samples.

It uses the DESeq2 package for RNA-seq count data analysis.

The dataset includes quality control (QC) visualizations.

Principal Component Analysis (PCA) plots are provided for sample clustering.

Heatmaps illustrate the expression patterns of top differentially expressed genes.

EnhancedVolcano plots are included to visualize significant genes.

The dataset enables users to explore gene expression changes in colorectal cancer.

All R scripts and associated visualizations are included for reproducibility.

The workflow can be adapted for other RNA-seq datasets.

The dataset supports bioinformatics, transcriptomics, and cancer research studies.

It provides an educational resource for DESeq2-based RNA-seq analysis.

The use of RNA-sequencing has garnered much attention in recent years for characterizing and understanding various biological systems. However, it remains a major challenge to gain insights from a large number of RNA-seq experiments collectively, due to the normalization problem. Normalization has been challenging due to an inherent circularity, requiring that RNA-seq data be normalized before any pattern of differential (or non-differential) expression can be ascertained; meanwhile, the prior knowledge of non-differential transcripts is crucial to the normalization process. Some methods have successfully overcome this problem by the assumption that most transcripts are not differentially expressed. However, when RNA-seq profiles become more abundant and heterogeneous, this assumption fails to hold, leading to erroneous normalization. We present a normalization procedure that does not rely on this assumption, nor prior knowledge about the reference transcripts. This algorithm is based on a graph constructed from intrinsic correlations among RNA-seq transcripts and seeks to identify a set of densely connected vertices as references. Application of this algorithm on our synthesized validation data showed that it could recover the reference transcripts with high precision, thus resulting in high-quality normalization. On a realistic data set from the ENCODE project, this algorithm gave good results and could finish in a reasonable time. These preliminary results imply that we may be able to break the long persisting circularity problem in RNA-seq normalization.

This dataset contains a comprehensive analysis of differential gene expression (DGE) data.

The data is processed and visualized using DESeq2, a widely used R package for RNA-seq analysis.

It includes normalized counts, statistical results, and visualization plots.

Provides insights into gene expression changes across different experimental conditions.

Facilitates downstream bioinformatics analysis and interpretation.

Includes ready-to-use scripts for performing DGE analysis and generating publication-quality plots.

Designed for researchers, bioinformaticians, and students working on transcriptomics.

Supports reproducible research practices with fully documented code.

The dataset is derived from GSE227516, a public RNA-seq dataset.

Suitable for learning, demonstration, and comparative analysis of gene expression workflows.

Lists of unique genes that are present in recovered similar transcripts and differentially expressed genes from libraries associated with one condition versus another condition along with significant GO terms recovered from M. albus and M. siculus when our algorithm is applied to A. thaliana and M. truncatula in the 25_3 assembly. (ZIP 101 kb)

• This dataset contains differential gene expression (DGE) analysis results derived from the GEO accession GSE166044. • The analysis was performed using the DESeq2 workflow for identifying significantly upregulated and downregulated genes. • Raw count data from the study were normalized and processed following standard RNA-seq best practices. • Quality assessment and exploratory analysis were conducted using Principal Component Analysis (PCA). • Gene expression clustering patterns were visualized using heatmaps generated from transformed count matrices. • Volcano plots were created to highlight differentially expressed genes based on fold change and statistical significance. • The dataset includes all scripts, result files, and visualization outputs generated through the R-based analysis pipeline. • This resource provides a complete reproducible workflow for understanding expression differences between experimental groups in GSE166044. • The provided R script automates normalization, variance stabilization, PCA plotting, heatmap creation, and volcano plot generation. • Researchers can use this dataset to reproduce the analysis, extend downstream biological interpretation, or use it as a reference for their own RNA-seq workflows.

*The sequencing depth was defined as the number of nt used for the analysis divided by the size of the genome.

Summary statistics of RNA-seq (quantification) library sequencing and mapping.

Heatmap of transcription levels of identified DEGs associated with NK cell cytotoxicity. Scatter plot displaying genes related to NK cell cytotoxicity. Scatter plot displaying genes related to signaling pathways.Heatmaps displaying FKPM values. Line graphs showing the transcriptional changes in genes related to activation, migration or tumor killing activity of NK cells.

Summary of the RNA-sequencing data.

Additional file 1: Figure S1. Quality control and batch effect correction in scRNA-Seq, related to Figure 1 A. Violin plots showing the number of expressed genes, the number of reads uniquely mapped against the reference genome, and the fraction of mitochondrial genes compared to all genes per cell in scRNA-Seq data. B. Box plot showing the number of genes (left) and the number of uniquely mapped reads (right) per cell in each identified cell type in scRNA-Seq data. C. tSNE plot visualization of the sample source for all 70,201 cells. Each dot is a cell. Different colors represent different samples. D. tSNE plot visualization of unsupervised clustering analysis for all 70,201 cells based on scRNA-Seq data after quality control, which gave rise to 31 distinct clusters. Figure S2. Gene Ontology (GO) analysis of the DEGs for each cell type was performed and the representative enriched GO terms are presented, related to Figure 1. Figure S3. Expression of selected marker genes along the differentiation trajectory, related to Figure 2 A. tSNE plot demonstrating cell cycle regression (left). Visualization of myogenic differentiation trajectory by cell cycle phases (G1, S, and G2/M) (right). B. Donut plots showing the percentages of cells in G1, S, and G2M phase at different cell states. C. Expression levels of cell cycle-related genes in the myogenic cells organized into the Monocle trajectory. D. Expression levels of muscle related genes in the myogenic cells organized into the Monocle trajectory. Figure S4. Unsupervised clustering analysis for all cells in scATAC-Seq data and myogenic-specific scATAC-seq peaks, related to Figure 4 A-C. tSNE plot visualization of the sample source for all 48514 cells in scATAC-Seq. Each dot is a cell. Different colors represent different pigs (A), different embryonic stages (B), or different samples (C). D. tSNE plot visualization of unsupervised clustering analysis for all 48514 cells after quality control in scATAC-Seq data, which gave rise to 15 distinct clusters. E. tSNE plot visualization of myogenic cells and other cells. Clusters 4 and 8 in Figure S4D were annotated as myogenic cells due to their high levels of accessibility of marker genes associated with myogenic lineage. F. Genome browser view of myogenic-specific peaks at the TSS of MyoG and Myf5 for myogenic cells and other cells in the scATAC-seq dataset. Figure S5. Percentage distribution of open chromatin elements in scATAC-Seq data, related to Figure 4 A. Distribution of open chromatin elements in each snATAC-seq sample. B. Distribution of open chromatin elements in snATAC-seq of myogenic cell types. C. Percentage distribution of open chromatin elements among DAPs in myogenic cell types. Figure S6. Integrative analysis of transcription factors and target genes, related to Figure 5 A. tSNE depiction of regulon activity (“on-blue”, “off-gray”), TF gene expression (red scale), and expression of predicted target genes (purple scale) of MyoG, FOSB, and TCF12. B. Corresponding chromatin accessibility in scATAC data for TFs and predicted target genes are depicted. Figure S7. Pseudotime-dependent chromatin accessibility and gene expression changes, related to Figure 7. The first column shows the dynamics of the 10× Genomics TF enrichment score. The second column shows the dynamics of TF gene expression values, and the third and fourth columns represent the dynamics of the SCENIC-reported target gene expression values of corresponding TFs, respectively. Figure S8. Myogenesis related gene expression in DMD (Duchenne muscular dystrophy) mice. Comparison of RNA-seq data of flexor digitorum short (FDB), extensor digitorum long (EDL), and soleus (SOL) in DMD and wild-type mice including 2- month and 5-month age. A. The expression levels of myogenesis related genes (Myod1, Myog, Myf5, Pax7). B. The expression levels of related genes that were upregulated during porcine embryonic myogenesis (EGR1, RHOB, KLF4, SOX8, NGFR, MAX, RBFOX2, ANXA6, HES6, RASSF4, PLS3, SPG21). C. The expression levels of related genes that were downregulated during porcine embryonic myogenesis COX5A, HOMER2, BNIP3, CNCS). Data were obtained from the GEO database (GSE162455; WT, n = 4; DMD, n = 7). Figure S9. Genome browser view of differentially accessible peaks at the TSS of EGR1 and RHOB between myogenic cells in the scATAC-seq dataset, related to Figure 8. Figure S10. Functional analysis of EGR1 in myogenesis, related to Figure 8 A-B. EdU assays for the proliferation of pig primary myogenic cells (A) and C2C12 myoblasts following EGR1 overexpression. C. qPCR analysis of the mRNA levels of cell cycle regulators in C2C12 cells following EGR1 overexpression. D. Immunofluorescence staining for MyHC in C2C12 cells following EGR1 overexpression and differentiation for 3 d. Then, the fusion index was calculated. Figure S11. Functional analysis of RHOB in myogenesis, related to Figure 8 A-B. EdU assays for proliferation of pig primary myogenic cells (A) and C2C12 myoblasts following RHOB overexpression. C. qPCR analysis of the mRNA levels of cell-cycle regulators in C2C12 cells following RHOB overexpression. D. Immunofluorescence staining for MyHC in C2C12 cells following RHOB overexpression and differentiation for 3 d. Then, the fusion index was calculated.

Summary of RNA sequencing results.

Supplemental mapping data, PCA plots, and differentially expressed genes for RNAseq data presented in the manuscript titled "Transcriptomic Responses are Sex-Dependent in the Skeletal Muscle and Liver in Offspring of Obese Mice"

1Total number and percentage (in parenthesis) of reads that mapped to the reference genome with 100% accuracy.2Total number and percentage (in parenthesis) of reads that mapped to a single location within the reference genome [11], [12].3Based on the total of uniquely mapped reads for the corresponding sample.

Summary of RNA-seq data mapped to the reference genome.

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...