Address of the bookmark: http://www.sthda.com/english/wiki/rna-seq-differential-expression-work-flow-using-deseq2

ashr - https://github.com/stephens999/ashr, https://cran.r-project.org/web/packages/ashr/index.html

consensusDE - https://bioconductor.org/packages/release/bioc/html/consensusDE.html

DESeq2 - https://bioconductor.org/packages/release/bioc/html/DESeq2.html

edgeR - https://bioconductor.org/packages/release/bioc/html/edgeR.html

limma - https://kasperdanielhansen.github.io/genbioconductor/html/limma.html  https://bioconductor.org/packages/release/bioc/html/limma.html

MetaCycle - https://cran.r-project.org/web/packages/MetaCycle/index.html, https://github.com/gangwug/MetaCycle

RUVSeq - https://bioconductor.org/packages/release/bioc/html/RUVSeq.html

SARTools - https://github.com/PF2-pasteur-fr/SARTools

tximport - https://github.com/mikelove/tximport

What is RNA-Seq?

RNA-Seq is a next-generation sequencing (NGS) technology used to study the transcriptome—the complete set of RNA molecules in a cell. It quantifies gene expression, detects novel transcripts, and captures alternative splicing events with high sensitivity and resolution.

Workflow for RNA-Seq Analysis

RNA-Seq analysis involves several stages, each requiring computational tools and expertise.

1. Experimental Design and Data Acquisition

Before diving into analysis, bioinformaticians should consider:

• Biological Replicates: Ensure statistical power to detect meaningful differences.
• Sequencing Depth: Align sequencing depth to study objectives (e.g., higher depth for low-abundance transcripts).
• Paired-End vs. Single-End: Paired-end sequencing provides more detailed information on transcript structure.

Once sequencing is complete, raw data is provided in FASTQ format, containing sequence reads and quality scores.

2. Quality Control and Preprocessing

Quality control (QC) ensures data integrity. Tools such as FastQC evaluate metrics like base quality, GC content, and adapter contamination.

Preprocessing Steps:

• Trimming: Tools like Trimmomatic or Cutadapt remove low-quality bases and adapter sequences.
• Filtering: Discard reads below a certain quality threshold or length.

3. Read Alignment

Reads are mapped to a reference genome or transcriptome to determine their origin. Alignment tools include:

• HISAT2: Handles large genomes efficiently and supports spliced alignments.
• STAR: High-speed aligner optimized for RNA-Seq.
• Bowtie2: Suitable for short-read alignment.

Output: A SAM/BAM file containing aligned reads.

4. Transcript Assembly and Quantification

This step involves identifying transcripts and quantifying their expression levels. Tools used include:

• StringTie: Assembles and quantifies transcripts from aligned reads.
• Salmon/Kallisto: Perform pseudo-alignment for rapid and accurate quantification.

Expression levels are typically measured as TPM (transcripts per million) or FPKM (fragments per kilobase of transcript per million mapped reads).

5. Differential Expression Analysis

To identify genes with altered expression between conditions, bioinformaticians use tools such as:

• DESeq2: Accounts for data normalization and variability.
• edgeR: Handles overdispersed count data efficiently.
• Limma-voom: Combines linear modeling with RNA-Seq count data.

The output includes a list of differentially expressed genes (DEGs) with statistical significance and fold-change values.

6. Functional Annotation and Pathway Analysis

Understanding the biological significance of DEGs involves:

• Gene Ontology (GO) Analysis: Tools like DAVID or clusterProfiler categorize genes based on their biological functions.
• Pathway Enrichment Analysis: Identifies pathways enriched in DEGs using tools like KEGG, Reactome, or GSEA.

7. Visualization

Visualizing results enhances interpretability. Common visualizations include:

• Heatmaps: Show expression patterns across samples (e.g., pheatmap).
• Volcano Plots: Highlight significant DEGs (e.g., ggplot2).
• PCA/UMAP: Assess sample clustering and variability (e.g., Seurat).

Challenges in RNA-Seq Analysis

1. Batch Effects: Technical variability can confound biological signals. Combat this with normalization techniques or batch-correction tools like ComBat.
2. Low-Quality Samples: Poor-quality RNA impacts downstream analyses.
3. Computational Complexity: RNA-Seq generates massive datasets, requiring robust computing resources and optimized pipelines.

Key Tools and Resources

• Bioconductor: A treasure trove of R packages for RNA-Seq analysis.
• Galaxy: A web-based platform for running RNA-Seq workflows.
• Nextflow/Snakemake: Workflow management tools to streamline analyses.

Applications of RNA-Seq

RNA-Seq is used in diverse research areas, including:

• Cancer Transcriptomics: Identifying tumor-specific expression profiles.
• Developmental Biology: Studying dynamic transcriptome changes.
• Drug Discovery: Screening genes modulated by therapeutic compounds.

Conclusion

RNA-Seq analysis is a cornerstone of modern transcriptomics, offering bioinformaticians a versatile toolkit for unraveling gene expression and regulation. Mastering RNA-Seq workflows and tools empowers researchers to transform raw sequencing data into biological discoveries.

Whether you’re investigating disease mechanisms, exploring cellular pathways, or developing new therapeutics, RNA-Seq is a powerful ally in your bioinformatics arsenal.

More at https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4

Address of the bookmark: https://github.com/aghozlane/shaman

BioJupies now supports user accounts! Sign in from the top right corner of the page for access to unlimited private notebooks, RNA-seq datasets and alignment jobs.

Address of the bookmark: https://amp.pharm.mssm.edu/biojupies/

https://bio7.org/about/

Address of the bookmark: https://bio7.org/home-2/

What Does DESeq2 Do?
DESeq2 analyzes count data—the number of sequencing reads that map to each gene. It:

Normalizes the data to account for sequencing depth and library size.

Estimates variance (dispersion) for each gene.

Fits a model to compare groups (e.g., control vs treated).

Calculates fold-changes and p-values to determine significance.

Installing DESeq2

You can install DESeq2 via Bioconductor in R:

if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DESeq2")

Inputs Needed

A count matrix: genes as rows, samples as columns (raw counts, not normalized).

A sample metadata table (also called colData): defines the condition/group for each sample.

Example:
# Count matrix (rows = genes, columns = samples)
counts <- read.csv("counts.csv", row.names = 1)

# Sample metadata
colData <- data.frame(
row.names = colnames(counts),
condition = c("control", "control", "treated", "treated")
)

DESeq2 Workflow

1. Load the package
library(DESeq2)
2. Create a DESeqDataSet object
dds <- DESeqDataSetFromMatrix(countData = counts,
colData = colData,
design = ~ condition)
3. Run the differential expression analysis
dds <- DESeq(dds)
4. Get the results
res <- results(dds)
head(res)
This gives a table with:

log2FoldChange: how much expression changed

pvalue: statistical significance

padj: adjusted p-value (FDR corrected)

Visualization (Optional but Powerful)

MA Plot
plotMA(res, ylim = c(-2, 2))

Volcano Plot (custom)
library(ggplot2)
res$significant <- res$padj < 0.05
ggplot(res, aes(x=log2FoldChange, y=-log10(padj), color=significant)) +
geom_point() +
theme_minimal()

Heatmap of Top Genes
library(pheatmap)
topgenes <- head(order(res$padj), 20)
vsd <- vst(dds, blind=FALSE)
pheatmap(assay(vsd)[topgenes, ])

Tips for Best Results
Use raw counts (not normalized or TPM/RPKM values).

Have replicates: DESeq2 relies on variance estimates, so at least 3 per group is ideal.

Watch out for batch effects—include them in your design if needed (e.g., ~ batch + condition).

Summary

Step Purpose
DESeqDataSetFromMatrix() Load your data into DESeq2
DESeq() Run the differential expression analysis
results() Extract the output (log fold change, p-values, etc.)
plotMA() / ggplot2 / pheatmap Visualize the results

Final Thoughts
DESeq2 is an essential tool for RNA-seq data analysis. It abstracts away much of the complexity of statistical modeling, while still giving you control when needed. Whether you're a bioinformatician or a wet-lab biologist, DESeq2 offers both ease of use and analytical power.

Address of the bookmark: https://bioinfo.noble.org/GWASPRO/

More at https://pubmed.ncbi.nlm.nih.gov/20494974/

Address of the bookmark: http://www.sequenceontology.org/cgi-bin/soba.cgi