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.

Study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. Comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.

Following tools are useful for mis sense muation detection ... 

PolyPhen‐2 (PP2)
“Predicts possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations”

MutationTaster‐2 (MT2)
“Evaluation of the disease‐causing potential of DNA sequence alterations”

MutationAssessor (MASS)
“Predicts the functional impact of amino acid substitutions in proteins, such as mutations discovered in cancer or missense polymorphisms”

LRT
“Identify a subset of deleterious mutations that disrupt highly conserved amino acids within protein‐coding sequences, which are likely to be unconditionally deleterious”

SIFT
“Predicts whether an amino acid substitution affects protein function”

GERP++
“Identifies constrained elements in multiple alignments by quantifying substitution deficits. These deficits represent substitutions that would have occurred if the element were neutral DNA, but did not occur because the element has been under functional constraint. We refer to these deficits as “rejected substitutions.” Rejected substitutions are a natural measure of constraint that reflects the strength of past purifying selection on the element”

phyloP
“Compute conservation or acceleration P values based on an alignment and a model of neutral evolution”

FatHMM unweighted (FatHMM‐U)
Predicts “functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants”

FatHMM weighted (FatHMM‐W)
Predicts “functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants” and its weighting scheme attributes higher tolerance scores to SNVs in proteins, related proteins, or domains that already include a high fraction of pathogenic variantsh

Combined Annotation Dependent Depletion (CADD)
“CADD is a tool for scoring the deleteriousness of single‐nucleotide variants as well as insertion/deletions variants in the human genome”

Address of the bookmark: http://www.nature.com/nature/journal/v517/n7536/full/nature13907.html

Address of the bookmark: https://github.com/walaj/svaba

Address of the bookmark: https://github.com/mkirsche/Jasmine

Address of the bookmark: http://bioinformatics.ua.pt/software/smash/

Access Spines here.

Address of the bookmark: https://www.broadinstitute.org/genome-sequencing-and-analysis/spines

More at file:///home/urbe/Downloads/diamond_manual.pdf

http://www.nature.com/nmeth/journal/v12/n1/full/nmeth.3176.html

Address of the bookmark: https://github.com/bbuchfink/diamond

Distribution package for the Prgressive Cactus multiple genome aligner. Dependencies are linked as submodules

https://github.com/glennhickey/progressiveCactus

Address of the bookmark: https://github.com/glennhickey/progressiveCactus