https://www.mdpi.com/2073-4425/11/7/756/htm

https://shadowcaster.readthedocs.io/en/latest/

https://github.com/dani2s/ShadowCaster_testData

Address of the bookmark: https://github.com/dani2s/ShadowCaster

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-6568-2

Address of the bookmark: https://github.com/HuffordLab/GenomeQC

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: http://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html

http://www.extension.org/pages/67926/upcoming-webinar:-selective-sequencing-through-combinatorial-pooling#.UggsVuHyPqU

Appointment Adv.No.2

Applications in plain paper are invited from Indian citizens for one/two position each of Research Associate, Traineeship and Studentship for BIF facility, Division of Life Sciences, IASST.

Applications with complete Bio-data containing contact address, e-mail and phone number, two recent passport size photographs and attested copies of mark sheets, certificates etc., should be sent to the Registrar, IASST, Paschim Boragaon, Garchuk, Guwahati – 781035, Assam, so as to reach on or before 5/05/2014.

A. Research Associate:

Number of vacancies: 1 (One)

Qualifications:

PhD in Bioinformatics or allied disciplines with knowledge of Bioinformatics. The candidates who have submitted PhD thesis may also apply.

In case, candidates having PhD are not found, candidates having MSc in Bioinformatics or allied disciplines with sound knowledge of Bioinformatics will be preferred.

Remuneration: Candidate having PhD will get a consolidated remuneration of Rs. 22,000/- +HRA per month. MSc having NET/GATE/SLET qualified candidate will get a remuneration of Rs. 16,000/= and HRA and candidate with only MSc will get a remuneration of Rs.14,000/- and HRA.

Tenure:

The post is initially for one year and may be extended depending on the performance till the tenure of the project.

B. Traineeship:

Number of vacancies: 2 (Two)

Qualifications:

Candidate with a postgraduate degree in Bioinformatics/Biotechnology/Life sciences from a recognised University

Remuneration: Rs. 5000/month for 6 months

C. Studentship:

Number of vacancies: 2 (Two)

Qualifications:

Candidate pursuing M.Sc in bioinformatics in a recognised University.

Remuneration: Rs. 5000/month for 6 months

Advertisement:

http://iasst.gov.in/pdf/recruitment/advt%20no_2_24042014.pdf

http://katjulab.com/research.html

If you would like to use GeneValidator on a few sequences, see our online GeneValidator Web App -http://genevalidator.sbcs.qmul.ac.uk.

If you use GeneValidator in your work, please cite us as follows:

Dragan M‡, Moghul MI‡, Priyam A, Bustos C & Wurm Y. 2016. GeneValidator: identify problems with protein-coding gene predictions. Bioinformatics, doi: 10.1093/bioinformatics/btw015.

 

Address of the bookmark: https://github.com/wurmlab/genevalidator

Address of the bookmark: http://pbil.univ-lyon1.fr/software/DeCoSTAR/