DeSeq2
DESeq2 is an R/Bioconductor package for differential analysis of high-throughput sequencing count data. It is widely used for RNA-seq differential expression analysis and can also be applied to other sequencing-based assays that produce count data, including ChIP-seq, ribosome profiling, CLIP, metagenomics, and HT-CRISPR screens. DESeq2 models count data using the negative binomial, also known as Gamma-Poisson, distribution and provides robust methods for normalization, dispersion estimation, statistical testing, and result interpretation.
Key benefits
Established R/Bioconductor package for sequencing count data analysis
Robust differential expression and differential abundance testing
Suitable for RNA-seq and other count-based high-throughput assays
Includes normalization, dispersion estimation, statistical testing, and shrinkage methods
Integrates well into reproducible R and Bioconductor workflows
Applications
Differential expression analysis of RNA-seq data
Differential analysis of ChIP-seq, CLIP, and ribosome profiling count data
Differential abundance analysis in metagenomics workflows
Analysis of HT-CRISPR screen count data
Statistical comparison of sequencing-based experiments across conditions
Intended use
DESeq2 is intended for bioinformaticians, transcriptomics researchers, genomics researchers, and life scientists working with sequencing-based count data. It is particularly suited for users who need statistically robust differential analysis within the R/Bioconductor ecosystem.