DEGseq

DEGseq implements statistical methods to identify differentially expressed genes and isoforms from high-throughput RNA-seq data for quantitative transcriptome profiling.

Key Features:

• R package: Provides an implementation distributed as an R package for RNA-seq differential expression analysis.
• Integration of existing methods: Incorporates three established methods for analyzing differential gene expression.
• MA-plot–based methods: Introduces two methods derived from MA-plots for detection and visualization of differential expression.
• Gene and isoform-level analysis: Supports detection of differential expression at both gene and isoform levels.
• Quantitative transcriptome profiling: Operates on high-throughput RNA sequencing (RNA-seq) data to enable quantitative comparisons of transcriptomes.

Scientific Applications:

• Differential expression discovery: Identification of genes or isoforms with altered expression between experimental conditions.
• Comparative transcriptomics: Comparing expression profiles across samples for studies such as cancer research and developmental biology.
• Hypothesis generation: Supporting generation of hypotheses about gene function and regulation from transcriptome-level expression changes.

Methodology:

Implements three established differential expression methods and two MA-plot–based methods applied to quantitative RNA-seq data.