MeV

MeV performs analysis of gene expression data, including RNA-Seq, to detect differential expression and assess functional annotation enrichment.

Key Features:

• RNA-Seq Data Compatibility: Supports RNA-Seq count data and workflows for transcript-level analysis.
• Automatic Data Processing: Converts raw counts to normalized values such as RPKM and FPKM.
• Differential Expression Analysis: Implements published methods for differential expression, including DEGseq, DESeq, and edgeR.
• Functional Annotation Enrichment Detection: Performs enrichment analysis linking gene sets to biological processes, molecular functions, and cellular components.
• Genomic and Expression Visualization: Provides visualization capabilities for genomic and gene expression datasets.

Scientific Applications:

• Differential expression studies: Identifying genes with significant expression changes between experimental conditions using RNA-Seq data.
• Transcriptomics research: Characterizing gene expression dynamics across samples and conditions.
• Disease mechanism and target discovery: Investigating disease-related expression changes and associated functional annotations to inform potential therapeutic targets.
• Evolutionary and ecological genetics: Applying gene expression analysis to questions in evolutionary biology and ecological genetics.

Methodology:

Processing of RNA-Seq count data with automatic conversion to RPKM/FPKM, differential expression detection using methods such as DEGseq, DESeq, and edgeR, and functional annotation enrichment analysis linking genes to biological process, molecular function, and cellular component categories.