CAGEr

CAGEr analyzes Cap Analysis of Gene Expression (CAGE) sequencing data to identify, normalize, and explore transcription start sites (TSSs) and promoter usage and to integrate TSS maps with other genomic datasets.

Key Features:

• Preprocessing and Normalization: Performs preprocessing and robust normalization of CAGE sequencing data to accurately identify transcription start sites (TSSs).
• Differential TSS Usage Analysis: Implements methodologies for detecting and quantifying differential usage of transcription start sites across conditions or cell types.
• Promoterome Mining: Integrates promoterome mining with CAGE data processing to identify active promoters and associated regulatory elements.
• Integration with Other Genomic Data Types: Facilitates integration of high-resolution TSS maps with other genomic datasets for integrative analyses.
• Access to Extensive TSS Collections: Provides access to large collections of TSSs from published CAGE datasets, including direct access to the FANTOM5 resource with context-specific human and mouse TSS data.
• R Implementation: Implemented in R for manipulation and integration of high-resolution TSS data.

Scientific Applications:

• Transcriptome and TSS Mapping: Generates single-base-pair resolution maps of transcription start sites for transcriptome analyses.
• Promoter Activity and Transcriptional Dynamics: Enables studies of promoter activity and transcriptional dynamics.
• Differential Promoter/TSS Usage Studies: Investigates variation in TSS utilization across conditions or cell types.
• Integrative Functional Genomics and Systems Biology: Integrates TSS data with other genomic datasets to uncover gene regulation networks and functional genomics insights.
• Personalized Medicine Applications: Supports applications that leverage context-specific TSS information for translational or personalized analyses.

Methodology:

Implemented in R; performs preprocessing and normalization of CAGE sequencing reads, identification and quantification of transcription start sites, analysis of differential TSS usage, promoterome mining, and integration with other genomic datasets, with access to published TSS collections including FANTOM5.