sagenhaft

sagenhaft extracts and analyzes Serial Analysis of Gene Expression (SAGE) sequencing tag data to generate SAGE libraries, correct sequencing errors using an Expectation Maximization Algorithm based on a Mixture Model for tag counts, and compare SAGE libraries.

Key Features:

• Extraction of SAGE Libraries: Extracts SAGE libraries from raw sequence files to produce tag count datasets.
• Sequencing Error Correction: Implements an Expectation Maximization Algorithm based on a Mixture Model for tag counts to correct sequencing errors.
• Library Comparison: Compares different SAGE libraries to identify expression differences across samples.

Scientific Applications:

• Gene Expression Profiling: Analysis of SAGE-derived tag counts for quantitative gene expression profiling.
• Differential Expression Analysis: Identification of differentially expressed genes between SAGE libraries.
• Regulatory and Pathway Studies: Investigation of gene expression regulation and exploration of biological pathways using SAGE data.

Methodology:

Extraction of SAGE libraries from sequence files; sequencing error correction via an Expectation Maximization Algorithm based on a Mixture Model for tag counts; comparative analysis of SAGE libraries.