GraphiteWeb

GraphiteWeb performs pathway-level analysis and network-based interpretation of gene expression data from microarray and RNA-seq experiments.

Key Features:

• Supported data types: Accepts gene expression data from microarray and RNA-seq experiments.
• Network representation: Represents biological pathways as networks with genes as nodes and their interactions as edges.
• Topological and multivariate analyses: Integrates topological and multivariate approaches to capture relationships among genes within pathways beyond non-topological and univariate methods.
• Gene set analyses: Implements five distinct gene set analysis methods.
• Organisms and databases: Performs analyses across three model organisms using pathway information from two pathway databases.
• Network visualization outputs: Produces network-based visualizations of pathway topology and gene expression mappings.

Scientific Applications:

• Interpretation of gene expression: Translates differential expression results into pathway- and network-level insights.
• Pathway topology analysis: Identifies biological signals that depend on gene interactions and pathway structure rather than individual gene changes alone.
• Cross-organism pathway analysis: Enables pathway-level comparisons using data from three model organisms and two pathway databases.

Methodology:

Represents pathways as networks (genes as nodes, interactions as edges), integrates topological and multivariate approaches, and implements five distinct gene set analyses on microarray and RNA-seq expression data using pathway information from two pathway databases across three model organisms.