GraphAlignment

GraphAlignment aligns biological interaction networks across species in R to map conserved functional relationships between genes by integrating interaction patterns and sequence similarity.

Key Features:

• Network alignment based on similarity scores: Employs a scoring function that evaluates mutual similarities between two networks by considering interaction patterns and node sequence similarities.
• Bayesian analysis for alignment inference: Utilizes systematic Bayesian analysis to infer high-scoring alignments and estimate alignment parameters.
• Cross-species functional relationship mapping: Reveals conserved gene expression clusters and supports prediction of gene function across species from aligned networks.

Scientific Applications:

• Analyzing coexpression networks: Aligns coexpression networks to identify conserved clusters relevant to phenotypic traits, including comparisons between species such as human and mouse.
• Predicting gene function: Predicts gene function across species based on network-alignment evidence rather than sequence similarity alone.
• Evolutionary studies of functional conservation: Investigates conservation of gene expression patterns and functional relationships driven by gene or protein interactions to inform phenotypic evolution research.

Methodology:

Development of a scoring function integrating interaction patterns and node sequence similarities, systematic Bayesian inference to derive high-scoring alignments and refine alignment parameters, and application of these alignments to study conservation of gene expression clusters in evolutionary analyses.