Corbi: a new R package for biological network alignment and querying

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Large-scale biological networks are increasingly available from high-throughput data and literature.
• Existing biological network analysis tools are limited, hindering full data utilization.
• Network alignment is crucial for applications like predicting protein-protein interactions (PPI), but current methods are not fully satisfactory.

Purpose of the Study:

• To address limitations in biological network alignment.
• To introduce CNetA, an extension of the CNetQ method for network alignment.
• To evaluate CNetA's performance against existing methods.

Main Methods:

• Extended CNetQ, a conditional random fields-based network querying method, to network alignment.
• Employed an iterative bi-directional mapping strategy in the new CNetA method.
• Compared CNetA with four other methods using simulated and real protein-protein interaction (PPI) networks, assessing structural and biological measures.

Main Results:

• CNetA demonstrated superior accuracy in node and network alignment on simulated data generated from a biological network evolutionary model.
• On real PPI data, CNetA identified larger conserved and connected subnetworks compared to structure- or biology-dominated methods.
• CNetA effectively balances biological and structural similarities in network alignment.

Conclusions:

• CNetA offers improved accuracy and balance in biological network alignment.
• The developed methods, CNetQ and CNetA, are available in the R package Corbi.
• Web services for CNetQ and CNetA are freely accessible, facilitating broader use in biological network analysis.