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GLAlign: A Novel Algorithm for Local Network Alignment.

Marianna Milano, Pietro Hiram Guzzi, Mario Cannataro

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|July 12, 2018

Summary

This summary is machine-generated.

GLAlign improves local network alignment by using global alignment topological information. This new methodology enhances the performance of local aligners for biological network comparison.

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Area of Science:

Computational Biology
Bioinformatics

Background:

Networks are widely used for modeling biological systems, such as Protein-Protein Interaction Networks (PPINs).
Comparing PPINs across organisms aids in understanding conserved biological processes.
Network alignment algorithms are crucial for this comparison, categorized as global or local.

Purpose of the Study:

To enhance local network alignment performance by integrating global alignment information.
To introduce GLAlign, a novel methodology combining global and local alignment strategies.
To provide a practical implementation of the GLAlign methodology.

Main Methods:

Utilizing topological information derived from a preliminary global network alignment.
Guiding the steps of a local network alignment process with global alignment insights.
Developing and implementing the GLAlign methodology.

Main Results:

The GLAlign methodology significantly improves the performance of local network aligners.
GLAlign outperforms existing state-of-the-art local alignment algorithms.
A prototype implementation of GLAlign was evaluated as a proof-of-principle.

Conclusions:

Integrating global alignment information effectively enhances local network alignment.
GLAlign offers a superior approach for comparing biological networks.
The GLAlign tool is publicly available for academic research.