Ten practical tips and tricks to improve the effectiveness of biological network alignment

Area of Science:

• Computational Biology
• Bioinformatics
• Systems Biology

Background:

• Biological networks represent complex interactions within living systems.
• Comparing these networks across species or conditions reveals conserved biological mechanisms.
• Network alignment (NA) is a key computational approach for this comparison.

Purpose of the Study:

• To provide a comprehensive overview of network alignment methodologies.
• To highlight challenges and best practices in network alignment research.
• To guide researchers in leveraging NA for deeper biological insights.

Main Methods:

• Discusses data preprocessing, input formats, and network types (attributed, temporal, multilayer).
• Explores challenges: seed nodes selection, algorithm configuration, cross-species alignment.
• Emphasizes integrating functional annotations, sequence similarity, and network topology.

Main Results:

• Reviews Local and Global Network Alignment strategies, detailing their pros and cons.
• Provides practical recommendations for documenting and visualizing NA experiments.
• Highlights the importance of using diverse alignment tools and adhering to best practices.

Conclusions:

• Network alignment is crucial for understanding conserved biological processes and evolutionary relationships.
• Effective NA requires careful consideration of data, methods, and integration of biological information.
• This work aims to enhance the reproducibility and impact of network alignment studies in biology.