Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized...

Area of Science:

• Biochemistry
• Bioinformatics
• Computational Biology

Background:

• Bioactive peptides are crucial for human health, attracting significant academic and pharmaceutical interest.
• The proliferation of bioactive peptide databases leads to data redundancy, inconsistency, and errors, hindering comprehensive analysis.
• A unified data view is essential for efficient and accurate research on bioactive peptides.

Purpose of the Study:

• To address data fragmentation and inconsistency in bioactive peptide resources.
• To develop an integrated platform for comprehensive analysis of bioactive peptide data.
• To provide a user-friendly tool for exploring and visualizing bioactive peptide networks.

Main Methods:

• Collected data from diverse bioactive peptide databases.
• Organized integrated data into a graph database (starPepDB) with nodes for peptides and metadata.
• Developed a software tool (starPep toolbox) for visual network analysis, including retrieval, filtering, and visualization.

Main Results:

• Created starPepDB, a graph database containing 71,310 nodes and 348,505 relationships, with 45,120 nodes representing peptides.
• The starPep toolbox offers functionalities for interactive exploration, network construction, and data export.
• The integrated database and tool facilitate a unified and comprehensive analysis of bioactive peptide information.

Conclusions:

• starPepDB and starPep toolbox provide a unified solution for managing and analyzing bioactive peptide data.
• The platform enhances research efficiency by overcoming data redundancy and standardization issues.
• This integrated approach supports deeper insights into the roles and applications of bioactive peptides.