SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery.

Area of Science:

• Genomics
• Bioinformatics
• Cancer Therapeutics

Background:

• Synthetic lethality (SL) describes gene pairs where simultaneous mutations impair cell viability, offering potential in cancer treatment.
• Identifying novel SL gene pairs is crucial for developing targeted anticancer therapies.

Purpose of the Study:

• To present an updated version of the SynLethDB database, enhancing the discovery of clinically relevant SL gene pairs.
• To incorporate new SL data, particularly from Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, and related biological knowledge.

Main Methods:

• The SynLethDB database was significantly expanded with new SLs identified via CRISPR screening.
• A knowledge graph of human SLs was integrated, alongside 26 other relationship types.
• A new web interface was developed with modules for disease/compound-based queries, gene set enrichment analysis, and knowledge graph visualization.

Main Results:

• The updated database includes over 16,000 new SLs, involving 14,100 genes, 53 cancers, and 1,898 drugs.
• The new interface facilitates exploration of SL data through various analytical and visualization tools.
• Data is accessible via direct download and RESTful Application Programming Interfaces (APIs).

Conclusions:

• The enhanced SynLethDB v2 provides a comprehensive resource for exploring synthetic lethality.
• This updated database facilitates the discovery of novel cancer therapeutic targets and strategies.
• The integrated knowledge graph and user-friendly interface support advanced research in cancer genomics and drug discovery.