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

Jie Wang1, Min Wu2, Xuhui Huang3

  • 1School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China.

Database : the Journal of Biological Databases and Curation
|May 13, 2022
PubMed

Insights

Researchers updated SynLethDB, a database for synthetic lethality (SL) gene pairs. This version includes over 16,000 new SLs from CRISPR screening, aiding cancer therapeutic 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.

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