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

Author:

Wang Jie1,Wu Min2ORCID,Huang Xuhui3,Wang Li1,Zhang Sophia4,Liu Hui5ORCID,Zheng Jie16ORCID

Affiliation:

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

2. Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Singapore 138632, Singapore

3. School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore 117417, Singapore

4. College of Agriculture and Life Sciences, Cornell University, 260 Roberts Hall, Ithaca, NY 14853, USA

5. School of Computer Science and Technology, Nanjing Tech University, 30 Puzhu Road, Nanjing 211816, China

6. Shanghai Engineering Research Center of Intelligent Vision and Imaging, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China

Abstract

Abstract Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL:  https://synlethdb.sist.shanghaitech.edu.cn/v2.

Funder

ShanghaiTech university

Publisher

Oxford University Press (OUP)

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,Information Systems

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