Computational methods, databases and tools for synthetic lethality prediction. Issue 3 (29th March 2022)

• Record Type: Journal Article
• Authors: Wang, Jing; Zhang, Qinglong; Han, Junshan; Zhao, Yanpeng; Zhao, Caiyun; Yan, Bowei; Dai, Chong; Wu, Lianlian; Wen, Yuqi; Zhang, Yixin; Leng, Dongjin; Wang, Zhongming; Yang, Xiaoxi; He, Song; Bo, Xiaochen
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 3(2022)
• Publication Date: 2022-03-29
• Subjects: synthetic lethality -- computational methods -- deep learning -- machine learning; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac106
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.