Computational methods, databases and tools for synthetic lethality prediction

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 in...

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Published in:Briefings in bioinformatics Vol. 23; no. 3
Main 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
Format: Journal Article
Language:English
Published: England Oxford University Press 13.05.2022
Oxford Publishing Limited (England)
Subjects:
Cancer
Cell death
Cell survival
Computer applications
Computer networks
Deactivation
Deep learning
Genes
Inactivation
Lethality
Machine learning
Poly(ADP-ribose) polymerase
Predictions
Review
Sampling methods
Software
Statistical methods
computational methods
deep learning
machine learning
synthetic lethality
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary: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.
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Jing Wang and Qinglong Zhang authors contributed equally to this work.
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbac106