An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data

BMC Bioinformatics

Research

Troy M. LaPolice¹  Yi-Fei Huang² 

[1] Department of Biology, Pennsylvania State University, 16802, University Park, PA, USA;Bioinformatics and Genomics Graduate Program, Pennsylvania State University, 16802, University Park, PA, USA;Huck Institutes of the Life Sciences, Pennsylvania State University, 16802, University Park, PA, USA;Department of Biology, Pennsylvania State University, 16802, University Park, PA, USA;Huck Institutes of the Life Sciences, Pennsylvania State University, 16802, University Park, PA, USA;

关键词: Deep Learning;    Unsupervised;    Essential Genes;    Loss of Function Intolerance;    Population Genomics;    Functional Genomics;   

DOI  :  10.1186/s12859-023-05481-z

 received in 2022-09-13, accepted in 2023-09-13,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed to predict human essential genes from population genomic data. While the existing methods are highly predictive of essential genes of long length, they have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome.ResultsMotivated by the premise that population and functional genomic data may provide complementary evidence for gene essentiality, here we present an evolution-based deep learning model, DeepLOF, to predict essential genes in an unsupervised manner. Unlike previous population genetic methods, DeepLOF utilizes a novel deep learning framework to integrate both population and functional genomic data, allowing us to pinpoint short essential genes that can hardly be predicted from population genomic data alone. Compared with previous methods, DeepLOF shows unmatched performance in predicting ClinGen haploinsufficient genes, mouse essential genes, and essential genes in human cell lines. Notably, at a false positive rate of 5%, DeepLOF detects 50% more ClinGen haploinsufficient genes than previous methods. Furthermore, DeepLOF discovers 109 novel essential genes that are too short to be identified by previous methods.ConclusionThe predictive power of DeepLOF shows that it is a compelling computational method to aid in the discovery of essential genes.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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