An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data
Abstract Background The 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 popul...
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-023-05481-z |
| _version_ | 1797556070989692928 |
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| author | Troy M. LaPolice Yi-Fei Huang |
| author_facet | Troy M. LaPolice Yi-Fei Huang |
| author_sort | Troy M. LaPolice |
| collection | DOAJ |
| description | Abstract Background The 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. Results Motivated 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. Conclusion The predictive power of DeepLOF shows that it is a compelling computational method to aid in the discovery of essential genes. |
| first_indexed | 2024-03-10T16:56:32Z |
| format | Article |
| id | doaj.art-111a54c0ab144522abec3097ab941e6c |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-10T16:56:32Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-111a54c0ab144522abec3097ab941e6c2023-11-20T11:06:36ZengBMCBMC Bioinformatics1471-21052023-09-0124112110.1186/s12859-023-05481-zAn unsupervised deep learning framework for predicting human essential genes from population and functional genomic dataTroy M. LaPolice0Yi-Fei Huang1Department of Biology, Pennsylvania State UniversityDepartment of Biology, Pennsylvania State UniversityAbstract Background The 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. Results Motivated 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. Conclusion The predictive power of DeepLOF shows that it is a compelling computational method to aid in the discovery of essential genes.https://doi.org/10.1186/s12859-023-05481-zDeep LearningUnsupervisedEssential GenesLoss of Function IntolerancePopulation GenomicsFunctional Genomics |
| spellingShingle | Troy M. LaPolice Yi-Fei Huang An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data BMC Bioinformatics Deep Learning Unsupervised Essential Genes Loss of Function Intolerance Population Genomics Functional Genomics |
| title | An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data |
| title_full | An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data |
| title_fullStr | An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data |
| title_full_unstemmed | An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data |
| title_short | An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data |
| title_sort | unsupervised deep learning framework for predicting human essential genes from population and functional genomic data |
| topic | Deep Learning Unsupervised Essential Genes Loss of Function Intolerance Population Genomics Functional Genomics |
| url | https://doi.org/10.1186/s12859-023-05481-z |
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