A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing
Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Her...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:c217fd3d6f434770b3db9fd65ec30c962021-12-03T05:24:53ZA Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing2296-858X10.3389/fmed.2021.684238https://doaj.org/article/c217fd3d6f434770b3db9fd65ec30c962021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmed.2021.684238/fullhttps://doaj.org/toc/2296-858XCell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.Bo-Wei HanXu YangShou-Fang QuZhi-Wei GuoLi-Min HuangKun LiKun LiGuo-Jun OuyangGeng-Xi CaiGeng-Xi CaiWei-Wei XiaoRong-Tao WengShun XuJie HuangXue-Xi YangYing-Song WuFrontiers Media S.A.articlecell-free DNAdeep learningnucleosome footprintwhole-genome sequencingautoencoderMedicine (General)R5-920ENFrontiers in Medicine, Vol 8 (2021) |
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cell-free DNA deep learning nucleosome footprint whole-genome sequencing autoencoder Medicine (General) R5-920 |
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cell-free DNA deep learning nucleosome footprint whole-genome sequencing autoencoder Medicine (General) R5-920 Bo-Wei Han Xu Yang Shou-Fang Qu Zhi-Wei Guo Li-Min Huang Kun Li Kun Li Guo-Jun Ouyang Geng-Xi Cai Geng-Xi Cai Wei-Wei Xiao Rong-Tao Weng Shun Xu Jie Huang Xue-Xi Yang Ying-Song Wu A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing |
| description |
Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost. |
| format |
article |
| author |
Bo-Wei Han Xu Yang Shou-Fang Qu Zhi-Wei Guo Li-Min Huang Kun Li Kun Li Guo-Jun Ouyang Geng-Xi Cai Geng-Xi Cai Wei-Wei Xiao Rong-Tao Weng Shun Xu Jie Huang Xue-Xi Yang Ying-Song Wu |
| author_facet |
Bo-Wei Han Xu Yang Shou-Fang Qu Zhi-Wei Guo Li-Min Huang Kun Li Kun Li Guo-Jun Ouyang Geng-Xi Cai Geng-Xi Cai Wei-Wei Xiao Rong-Tao Weng Shun Xu Jie Huang Xue-Xi Yang Ying-Song Wu |
| author_sort |
Bo-Wei Han |
| title |
A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing |
| title_short |
A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing |
| title_full |
A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing |
| title_fullStr |
A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing |
| title_full_unstemmed |
A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing |
| title_sort |
deep-learning pipeline for tss coverage imputation from shallow cell-free dna sequencing |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/c217fd3d6f434770b3db9fd65ec30c96 |
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