In:
Frontiers in Medicine, Frontiers Media SA, Vol. 8 ( 2021-12-3)
Kurzfassung:
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.
Materialart:
Online-Ressource
ISSN:
2296-858X
DOI:
10.3389/fmed.2021.684238
DOI:
10.3389/fmed.2021.684238.s001
DOI:
10.3389/fmed.2021.684238.s002
DOI:
10.3389/fmed.2021.684238.s003
Sprache:
Unbekannt
Verlag:
Frontiers Media SA
Publikationsdatum:
2021
ZDB Id:
2775999-4