Publication Date
10-11-2024
Journal
Nature Communications
DOI
10.1038/s41467-024-53114-7
PMID
39394211
PMCID
PMC11470080
PubMedCentral® Posted Date
10-11-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
DNA Transposable Elements, Single-Cell Analysis, Deep Learning, Animals, Humans, Genomics, Genetic Loci, Computational models, Genome informatics, Software, Machine learning, Gene regulation
Abstract
Transposable elements (TEs) are crucial for genetic diversity and gene regulation. Current single-cell quantification methods often align multi-mapping reads to either 'best-mapped' or 'random-mapped' locations and categorize them at the subfamily levels, overlooking the biological necessity for accurate, locus-specific TE quantification. Moreover, these existing methods are primarily designed for and focused on transcriptomics data, which restricts their adaptability to single-cell data of other modalities. To address these challenges, here we introduce MATES, a deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. When applied to diverse single-cell omics datasets, MATES shows improved performance over existing methods, enhancing the accuracy of TE quantification and aiding in the identification of marker TEs for identified cell populations. This development facilitates the exploration of single-cell heterogeneity and gene regulation through the lens of TEs, offering an effective transposon quantification tool for the single-cell genomics community.
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