ArticleNature communications2024
MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Review
- Active Human Transposable Elements: Long-Read Sequencing Technologies, Computational Analysis, and Implications for Human Disease.Biomolecules · 2026Review
- Transposable elements and homotypic niches drive immune dynamics and resistance in melanoma epigenetic-based immunotherapy.Science advances · 2026Article
- LINE-1 retrotransposons: Multifaceted functions and regulatory mechanisms in health and disease.Molecular therapy. Nucleic acids · 2026Review
- TE-SCALE: a comprehensive database for exploring transposable element expression across human cancers at single-cell resolution.Nucleic acids research · 2026Article
- Transposable Element Dysregulation in Hepatocellular Carcinoma: Epigenetic Mechanisms, Immune Remodeling, and Translational Opportunities.Journal of hepatocellular carcinoma · 2026Review
- A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.Nature biomedical engineering · 2025Article
- Transposable elements in health and disease: Molecular basis and clinical implications.Chinese medical journal · 2025Review
- Dynamic dysregulation of retrotransposons in neurodegenerative diseases at the single-cell level.Genome research · 2024Article
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Authors and funding
8 authors.
Funding
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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