MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell.

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 i

SUBMITTER: Wang R 

PROVIDER: S-EPMC11470080 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature