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

Ruohan Wang, Yumin Zheng, Zijian Zhang, Kailu Song, Erxi Wu, Xiaopeng Zhu, Tao P. Wu () and Jun Ding ()
Additional contact information
Ruohan Wang: McGill University
Yumin Zheng: Research Institute of the McGill University Health Centre
Zijian Zhang: Baylor College of Medicine
Kailu Song: Research Institute of the McGill University Health Centre
Erxi Wu: Baylor College of Medicine
Xiaopeng Zhu: MyCellome LLC.
Tao P. Wu: Baylor College of Medicine
Jun Ding: McGill University

Nature Communications, 2024, vol. 15, issue 1, 1-22

Abstract: 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-53114-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53114-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-53114-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().