ReadZS detects cell type-specific and developmentally regulated RNA processing programs in single-cell RNA-seq

被引:3
|
作者
Meyer, Elisabeth [1 ,2 ]
Chaung, Kaitlin [1 ,2 ]
Dehghannasiri, Roozbeh [1 ,2 ]
Salzman, Julia [1 ,2 ,3 ]
机构
[1] Stanford Univ, Dept Biochem, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94305 USA
[3] Stanford Univ, Dept Stat Courtesy, Stanford, CA 94305 USA
基金
美国国家科学基金会;
关键词
scRNA-seq; Differential RNA processing; Alternative polyadenylation; Untranslated regions; 3' UNTRANSLATED REGIONS; ALTERNATIVE POLYADENYLATION; MESSENGER-RNAS; GREATWALL;
D O I
10.1186/s13059-022-02795-8
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
RNA processing, including splicing and alternative polyadenylation, is crucial to gene function and regulation, but methods to detect RNA processing from single-cell RNA sequencing data are limited by reliance on pre-existing annotations, peak calling heuristics, and collapsing measurements by cell type. We introduce ReadZS, an annotation-free statistical approach to identify regulated RNA processing in single cells. ReadZS discovers cell type-specific RNA processing in human lung and conserved, developmentally regulated RNA processing in mammalian spermatogenesis-including global 3 ' UTR shortening in human spermatogenesis. ReadZS also discovers global 3 ' UTR lengthening in Arabidopsis development, highlighting the usefulness of this method in under-annotated transcriptomes.
引用
收藏
页数:28
相关论文
共 50 条
  • [21] SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data
    Cao, Yinghao
    Wang, Xiaoyue
    Peng, Gongxin
    FRONTIERS IN GENETICS, 2020, 11
  • [22] Realistic Cell Type Annotation and Discovery for Single-cell RNA-seq Data
    Zhai, Yuyao
    Chen, Liang
    Deng, Minghua
    PROCEEDINGS OF THE THIRTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2023, 2023, : 4967 - 4974
  • [23] HArmonized single-cell RNA-seq Cell type Assisted Deconvolution (HASCAD)
    Chiu, Yen-Jung
    Ni, Chung-En
    Huang, Yen-Hua
    BMC MEDICAL GENOMICS, 2023, 16 (SUPPL 2)
  • [24] Single-Cell RNA-Seq Analysis Reveals Lung Epithelial Cell Type-Specific Responses to HDM and Regulation by Tet1
    Zhu, Tao
    Brown, Anthony P.
    Cai, Lucy P.
    Quon, Gerald
    Ji, Hong
    GENES, 2022, 13 (05)
  • [25] Unsupervised cell functional annotation for single-cell RNA-seq
    Li, Dongshunyi
    Ding, Jun
    Bar-Joseph, Ziv
    GENOME RESEARCH, 2022, 32 (09) : 1765 - 1775
  • [26] Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity
    Chen, Renchao
    Wu, Xiaoji
    Jiang, Lan
    Zhang, Yi
    CELL REPORTS, 2017, 18 (13): : 3227 - 3241
  • [27] Yeast Single-cell RNA-seq, Cell by Cell and Step by Step
    Nadal-Ribelles, Mariona
    Islam, Saiful
    Wei, Wu
    Latorre, Pablo
    Nguyen, Michelle
    de Nadal, Eulalia
    Posas, Francesc
    Steinmetz, Lars M.
    BIO-PROTOCOL, 2019, 9 (17):
  • [28] Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
    Kotliar, Dylan
    Veres, Adrian
    Nagy, M. Aurel
    Tabrizi, Shervin
    Hodis, Eran
    Melton, Douglas A.
    Sabeti, Pardis C.
    ELIFE, 2019, 8
  • [29] SINGLE-CELL ANALYSIS From single-cell RNA-seq to transcriptional regulation
    La Manno, Gioele
    NATURE BIOTECHNOLOGY, 2019, 37 (12) : 1421 - 1422
  • [30] RNA Velocity: Molecular Kinetics from Single-Cell RNA-Seq
    Svensson, Valentine
    Pachter, Lior
    MOLECULAR CELL, 2018, 72 (01) : 7 - 9