DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates-Reference-Cited by-同舟云学术

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DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates

Published:2024-09-06 Issue:1 Volume:25 Page:
ISSN:1474-760X
Container-title:Genome Biology
language:en
Short-container-title:Genome Biol

Author:

Mizukoshi Chikara,Kojima Yasuhiro,Nomura Satoshi,Hayashi Shuto,Abe Ko,Shimamura Teppei

Funder

Japan Society for the Promotion of Science

Japan Agency for Medical Research and Development

Moonshot Research and Development Program

Tokyo Medical and Dental University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1186/s13059-024-03367-8.pdf

Reference60 articles.

1. Bradley RK, Anczuków O. RNA splicing dysregulation and the hallmarks of cancer. Nat Rev Cancer. 2023;23(3):135–55.

2. Fang Z, Mei W, Qu C, Lu J, Shang L, Cao F, Li F. Role of m6A writers, erasers and readers in cancer. Exp Hematol Oncol. 2022;11(1):45.

3. Battich N, Beumer J, Barbanson BD, Krenning L, Baron CS, Tanenbaum ME, Clevers H, Oudenaarden AV. Sequencing metabolically labeled transcripts in single cells reveals mRNA turnover strategies. Science. 2020;367(6482):1151–6.

4. Qiu Q, Hu P, Qin X, Govek KW, Cámara PG, Wu H. Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq. Nat Methods. 2020;17(10):991–1001.

5. La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V, Lidschreiber K, et al. RNA velocity of single cells. Nature. 2018;560(7719):494–8.

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