| Literature DB >> 36229673 |
Jiaqi Li1,2, Jingjing Wang3,4, Peijing Zhang1,2, Renying Wang1, Yuqing Mei1, Zhongyi Sun1, Lijiang Fei1, Mengmeng Jiang1,2, Lifeng Ma1, Weigao E1, Haide Chen1,2, Xinru Wang1, Yuting Fu1, Hanyu Wu1, Daiyuan Liu1, Xueyi Wang1, Jingyu Li1, Qile Guo5, Yuan Liao1,6, Chengxuan Yu1, Danmei Jia1, Jian Wu7, Shibo He8, Huanju Liu9, Jun Ma9, Kai Lei10, Jiming Chen8, Xiaoping Han11,12, Guoji Guo13,14,15,16,17.
Abstract
Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila and earthworm. We then integrated cell landscapes from eight representative metazoan species to study gene regulation across evolution. Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in vertebrates and invertebrates. Our work provides a valuable resource and offers a new strategy for studying regulatory grammar in diverse biological systems.Entities:
Year: 2022 PMID： 36229673 DOI： 10.1038/s41588-022-01197-7
Source DB: PubMed Journal: Nat Genet ISSN： 1061-4036 Impact factor: 41.307