Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning
文献类型：期刊论文
| 作者 | Mao, Miaowei1,2,3; Hu, Yue2; Yang, Yun2; Wei, Huanhuan2; Wang, Zefeng2; Qian, Yajie3; Yang, Yi3; Fan, Wei1; Li, Xiaoling1; , |
| 刊名 | CELL SYSTEMS
 |
| 出版日期 | 2018 |
| 卷号 | 7期号:5页码:510-+ |
| 关键词 | Metagenome Next-generation sequencing 16S rRNA Enterotype Genome-wide association study |
| ISSN号 | 2405-4712 |
| DOI | 10.1016/j.cels.2018.09.002 |
| 文献子类 | Article |
| 英文摘要 | Alternative splicing (AS) is generally regulated by trans-splicing factors that specifically bind to cis-elements in pre-mRNAs. The human genome encodes similar to 1,500 RNA binding proteins (RBPs) that potentially regulate AS, yet their functions remain largely unknown. To explore their potential activities, we fused the putative functional domains of RBPs to a sequence-specific RNA-binding domain and systemically analyzed how these engineered factors affect splicing. We discovered that similar to 80% of low-complexity domains in endogenous RBPs displayed distinct context-dependent activities in regulating splicing, indicating that AS is under more extensive regulation than previously expected. We developed a machine learning approach to classify and predict the activities of RBPs based on their sequence compositions and further validated this model using endogenous RBPs and synthetic polypeptides. These results represent a systematic inspection, modeling, prediction, and validation of how RBP sequences affect their activities in controlling splicing, paving the way for de novo engineering of artificial splicing factors. |
| 学科主题 | Biochemistry & Molecular Biology ; Cell Biology |
| WOS关键词 | SYSTEMATIC IDENTIFICATION ; DETAINED INTRONS ; RNA RECOGNITION ; RICH DOMAINS ; HNRNP A1 ; ACTIVATION ; ENHANCERS ; PROTEINS ; ELEMENTS ; CODE |
| 语种 | 英语 |
| WOS记录号 | WOS:000451567600005 |
| 出版者 | CELL PRESS |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/981]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.NIEHS, Signal Transduct Lab, POB 12233, Res Triangle Pk, NC 27709 USA, 2.Chinese Acad Sci, Shanghai Inst Nutr & Hlth,Shanghai Inst Biol Sci, CAS Ctr Excellence Mol Cell Sci,Univ Chinese Acad, CAS Key Lab Computat Biol,CAS MPG Partner Inst Co, Shanghai 200031, Peoples R China; 3.East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Synthet Biol & Biotechnol Lab, Sch Pharm, Shanghai 200237, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Mao, Miaowei,Hu, Yue,Yang, Yun,et al. Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning[J]. CELL SYSTEMS,2018,7(5):510-+. |
| APA | Mao, Miaowei.,Hu, Yue.,Yang, Yun.,Wei, Huanhuan.,Wang, Zefeng.,...&,.(2018).Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.CELL SYSTEMS,7(5),510-+. |
| MLA | Mao, Miaowei,et al."Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning".CELL SYSTEMS 7.5(2018):510-+. |
入库方式： OAI收割
来源：上海营养与健康研究所
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