气候变化领域集成服务门户(CCPortal): A machine learning-based framework for modeling transcription elongation

CCPortal

DOI	10.1073/pnas.2007450118
	A machine learning-based framework for modeling transcription elongation
	Feng P.; Xiao A.; Fang M.; Wan F.; Li S.; Lang P.; Zhao D.; Zeng J.
发表日期	2021
ISSN	00278424
卷号	118 期号:6
英文摘要	RNA polymerase II (Pol II) generally pauses at certain positions along gene bodies, thereby interrupting the transcription elongation process, which is often coupled with various important biological functions, such as precursor mRNA splicing and gene expression regulation. Characterizing the transcriptional elongation dynamics can thus help us understand many essential biological processes in eukaryotic cells. However, experimentally measuring Pol II elongation rates is generally time and resource consuming. We developed PEPMAN (polymerase II elongation pausing modeling through attention-based deep neural network), a deep learning-based model that accurately predicts Pol II pausing sites based on the native elongating transcript sequencing (NET-seq) data. Through fully taking advantage of the attention mechanism, PEPMAN is able to decipher important sequence features underlying Pol II pausing. More importantly, we demonstrated that the analyses of the PEPMAN-predicted results around various types of alternative splicing sites can provide useful clues into understanding the cotranscriptional splicing events. In addition, associating the PEPMAN prediction results with different epigenetic features can help reveal important factors related to the transcription elongation process. All these results demonstrated that PEPMAN can provide a useful and effective tool for modeling transcription elongation and understanding the related biological factors from available high-throughput sequencing data. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Alternative splicing; Deep learning; Pol II pausing
语种	英语
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180715
作者单位	Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China; School of Life Sciences, Tsinghua University, Beijing, China; Ministry of Education Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
推荐引用方式 GB/T 7714	Feng P.,Xiao A.,Fang M.,et al. A machine learning-based framework for modeling transcription elongation[J],2021,118(6).
APA	Feng P..,Xiao A..,Fang M..,Wan F..,Li S..,...&Zeng J..(2021).A machine learning-based framework for modeling transcription elongation.Proceedings of the National Academy of Sciences of the United States of America,118(6).
MLA	Feng P.,et al."A machine learning-based framework for modeling transcription elongation".Proceedings of the National Academy of Sciences of the United States of America 118.6(2021).