Deep learning of the splicing (epi) genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision

NWIPB OpenIR

	Deep learning of the splicing (epi) genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision
	Xu, Yungang ; Wang, Yongcui ; Luo, Jiesi ; Zhao, Weiling ; Zhou, Xiaobo
	2017-12-01
发表期刊	NUCLEIC ACIDS RESEARCH
摘要	Alternative splicing (AS) is a genetically and epigenetically regulated pre-mRNA processing to increase transcriptome and proteome diversity. Comprehensively decoding these regulatory mechanisms holds promise in getting deeper insights into a variety of biological contexts involving in AS, such as development and diseases. We assembled splicing (epi) genetic code, DeepCode, for human embryonic stem cell (hESC) differentiation by integrating heterogeneous features of genomic sequences, 16 histone modifications with a multi-label deep neural network. With the advantages of epigenetic features, DeepCode significantly improves the performance in predicting the splicing patterns and their changes during hESC differentiation. Meanwhile, DeepCode reveals the superiority of epigenomic features and their dominant roles in decoding AS patterns, highlighting the necessity of including the epigenetic properties when assembling a more comprehensive splicing code. Moreover, DeepCode allows the robust predictions across cell lineages and datasets. Especially, we identified a putative H3K36me3-regulated AS event leading to a nonsense-mediated mRNA decay of BARD1. Reduced BARD1 expression results in the attenuation of ATM/ATR signalling activities and further the hESC differentiation. These results suggest a novel candidate mechanism linking histone modifications to hESC fate decision. In addition, when trained in different contexts, DeepCode can be expanded to a variety of biological and biomedical fields.
文献类型	期刊论文
条目标识符	http://210.75.249.4/handle/363003/59384
专题	中国科学院西北高原生物研究所
推荐引用方式 GB/T 7714	Xu, Yungang,Wang, Yongcui,Luo, Jiesi,et al. Deep learning of the splicing (epi) genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision[J]. NUCLEIC ACIDS RESEARCH,2017.
APA	Xu, Yungang,Wang, Yongcui,Luo, Jiesi,Zhao, Weiling,&Zhou, Xiaobo.(2017).Deep learning of the splicing (epi) genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision.NUCLEIC ACIDS RESEARCH.
MLA	Xu, Yungang,et al."Deep learning of the splicing (epi) genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision".NUCLEIC ACIDS RESEARCH (2017).