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

View ORCID ProfileYungang Xu, Yongcui Wang, Jiesi Luo, Weiling Zhao, Xiaobo Zhou
doi: https://doi.org/10.1101/189183
Yungang Xu
1Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
3Center for Systems Medicine, School of Medical Bioinformatics, University of Texas Health Science Center at Houston, TX, 77030, USA
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  • ORCID record for Yungang Xu
Yongcui Wang
2Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, 810008, China
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Jiesi Luo
1Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
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Weiling Zhao
1Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
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Xiaobo Zhou
1Center for Bioinformatics and Systems Biology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
3Center for Systems Medicine, School of Medical Bioinformatics, University of Texas Health Science Center at Houston, TX, 77030, USA
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  • For correspondence: xiaobo.zhou@uth.tmc.edu
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ABSTRACT

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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 15, 2017.
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Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision
Yungang Xu, Yongcui Wang, Jiesi Luo, Weiling Zhao, Xiaobo Zhou
bioRxiv 189183; doi: https://doi.org/10.1101/189183
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Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision
Yungang Xu, Yongcui Wang, Jiesi Luo, Weiling Zhao, Xiaobo Zhou
bioRxiv 189183; doi: https://doi.org/10.1101/189183

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