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Using Deep Learning to Predict Transcription Factor Binding Sites Combining Raw DNA Sequence, Evolutionary Information and Epigenomic Data

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Book cover Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12838))

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Abstract

DNA-binding proteins (DBPs) have an important role in various regulatory tasks. In recent years, with developing of deep learning, many fields like natural language processing, computer vision and so on have achieve great success. Some great model, for example DeepBind, brought deep learning to motif discovery and also achieve great success in predicting DNA-transcription factor binding, aka motif discovery. But these methods required integrating multiple features with raw DNA sequences such as secondary structure and their performances could be further improved. In this paper, we propose an efficient and simple neural network-based architecture, DBPCNN, integrating conservation scores and epigenomic data to raw DNA sequences for predicting in-vitro DNA protein binding sequence. We show that conservation scores and epigenomic data for raw DNA sequences can significantly improve the overall performance of the proposed model. Moreover, the automatic extraction of the DBA-binding proteins can enhance our understanding of the binding specificities of DBPs. We verify the effectiveness of our model on 20 motif datasets from in-vitro protein binding microarray data. More specifically, the average area under the receiver operator curve (AUC) was improved by 0.58% for conservation scores, 1.29% for MeDIP-seq, 1.20% for histone modifications respectively, and 2.19% for conservation scores, MeDIP-seq and histone modifications together. And the mean average precision (AP) was increased by 0.62% for conservation scores, 1.46% for MeDIP-seq, 1.27% for histone modifications respectively, and 2.29% for conservation scores, MeDIP-seq and histone modifications together.

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Acknowledgements

This work was supported by the grant of National Key R&D Program of China (No. 2018AAA0100100 & 2018YFA0902600) and partly supported by National Natural Science Foundation of China (Grant nos. 61861146002, 61732012, 61772370, 62002266, 61932008, 61772357, and 62073231) and supported by “BAGUI Scholar” Program and the Scientific & Technological Base and Talent Special Program, GuiKe AD18126015 of the Guangxi Zhuang Autonomous Region of China.

Author information

Authors and Affiliations

  1. Guangxi Academy of Science, Nanning, 530007, China

    Youhong Xu, Qinghu Zhang, Zhanheng Chen & Changan Yuan

  2. School of Computer and Information Engineering, Nanning Normal University, Nanning, 530299, China

    Youhong Xu, Qinghu Zhang, Zhanheng Chen & Xiao Qin

  3. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

    Youhong Xu, Qinghu Zhang, Zhanheng Chen & Hongjie Wu

Authors
  1. Youhong Xu
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  2. Qinghu Zhang
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  3. Zhanheng Chen
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  4. Changan Yuan
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  5. Xiao Qin
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  6. Hongjie Wu
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Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Xu, Y., Zhang, Q., Chen, Z., Yuan, C., Qin, X., Wu, H. (2021). Using Deep Learning to Predict Transcription Factor Binding Sites Combining Raw DNA Sequence, Evolutionary Information and Epigenomic Data. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_35

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  • DOI: https://doi.org/10.1007/978-3-030-84532-2_35

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