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DeepSite: bidirectional LSTM and CNN models for predicting DNA–protein binding

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Abstract

Transcription factors are cis-regulatory molecules that bind to specific sub-regions of DNA promoters and initiate transcription, the process that regulates the conversion of genetic information from DNA to RNA. Several computational methods have been developed to predict DNA–protein binding sites in DNA sequence using convolutional neural network (CNN). However, these techniques could indicate the dependency information of DNA sequence information in the framework of CNN. In addition, these methods are not accurate enough in prediction of the DNA–protein binding sites from the DNA sequence. In this study, we employ the bidirectional long short-term memory (BLSTM) and CNN to capture long-term dependencies between the sequence motifs in DNA, which is called DeepSite. Apart from traditional CNN, which includes six layers: input layer, BLSTM layer, CNN layer, pooling layer, full connection layer and output layer, DeepSite approach can predict DNA–protein binding sites with 87.12% sensitivity, 91.06% specificity, 89.19% accuracy and 0.783 MCC, when tested on the 690 Chip-seq experiments from ENCODE. Lastly, we conclude that our proposed method can also be applied to find DNA–protein binding sites in different DNA sequences.

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Notes

  1. http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeAwgTfbsUniform/

Abbreviations

Acc:

Accuracy

AUC:

The area under the ROC curve

BLSTM:

Bidirectional long short-term memory

BP:

Back-propagation algorithm

CNN:

Convolutional neural network

ENCODE:

The Encyclopedia of DNA elements

FN:

The number of false negative

FP:

The number of false positive

GPU:

Graphical processing units

MCC:

Mathews correlation coefficient

PFM:

Positional frequency matrix

Pre:

Precision

PSSM:

Position specific scoring matrix

ROC:

Receiver operating characteristic

Sen:

Sensitivity

Spe:

Specificity

TN:

The number of true negatives

TP:

The number of true positive

TFs:

Transcription factors

TFBS:

Transcription factor binding site

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants nos. 61702058, 61772091, 61802035, 71701026; the China Postdoctoral Science Foundation funded project under Grant no. 2017M612948; the Scientific Research Foundation for Education Department of Sichuan Province under Grant no. 18ZA0098; the Sichuan Science and Technology Program under Grant nos. 2018JY0448, 2019YFG0106, 2019YFS0067, 2018GZ0307; the Natural Science Foundation of Guangxi under Grant no. 2018GXNSFDA138005; the Innovative Research Team Construction Plan in Universities of Sichuan Province under Grant no. 18TD0027; the Fund of Science and Technology Department of Guizhou Province under Grant no. J[2014]2134; the Scientific Research Foundation for Young Academic Leaders of Chengdu University of Information Technology under Grant nos. J201706, J201701; the Scientific Research Foundation for Advanced Talents of Chengdu University of Information Technology under Grant nos. KYTZ201717, KYTZ201715, KYTZ201750; Guangdong Key Laboratory Project under Grant no. 2017B030314073.

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Authors and Affiliations

  1. School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China

    Yongqing Zhang & Shengjie Ji

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Yongqing Zhang

  3. School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

    Shaojie Qiao

  4. College of Chemistry, Sichuan University, Chengdu, 610064, China

    Yizhou Li

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  1. Yongqing Zhang
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Correspondence to Shaojie Qiao.

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Zhang, Y., Qiao, S., Ji, S. et al. DeepSite: bidirectional LSTM and CNN models for predicting DNA–protein binding. Int. J. Mach. Learn. & Cyber. 11, 841–851 (2020). https://doi.org/10.1007/s13042-019-00990-x

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