An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning

Fang, Jing; Zhang, Shaowu; Cao, Zhen; Zhang, Shihua

doi:10.1109/tcbb.2019.2901789

Cited by 23 publications

(10 citation statements)

References 44 publications

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“…As shown in Table 4 and Figure 6, the combined model showed an improved mean AUROC over that of the sequence model (AUROC of 0.603 vs. 0.529), though the result was expected as epigenomics features showed a higher predictive power than sequences from Figure 5. Still, our finding was consistent with other publications showing integrating epigenomic features with sequence data improved the performance over using sequence data alone in predicting epigenomicsrelated features with fully-connected layers or recurrent neural network [20,36,37]. However, the performance was not enhanced over that of the epigenomics model (AUROC of 0.603 vs. 0.648).…”

Section: Combining Epigenomic and Sequence Data: Do We Gain Additionasupporting

confidence: 91%

Local Epigenomic Data are more Informative than Local Genome Sequence Data in Predicting Enhancer-Promoter Interactions Using Neural Networks

Xiao

Zhuang

Pan

2019

Genes

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Enhancer-promoter interactions (EPIs) are crucial for transcriptional regulation. Mapping such interactions proves useful for understanding disease regulations and discovering risk genes in genome-wide association studies. Some previous studies showed that machine learning methods, as computational alternatives to costly experimental approaches, performed well in predicting EPIs from local sequence and/or local epigenomic data. In particular, deep learning methods were demonstrated to outperform traditional machine learning methods, and using DNA sequence data alone could perform either better than or almost as well as only utilizing epigenomic data. However, most, if not all, of these previous studies were based on randomly splitting enhancer-promoter pairs as training, tuning, and test data, which has recently been pointed out to be problematic; due to multiple and duplicating/overlapping enhancers (and promoters) in enhancer-promoter pairs in EPI data, such random splitting does not lead to independent training, tuning, and test data, thus resulting in model over-fitting and over-estimating predictive performance. Here, after correcting this design issue, we extensively studied the performance of various deep learning models with local sequence and epigenomic data around enhancer-promoter pairs. Our results confirmed much lower performance using either sequence or epigenomic data alone, or both, than reported previously. We also demonstrated that local epigenomic features were more informative than local sequence data. Our results were based on an extensive exploration of many convolutional neural network (CNN) and feed-forward neural network (FNN) structures, and of gradient boosting as a representative of traditional machine learning.Genes 2020, 11, 41 2 of 15 Experimental methods based on chromosome conformation capture (3C, 4C, and Hi-C) or extensions that incorporate ChIP-sequencing such as paired-end tag sequencing (ChIA-PET) are, however, costly, and the results are only available for a few cell types [4][5][6][7]. Computational tools offer an alternative by utilizing various DNA sequence and/or epigenomic annotation data to predict EPIs with machine learning models built from experimentally obtained EPI data [8][9][10][11].Whalen, et al. [11] reported that a gradient boosting method, called TargetFinder, accurately distinguished between interacting and non-interacting enhancer-promoter pairs based on epigenomic profiles. They included histone modifications and transcription factor binding (based on ChIP-seq), and DNase I hypersensitive sites (DNase-seq) with a focus on distal interaction (>10 kb) in high resolution. The idea was further extended to predict EPIs solely from local DNA sequence data and achieved high prediction accuracy [12][13][14].In particular, convolutional neural networks (CNNs), known for capturing stationary patterns in data with successful applications in image and text recognition [15,16], were shown to perform well in predicting EPIs based on DNA sequence alone. A natural questi...

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Section: Combining Epigenomic and Sequence Data: Do We Gain Additionasupporting

confidence: 91%

Local Epigenomic Data are more Informative than Local Genome Sequence Data in Predicting Enhancer-Promoter Interactions Using Neural Networks

Xiao

Zhuang

Pan

2019

Genes

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“…Therefore, it is not often used to interpret deep learning model (which are already computing intensive) except when there are few input features. For example, leave-one-feature-out was used to determine that, of five histone marks, removing H3K4me3 resulted in the largest decrease in a deep learning model's ability to predict transcription factor (TF) binding sites [49].…”

Section: Sensitivity Analysismentioning

confidence: 99%

Opening the Black Box: Interpretable Machine Learning for Geneticists

2020

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“…We adopted the metrics of Accuracy, Precision, Recall, F1 score, AUC and AUPR to assess the performance of SEPT. These metrics are defined respectively as the following [48][49][50][51]. where TP is the number of correctly predicted EPIs, TN is the number of correctly predicted non-EPIs, FP is the number of incorrectly predicted EPIs and FN is the number of incorrectly predicted non-EPIs.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Prediction of Enhancer-Promoter Interactions Using the Cross-Cell Information and Domain Adversarial Neural Network

Jing

Zhang

2020

Preprint

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Background: Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent EPI prediction across cell lines can be made based on sequence-level information.Results: In this work, we present a novel Sequence-based method (called SEPT) to predict the Enhancer-Promoter interactions in the new cell line by using the cross-cell information and Transfer learning. SEPT first leans the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer leaning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC (the area under the receiver operating curves).Conclusion: SEPT is an effective method for predicting EPIs in the new cell line. Domain adversarial architecture of transfer leaning used in SEPT can learn the latent EPI features shared among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction.

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An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning

Cited by 23 publications

References 44 publications

Local Epigenomic Data are more Informative than Local Genome Sequence Data in Predicting Enhancer-Promoter Interactions Using Neural Networks

Local Epigenomic Data are more Informative than Local Genome Sequence Data in Predicting Enhancer-Promoter Interactions Using Neural Networks

Opening the Black Box: Interpretable Machine Learning for Geneticists

Prediction of Enhancer-Promoter Interactions Using the Cross-Cell Information and Domain Adversarial Neural Network

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