DeepPolyA: A Convolutional Neural Network Approach for Polyadenylation Site Prediction

1. DeepPolyA: A Convolutional Neural Network Approach for Polyadenylation Site Prediction Xin Gao, Department of Computer Science, NJIT, Newark, NJ Jie Zhang, Adobe Systems, San Jose, CA Zhi Wei, Department of Computer Science, NJIT, Newark, NJ Hakon Hakonarson, Abramson Research Center, The Children’s Hospital of Philadelphia, PA; Department of Pediatrics, University of Pennsylvania, PA Published in journal of the Institute for Electrical and Electronics Engineers (IEEE), April 2018 Presenter: Wei Chun Chen (John)

2. Overview • Introduction • Methods • Results / Discussion • Conclusion • Future Extension

3. Polyadenylation • Definition: • The addition of a poly(A) tail to the 3’-end of a messenger RNA (mRNA). • A required step for maturation of the mRNA for translation. • It is important for mRNA stability, translational efficiency, and transport. • May associated with immunological, endocrine, and neurological diseases (i.e. Alzheimer Diseases and Parkinson Diseases)

4. DNA (deoxyribonucleic acid) carries genetic information and contains 4 nucleotide bases: Gene Gene DNA

5. DNA transcription Pre-mRNA posttranscriptional modification Mature mRNA translation Protein (amino acids)

6. Polyadenylation: the addition of a poly(A)-tail to the 3’-end of the mRNA. • Poly(A) tail protects mRNA from enzymatic degradation and aids in translation and mRNA transport. • Poly(A) site is the cleavage site. U-rich U/GU-rich ≤ 30 nt Poly(A) tail

7. Alternative Polyadenylation. There are multiple poly(A) sites in a single gene. • To further understand the pattern of gene expression and the underlying mechanism of gene regulation, it is imperative to accurately determine the poly(A) sites. Tian and Manley, Nature Rev Mol Cell Biol, 2016

8. Related Work • Chang et al. proposed a predictive model of SVMs for feature extraction and poly(A) sites prediction in humans. • Ji et al. proposed a framework called poly(A) site classifier for predicting poly(A) sites in plants. • Graber et al. proposed a contextual model using hidden Markov model (HMM) to predict poly(A) sites in yeasts.

9. Significance of this Paper • The authors proposed a CNN-based model named DeepPolyA to predict poly(A) sites by identifying poly(A) signal (AAUAAA) in Arabidopsis (a small flowering plant). • This is the first deep learning based method used for studying in this area. • Advantage: DeepPolyA can automatically learn poly(A)-related motifs (i.e. poly(A) signal) without involving any manual feature engineering.

10. Contribution • DeepPolyA outperforms several neural network methods (RNN, CNN-RNN), deep learning models (DanQ, DeepSEA, VGG) , and popular machine learning approaches (SVM, Bayesian Networks, Random Forest).

11. Figure 1 II. Methods • Neural network architecture of DeepPolyA • One-hot encoding • Two convolution layers Table 1

12. Other Models • Deep Learning: • DeepSEA • DnaQ • VGG • Machine Learning: • SVM • NB • RF Figure 2. (a) RNN (Recurrent Nerual Network) and (b) CNN-RNN model architectures.

13. Model Training and Testing Configure and apply different hyperparameters to train the models. Find parameters (weight) that can minimize the objective function. Evaluate against other models on the test set.

14. Prediction Assessment • Sensitivity – measures the proportion of the true positives that are correctly predicted. • Specificity – measures the proportion of the ture negatives that are correctly predicted. • Accuracy – the closeness of a measurement to the true value • MCC – a correlation coefficient between the observed and predicted binary classifications • AUC – area under the receiver-operating characteristic (ROC) curve • F-score TP = True Positive TN = True Negative FP = False Positive FN = False Negative

15. III. Results / Discussion

16. 97.06% 82.60% 91.28% Figure 3. Prediction performance of DeepPolyA comparing with other methods. 93.01% 89.51% 91.51%

17. Figure 4. (a) ROC curves and (b) Precision-Recall curves of prediction performance among all deep learning methods.

18. Figure 6. Prediction performance of DeepPolyA for alternative genomic context of negative DNA sequence samples. Figure 5. Prediction performance of DeepPolyA for DNA sequence windows of length 54 to 216 nucleotides.

19. Figure 7. Three convolution kernels visualized from JASPAR using TOMTOM. • JASPAR is an open-access database of transcription factor binding profiles. • TOMTOM program compares one or more motifs against a database of known motifs (JASPAR).

20. Figure 8. Saliency map visualization of an entire sequence with a zoom-in view of the sites around poly(A) sites.

21. VI. Conclusion • DeepPolyA outperforms all other methods • DeepPolyA can automatically extract poly(A) signals and features from raw sequence data

22. V. Future Extension • Use DeepPolyA convolutional neural network to predict cis regulatory element which may play a role in polyadenylation. • Or use the combination of poly(A) signal, upstream U-rich region, and downstream U/GU-rich region for poly(A) site prediction.