Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species

Abstract
In eukaryotes, polyadenylation (poly(A)) is an essential process during mRNA maturation. Identifying the cis-determinants of poly(A) signal (PAS) on the DNA sequence is the key to understand the mechanism of translation regulation and mRNA metabolism. Although machine learning methods were widely used in computationally identifying PAS, the need for tremendous amounts of annotation data hinder applications of existing methods in species without experimental data on PAS. Therefore, cross-species PAS identification, which enables the possibility to predict PAS from untrained species, naturally becomes a promising direction. In our works, we propose a novel deep learning method named Poly(A)-DG for cross-species PAS identification. Poly(A)-DG consists of a Convolution Neural Network-Multilayer Perceptron (CNN-MLP) network and a domain generalization technique. It learns PAS patterns from the training species and identifies PAS in target species without re-training. To test our method, we use three species and build cross-species training sets with two of them and evaluate the performance of the remaining one. Moreover, we test our method against insufficient data and imbalanced data issues and demonstrate that Poly(A)-DG not only outperforms state-of-the-art methods but also maintains relatively high accuracy when it comes to a smaller or imbalanced training set.

Citation
Zheng, Y., Wang, H., Zhang, Y., Gao, X., Xing, E. P., & Xu, M. (2020). Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species. PLOS Computational Biology, 16(11), e1008297. doi:10.1371/journal.pcbi.1008297

Acknowledgements
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/2602-01 and URF/1/3007-01. This work was supported in part by U.S. National Institutes of Health (NIH) grants P41-GM103712, R01-GM134020, R01-GM093156, and P30-DA035778. This work was supported in part by U.S. National Science Foundation (NSF) grant DBI-1949629 and IIS-2007595. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher
Public Library of Science (PLoS)

Journal
PLOS Computational Biology

DOI
10.1371/journal.pcbi.1008297

PubMed ID
33151940

Additional Links
https://dx.plos.org/10.1371/journal.pcbi.1008297

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