Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species.
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 us...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-11-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008297&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841533259369938944 |
|---|---|
| author | Yumin Zheng Haohan Wang Yang Zhang Xin Gao Eric P Xing Min Xu |
| author_facet | Yumin Zheng Haohan Wang Yang Zhang Xin Gao Eric P Xing Min Xu |
| author_sort | Yumin Zheng |
| collection | DOAJ |
| description | 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 four species and build cross-species training sets with two of them and evaluate the performance of the remaining ones. 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. |
| format | Article |
| id | doaj-art-bab2dcfbd2c042b09ed2b8a7a81f2f08 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2020-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-bab2dcfbd2c042b09ed2b8a7a81f2f082025-01-17T05:30:57ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-11-011611e100829710.1371/journal.pcbi.1008297Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species.Yumin ZhengHaohan WangYang ZhangXin GaoEric P XingMin XuIn 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 four species and build cross-species training sets with two of them and evaluate the performance of the remaining ones. 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008297&type=printable |
| spellingShingle | Yumin Zheng Haohan Wang Yang Zhang Xin Gao Eric P Xing Min Xu 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 |
| title | Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species. |
| title_full | Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species. |
| title_fullStr | Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species. |
| title_full_unstemmed | Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species. |
| title_short | Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species. |
| title_sort | poly a dg a deep learning based domain generalization method to identify cross species poly a signal without prior knowledge from target species |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008297&type=printable |
| work_keys_str_mv | AT yuminzheng polyadgadeeplearningbaseddomaingeneralizationmethodtoidentifycrossspeciespolyasignalwithoutpriorknowledgefromtargetspecies AT haohanwang polyadgadeeplearningbaseddomaingeneralizationmethodtoidentifycrossspeciespolyasignalwithoutpriorknowledgefromtargetspecies AT yangzhang polyadgadeeplearningbaseddomaingeneralizationmethodtoidentifycrossspeciespolyasignalwithoutpriorknowledgefromtargetspecies AT xingao polyadgadeeplearningbaseddomaingeneralizationmethodtoidentifycrossspeciespolyasignalwithoutpriorknowledgefromtargetspecies AT ericpxing polyadgadeeplearningbaseddomaingeneralizationmethodtoidentifycrossspeciespolyasignalwithoutpriorknowledgefromtargetspecies AT minxu polyadgadeeplearningbaseddomaingeneralizationmethodtoidentifycrossspeciespolyasignalwithoutpriorknowledgefromtargetspecies |