iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features
Abstract Background Promoters, non-coding DNA sequences located at upstream regions of the transcription start site of genes/gene clusters, are essential regulatory elements for the initiation and regulation of transcriptional processes. Furthermore, identifying promoters in DNA sequences and genome...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-10-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-022-08829-6 |
| _version_ | 1811194884885839872 |
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| author | Thanh-Hoang Nguyen-Vo Quang H. Trinh Loc Nguyen Phuong-Uyen Nguyen-Hoang Susanto Rahardja Binh P. Nguyen |
| author_facet | Thanh-Hoang Nguyen-Vo Quang H. Trinh Loc Nguyen Phuong-Uyen Nguyen-Hoang Susanto Rahardja Binh P. Nguyen |
| author_sort | Thanh-Hoang Nguyen-Vo |
| collection | DOAJ |
| description | Abstract Background Promoters, non-coding DNA sequences located at upstream regions of the transcription start site of genes/gene clusters, are essential regulatory elements for the initiation and regulation of transcriptional processes. Furthermore, identifying promoters in DNA sequences and genomes significantly contributes to discovering entire structures of genes of interest. Therefore, exploration of promoter regions is one of the most imperative topics in molecular genetics and biology. Besides experimental techniques, computational methods have been developed to predict promoters. In this study, we propose iPromoter-Seqvec – an efficient computational model to predict TATA and non-TATA promoters in human and mouse genomes using bidirectional long short-term memory neural networks in combination with sequence-embedded features extracted from input sequences. The promoter and non-promoter sequences were retrieved from the Eukaryotic Promoter database and then were refined to create four benchmark datasets. Results The area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR) were used as two key metrics to evaluate model performance. Results on independent test sets showed that iPromoter-Seqvec outperformed other state-of-the-art methods with AUCROC values ranging from 0.85 to 0.99 and AUCPR values ranging from 0.86 to 0.99. Models predicting TATA promoters in both species had slightly higher predictive power compared to those predicting non-TATA promoters. With a novel idea of constructing artificial non-promoter sequences based on promoter sequences, our models were able to learn highly specific characteristics discriminating promoters from non-promoters to improve predictive efficiency. Conclusions iPromoter-Seqvec is a stable and robust model for predicting both TATA and non-TATA promoters in human and mouse genomes. Our proposed method was also deployed as an online web server with a user-friendly interface to support research communities. Links to our source codes and web server are available at https://github.com/mldlproject/2022-iPromoter-Seqvec . |
| first_indexed | 2024-04-12T00:34:50Z |
| format | Article |
| id | doaj.art-08e33e81a2c047b2ab6f7804ec59d920 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-12T00:34:50Z |
| publishDate | 2022-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-08e33e81a2c047b2ab6f7804ec59d9202022-12-22T03:55:11ZengBMCBMC Genomics1471-21642022-10-0123S511210.1186/s12864-022-08829-6iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded featuresThanh-Hoang Nguyen-Vo0Quang H. Trinh1Loc Nguyen2Phuong-Uyen Nguyen-Hoang3Susanto Rahardja4Binh P. Nguyen5School of Mathematics and Statistics, Victoria University of WellingtonSchool of Information and Communication Technology, Hanoi University of Science and TechnologySchool of Mathematics and Statistics, Victoria University of WellingtonComputational Biology Center, International University - VNU HCMCSchool of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Mathematics and Statistics, Victoria University of WellingtonAbstract Background Promoters, non-coding DNA sequences located at upstream regions of the transcription start site of genes/gene clusters, are essential regulatory elements for the initiation and regulation of transcriptional processes. Furthermore, identifying promoters in DNA sequences and genomes significantly contributes to discovering entire structures of genes of interest. Therefore, exploration of promoter regions is one of the most imperative topics in molecular genetics and biology. Besides experimental techniques, computational methods have been developed to predict promoters. In this study, we propose iPromoter-Seqvec – an efficient computational model to predict TATA and non-TATA promoters in human and mouse genomes using bidirectional long short-term memory neural networks in combination with sequence-embedded features extracted from input sequences. The promoter and non-promoter sequences were retrieved from the Eukaryotic Promoter database and then were refined to create four benchmark datasets. Results The area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR) were used as two key metrics to evaluate model performance. Results on independent test sets showed that iPromoter-Seqvec outperformed other state-of-the-art methods with AUCROC values ranging from 0.85 to 0.99 and AUCPR values ranging from 0.86 to 0.99. Models predicting TATA promoters in both species had slightly higher predictive power compared to those predicting non-TATA promoters. With a novel idea of constructing artificial non-promoter sequences based on promoter sequences, our models were able to learn highly specific characteristics discriminating promoters from non-promoters to improve predictive efficiency. Conclusions iPromoter-Seqvec is a stable and robust model for predicting both TATA and non-TATA promoters in human and mouse genomes. Our proposed method was also deployed as an online web server with a user-friendly interface to support research communities. Links to our source codes and web server are available at https://github.com/mldlproject/2022-iPromoter-Seqvec .https://doi.org/10.1186/s12864-022-08829-6DNATranscription start sitePromoterTATA-boxBidirectional long short-term memory |
| spellingShingle | Thanh-Hoang Nguyen-Vo Quang H. Trinh Loc Nguyen Phuong-Uyen Nguyen-Hoang Susanto Rahardja Binh P. Nguyen iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features BMC Genomics DNA Transcription start site Promoter TATA-box Bidirectional long short-term memory |
| title | iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features |
| title_full | iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features |
| title_fullStr | iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features |
| title_full_unstemmed | iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features |
| title_short | iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features |
| title_sort | ipromoter seqvec identifying promoters using bidirectional long short term memory and sequence embedded features |
| topic | DNA Transcription start site Promoter TATA-box Bidirectional long short-term memory |
| url | https://doi.org/10.1186/s12864-022-08829-6 |
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