GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction
Transcription factors (TFs) are important factors that regulate gene expression. Revealing the mechanism affecting the binding specificity of TFs is the key to understanding gene regulation. Most of the previous studies focus on TF-DNA binding sites at the sequence level, and they seldom utilize the...
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| Format: | Article |
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AIMS Press
2023-07-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023704?viewType=HTML |
| _version_ | 1797691001463111680 |
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| author | Jujuan Zhuang Kexin Feng Xinyang Teng Cangzhi Jia |
| author_facet | Jujuan Zhuang Kexin Feng Xinyang Teng Cangzhi Jia |
| author_sort | Jujuan Zhuang |
| collection | DOAJ |
| description | Transcription factors (TFs) are important factors that regulate gene expression. Revealing the mechanism affecting the binding specificity of TFs is the key to understanding gene regulation. Most of the previous studies focus on TF-DNA binding sites at the sequence level, and they seldom utilize the contextual features of DNA sequences. In this paper, we develop an integrated spatiotemporal context-aware neural network framework, named GNet, for predicting TF-DNA binding signal at single nucleotide resolution by achieving three tasks: single nucleotide resolution signal prediction, identification of binding regions at the sequence level, and TF-DNA binding motif prediction. GNet extracts implicit spatial contextual information with a gated highway neural mechanism, which captures large context multi-level patterns using linear shortcut connections, and the idea of it permeates the encoder and decoder parts of GNet. The improved dual external attention mechanism, which learns implicit relationships both within and among samples, and improves the performance of the model. Experimental results on 53 human TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets shows that GNet outperforms the state-of-the-art methods in the three tasks, and the results of cross-species studies on 15 human and 18 mouse TF datasets of the corresponding TF families indicate that GNet also shows the best performance in cross-species prediction over the competitive methods. |
| first_indexed | 2024-03-12T02:08:17Z |
| format | Article |
| id | doaj.art-80b8b9b797594baf8e24b15451563d15 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-12T02:08:17Z |
| publishDate | 2023-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-80b8b9b797594baf8e24b15451563d152023-09-07T01:00:41ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-07-01209158091582910.3934/mbe.2023704GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution predictionJujuan Zhuang0Kexin Feng1Xinyang Teng 2Cangzhi Jia3School of Science, Dalian Maritime University, Dalian, Liaoning 116026, ChinaSchool of Science, Dalian Maritime University, Dalian, Liaoning 116026, ChinaSchool of Science, Dalian Maritime University, Dalian, Liaoning 116026, ChinaSchool of Science, Dalian Maritime University, Dalian, Liaoning 116026, ChinaTranscription factors (TFs) are important factors that regulate gene expression. Revealing the mechanism affecting the binding specificity of TFs is the key to understanding gene regulation. Most of the previous studies focus on TF-DNA binding sites at the sequence level, and they seldom utilize the contextual features of DNA sequences. In this paper, we develop an integrated spatiotemporal context-aware neural network framework, named GNet, for predicting TF-DNA binding signal at single nucleotide resolution by achieving three tasks: single nucleotide resolution signal prediction, identification of binding regions at the sequence level, and TF-DNA binding motif prediction. GNet extracts implicit spatial contextual information with a gated highway neural mechanism, which captures large context multi-level patterns using linear shortcut connections, and the idea of it permeates the encoder and decoder parts of GNet. The improved dual external attention mechanism, which learns implicit relationships both within and among samples, and improves the performance of the model. Experimental results on 53 human TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets shows that GNet outperforms the state-of-the-art methods in the three tasks, and the results of cross-species studies on 15 human and 18 mouse TF datasets of the corresponding TF families indicate that GNet also shows the best performance in cross-species prediction over the competitive methods.https://www.aimspress.com/article/doi/10.3934/mbe.2023704?viewType=HTMLtranscription factor binding sitegated highway neural networkencoder-decoder architectureexternal attention mechanism |
| spellingShingle | Jujuan Zhuang Kexin Feng Xinyang Teng Cangzhi Jia GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction Mathematical Biosciences and Engineering transcription factor binding site gated highway neural network encoder-decoder architecture external attention mechanism |
| title | GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction |
| title_full | GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction |
| title_fullStr | GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction |
| title_full_unstemmed | GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction |
| title_short | GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction |
| title_sort | gnet an integrated context aware neural framework for transcription factor binding signal at single nucleotide resolution prediction |
| topic | transcription factor binding site gated highway neural network encoder-decoder architecture external attention mechanism |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023704?viewType=HTML |
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