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Mining drought-responsive TF-TAG modules through a cross-research combinatorial analysis of ATAC-seq and RNA-seq based on deep neural networks in rice
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  • Jingpeng Liu,
  • Xuexiang Cen,
  • Lixian Lin,
  • Ximiao Shi,
  • Xiaowei Wang,
  • Zhongxian Chen,
  • Yu Zhang,
  • Xiangzi Zheng,
  • Binghua Wu,
  • Ying Miao
Jingpeng Liu
Fujian Agriculture and Forestry University
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Xuexiang Cen
Fujian Agriculture and Forestry University
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Lixian Lin
Fujian Agriculture and Forestry University
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Ximiao Shi
Fujian Agriculture and Forestry University
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Xiaowei Wang
Fujian Agriculture and Forestry University
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Zhongxian Chen
Fujian Agriculture and Forestry University
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Yu Zhang
Fujian Agriculture and Forestry University
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Xiangzi Zheng
Fujian Agriculture and Forestry University
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Binghua Wu
Fujian Agriculture and Forestry University
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Ying Miao
Fujian Agriculture and Forestry University

Corresponding Author:[email protected]

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Abstract

not-yet-known not-yet-known not-yet-known unknown Drought is a critical risk factor that impacts rice growth and yields. Previous studies have focused on the regulatory roles of individual transcription factors in response to drought stress. However, there is limited understanding of multi-factor stresses gene regulatory networks and their mechanisms of action. In this study, we utilized data from the jaspar database to compile a comprehensive dataset of transcription factors and their binding sites in rice, Arabidopsis, and barley genomes. We employed the pytorch framework for machine learning to develop a 9-layer convolutional deep neural network TFBind; its accuracy was 90% through literature review and dual-luciferase assay validation. Subsequently, we obtained rice RNA-seq and ATAC-seq data related to abiotic stress from the public database. Utilizing integrative analysis of WGCNA and ATAC-seq, we effectively identified transcription factors associated with open chromatin regions in response to drought. Interestingly, only 81% of the transcription factors directly bound to the opened genes by testing with TFBind model. By this approach we identified 15 drought-responsive transcription factors corresponding to open chromatin regions of targets, which enriched in the terms related to protein transport, protein allocation, nitrogen compound transport. This approach provides a valuable tool for predicting TF-TAG modules during biological processes.
Submitted to Plant, Cell & Environment
Submission Checks Completed
Assigned to Editor
Reviewer(s) Assigned
21 Jun 2024Review(s) Completed, Editorial Evaluation Pending
06 Jul 2024Reviewer(s) Assigned
02 Aug 2024Editorial Decision: Revise Minor
29 Sep 20241st Revision Received
09 Oct 2024Submission Checks Completed
09 Oct 2024Assigned to Editor
11 Oct 2024Review(s) Completed, Editorial Evaluation Pending
13 Oct 2024Reviewer(s) Assigned