Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning
Cells respond to environmental stimuli through transcriptional reprogramming orchestrated by transcription factors (TFs) which interpret cis-regulatory DNA sequences to determine the timing and locations of gene expression. The diversification of TFs and their interactions with cis-regulatory elemen...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Current Plant Biology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214662825000891 |
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| author | Umesh Bhati Akanksha Sharma Sagar Gupta Anchit Kumar Upendra Kumar Pradhan Ravi Shankar |
| author_facet | Umesh Bhati Akanksha Sharma Sagar Gupta Anchit Kumar Upendra Kumar Pradhan Ravi Shankar |
| author_sort | Umesh Bhati |
| collection | DOAJ |
| description | Cells respond to environmental stimuli through transcriptional reprogramming orchestrated by transcription factors (TFs) which interpret cis-regulatory DNA sequences to determine the timing and locations of gene expression. The diversification of TFs and their interactions with cis-regulatory elements (CREs) underpins plant adaptation to stress through the formation of gene regulatory networks (GRNs). However, deciphering condition-specific GRNs through selective TF bindings for spatio-temporal gene expression remains major challenge in plant biology. To decipher that the present study brings forward a novel computational framework designed to reason about the spatio-temporal dynamics of TF interaction. Leveraging over ∼23TB of multi-omics data (ChIP-seq, RNA-seq, and protein-protein interaction), a system of Bayesian causal networks was raised. It is capable of explaining TF’s conditional bindings across diverse conditions for Arabidopsis. These networks, validated against extensive experimental data, became input to a Graph Transformer deep learning system. Models were developed for 110 abiotic stress-related TFs, enabling accurate condition-specific detection of TF binding directly from RNA-seq data, bypassing the need for separate ChIP-seq experiments. The approach, CTF-BIND achieved a high average accuracy of ∼93 % when tested against a large volume of experimentally established data from various conditions. It is implemented as an interactive, open-access web server and database which captures dynamic shifts in regulatory pathways. CTF-BIND revolutionizes TF condition-specific binding identification with deep-learning, offering a cost-effective alternative to ChIP-seq. It is expected to accelerate the research towards crop improvement strategies. CTF-BIND is freely available as a web server at https://hichicob.ihbt.res.in/ctfbind/. |
| format | Article |
| id | doaj-art-8162029c2d404199865a392a8a13104d |
| institution | Kabale University |
| issn | 2214-6628 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Plant Biology |
| spelling | doaj-art-8162029c2d404199865a392a8a13104d2025-08-20T03:41:40ZengElsevierCurrent Plant Biology2214-66282025-09-014310052110.1016/j.cpb.2025.100521Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learningUmesh Bhati0Akanksha Sharma1Sagar Gupta2Anchit Kumar3Upendra Kumar Pradhan4Ravi Shankar5Studio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, HP 176061, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, IndiaStudio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, HP 176061, IndiaStudio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, HP 176061, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, IndiaStudio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, HP 176061, IndiaICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, Delhi, IndiaStudio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, HP 176061, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India; Corresponding author at: Studio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, HP 176061, India.Cells respond to environmental stimuli through transcriptional reprogramming orchestrated by transcription factors (TFs) which interpret cis-regulatory DNA sequences to determine the timing and locations of gene expression. The diversification of TFs and their interactions with cis-regulatory elements (CREs) underpins plant adaptation to stress through the formation of gene regulatory networks (GRNs). However, deciphering condition-specific GRNs through selective TF bindings for spatio-temporal gene expression remains major challenge in plant biology. To decipher that the present study brings forward a novel computational framework designed to reason about the spatio-temporal dynamics of TF interaction. Leveraging over ∼23TB of multi-omics data (ChIP-seq, RNA-seq, and protein-protein interaction), a system of Bayesian causal networks was raised. It is capable of explaining TF’s conditional bindings across diverse conditions for Arabidopsis. These networks, validated against extensive experimental data, became input to a Graph Transformer deep learning system. Models were developed for 110 abiotic stress-related TFs, enabling accurate condition-specific detection of TF binding directly from RNA-seq data, bypassing the need for separate ChIP-seq experiments. The approach, CTF-BIND achieved a high average accuracy of ∼93 % when tested against a large volume of experimentally established data from various conditions. It is implemented as an interactive, open-access web server and database which captures dynamic shifts in regulatory pathways. CTF-BIND revolutionizes TF condition-specific binding identification with deep-learning, offering a cost-effective alternative to ChIP-seq. It is expected to accelerate the research towards crop improvement strategies. CTF-BIND is freely available as a web server at https://hichicob.ihbt.res.in/ctfbind/.http://www.sciencedirect.com/science/article/pii/S2214662825000891Abiotic stressTranscriptional regulationCondition-specific TF-bindingBayesian networksDeep learning |
| spellingShingle | Umesh Bhati Akanksha Sharma Sagar Gupta Anchit Kumar Upendra Kumar Pradhan Ravi Shankar Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning Current Plant Biology Abiotic stress Transcriptional regulation Condition-specific TF-binding Bayesian networks Deep learning |
| title | Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning |
| title_full | Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning |
| title_fullStr | Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning |
| title_full_unstemmed | Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning |
| title_short | Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning |
| title_sort | decoding stress specific transcriptional regulation by causality aware graph transformer deep learning |
| topic | Abiotic stress Transcriptional regulation Condition-specific TF-binding Bayesian networks Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2214662825000891 |
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