通过因果关系感知Graph-Transformer深度学习解码压力特定转录调节

IF 4.5 Q1 PLANT SCIENCES
Umesh Bhati , Akanksha Sharma , Sagar Gupta , Anchit Kumar , Upendra Kumar Pradhan , Ravi Shankar
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引用次数: 0

摘要

细胞通过转录因子(tf)的转录重编程来响应环境刺激,转录因子解释顺式调控DNA序列,以确定基因表达的时间和位置。TFs的多样化及其与顺式调控元件(CREs)的相互作用是植物通过形成基因调控网络(grn)来适应逆境的基础。然而,通过选择性TF结合时空基因表达来破译条件特异性grn仍然是植物生物学的主要挑战。为了解释这一点,本研究提出了一个新的计算框架,旨在解释TF相互作用的时空动态。利用超过23TB的多组学数据(ChIP-seq、RNA-seq和蛋白质-蛋白质相互作用),建立了一个贝叶斯因果网络系统。它能够解释TF在不同条件下对拟南芥的条件结合。这些网络经过大量实验数据的验证,成为Graph Transformer深度学习系统的输入。为110种非生物应激相关TF建立了模型,可以直接从RNA-seq数据中准确检测TF结合的条件特异性,而无需单独的ChIP-seq实验。该方法,CTF-BIND在针对来自各种条件的大量实验建立的数据进行测试时,达到了~ 93 %的高平均精度。它是作为一个交互式的、开放访问的web服务器和数据库来实现的,它可以捕捉监管途径中的动态变化。CTF-BIND通过深度学习彻底改变了TF条件特异性结合识别,为ChIP-seq提供了经济有效的替代方案。它有望加速作物改良策略的研究。CTF-BIND作为web服务器可在https://hichicob.ihbt.res.in/ctfbind/免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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/.
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来源期刊
Current Plant Biology
Current Plant Biology Agricultural and Biological Sciences-Plant Science
CiteScore
10.90
自引率
1.90%
发文量
32
审稿时长
50 days
期刊介绍: Current Plant Biology aims to acknowledge and encourage interdisciplinary research in fundamental plant sciences with scope to address crop improvement, biodiversity, nutrition and human health. It publishes review articles, original research papers, method papers and short articles in plant research fields, such as systems biology, cell biology, genetics, epigenetics, mathematical modeling, signal transduction, plant-microbe interactions, synthetic biology, developmental biology, biochemistry, molecular biology, physiology, biotechnologies, bioinformatics and plant genomic resources.
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