Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice

被引:3
|
作者
Smet, Dajo [1 ,2 ]
Opdebeeck, Helder [1 ,2 ]
Vandepoele, Klaas [1 ,2 ,3 ]
机构
[1] Univ Ghent, Dept Plant Biotechnol & Bioinformat, Ghent, Belgium
[2] VIB, Ctr Plant Syst Biol, Ghent, Belgium
[3] Univ Ghent, Bioinformat Inst Ghent, Ghent, Belgium
来源
关键词
rice; regulatory elements; regulation of heat stress; regulation of drought stress; machine learning interpretation; GENE-EXPRESSION; ARABIDOPSIS; NETWORKS; E2F;
D O I
10.3389/fpls.2023.1212073
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Plants have evolved various mechanisms to adapt to adverse environmental stresses, such as the modulation of gene expression. Expression of stress-responsive genes is controlled by specific regulators, including transcription factors (TFs), that bind to sequence-specific binding sites, representing key components of cis-regulatory elements and regulatory networks. Our understanding of the underlying regulatory code remains, however, incomplete. Recent studies have shown that, by training machine learning (ML) algorithms on genomic sequence features, it is possible to predict which genes will transcriptionally respond to a specific stress. By identifying the most important features for gene expression prediction, these trained ML models allow, in theory, to further elucidate the regulatory code underlying the transcriptional response to abiotic stress. Here, we trained random forest ML models to predict gene expression in rice (Oryza sativa) in response to heat or drought stress. Apart from thoroughly assessing model performance and robustness across various input training data, the importance of promoter and gene body sequence features to train ML models was evaluated. The use of enriched promoter oligomers, complementing known TF binding sites, allowed us to gain novel insights in DNA motifs contributing to the stress regulatory code. By comparing genomic feature importance scores for drought and heat stress over time, general and stress-specific genomic features contributing to the performance of the learned models and their temporal variation were identified. This study provides a solid foundation to build and interpret ML models accurately predicting transcriptional responses and enables novel insights in biological sequence features that are important for abiotic stress responses.
引用
收藏
页数:18
相关论文
共 50 条
  • [21] Predicting Hit Music using MIDI features and Machine Learning
    Rajyashree, R.
    Anand, Anmol
    Soni, Yash
    Mahajan, Harshitaa
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON COMMUNICATION AND ELECTRONICS SYSTEMS (ICCES 2018), 2018, : 94 - 98
  • [22] Predicting superagers: a machine learning approach utilizing gut microbiome features
    Kim, Ha Eun
    Kim, Bori R.
    Hong, Sang Hi
    Song, Seung Yeon
    Jeong, Jee Hyang
    Kim, Geon Ha
    FRONTIERS IN AGING NEUROSCIENCE, 2024, 16
  • [23] A tongue features fusion approach to predicting prediabetes and diabetes with machine learning
    Li, Jun
    Yuan, Pei
    Hu, Xiaojuan
    Huang, Jingbin
    Cui, Longtao
    Cui, Ji
    Ma, Xuxiang
    Jiang, Tao
    Yao, Xinghua
    Li, Jiacai
    Shi, Yulin
    Bi, Zijuan
    Wang, Yu
    Fu, Hongyuan
    Wang, Jue
    Lin, Yenting
    Pai, ChingHsuan
    Guo, Xiaojing
    Zhou, Changle
    Tu, Liping
    Xu, Jiatuo
    JOURNAL OF BIOMEDICAL INFORMATICS, 2021, 115
  • [24] An Optimized Approach for Predicting Water Quality Features Based on Machine Learning
    Suwadi, Nur Afyfah
    Derbali, Morched
    Sani, Nor Samsiah
    Lam, Meng Chun
    Arshad, Haslina
    Khan, Imran
    Kim, Ki-Il
    WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022, 2022
  • [25] Predicting the success of startups using a machine learning approach
    Mona Razaghzadeh Bidgoli
    Iman Raeesi Vanani
    Mehdi Goodarzi
    Journal of Innovation and Entrepreneurship, 13 (1)
  • [26] Predicting the performance of a heat sink utilized with an energy storage unit using machine learning approach
    Salari, Ali
    Ahmadi, Rojin
    Vafadaran, Mohammad Shahab
    Shakibi, Hamid
    Sardarabadi, Mohammad
    JOURNAL OF ENERGY STORAGE, 2024, 83
  • [28] Predicting narcissistic personality traits from brain and psychological features: A supervised machine learning approach
    Jornkokgoud, Khanitin
    Baggio, Teresa
    Faysal, Md
    Bakiaj, Richard
    Wongupparaj, Peera
    Job, Remo
    Grecucci, Alessandro
    SOCIAL NEUROSCIENCE, 2023, 18 (05) : 257 - 270
  • [29] Predicting sunspot number from topological features in spectral images I: Machine learning approach
    Sierra-Porta, D.
    Tarazona-Alvarado, M.
    Acevedo, D. D. Herrera
    ASTRONOMY AND COMPUTING, 2024, 48
  • [30] Predicting health outcomes from time series features of affect dynamics: a machine learning approach
    Roque, Nelson
    Ram, Nilam
    Ong, Anthony
    PSYCHOTHERAPY AND PSYCHOSOMATICS, 2019, 88 : 110 - 110