iCREPCP: A deep learning-based web server for identifying base-resolution cis-regulatory elements within plant core promoters

Deng, Kaixuan; Zhang, Qizhe; Hong, Yuxin; Yan, Jianbing; Hu, Xuehai

doi:10.1016/j.xplc.2022.100455

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…To build a model with increased predictive power, we turned to a convolutional neural network with a DenseNet architecture (Huang et al 2017), because this approach had worked well with Plant STARR-seq data previously (Deng et al 2023). Our DenseNet model uses the sequence of a terminator as an input and predicts the strength of this sequence in tobacco leaves and maize protoplasts.…”

Section: Resultsmentioning

confidence: 99%

“…For our second model, we built a convolutional neural net model using EUGENe (version 0.0.6) (Klie et al 2022) and PyTorch ((Paszke et al 2019); version 1.11.0) in Python (version 3.8.10). We used a “DenseNet” (Huang et al 2017) architecture adapted from iCREPCP (Deng et al 2023). The model takes one-hot encoded DNA as an input which is fed to a convolutional layer with 128 filters and a kernel size of 5.…”

Section: Methodsmentioning

confidence: 99%

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Arabidopsisand Maize Terminator Strength is Determined by GC Content, Polyadenylation Motifs and Cleavage Probability

Gorjifard

Jores

Tonnies

et al. 2023

Preprint

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The 3' end of a gene, often called a terminator, modulates mRNA stability, localization, translation, and polyadenylation. Here, we adapted Plant STARR-seq, a massively parallel reporter assay, to measure the activity of over 50,000 terminators from the plants Arabidopsis thaliana and Zea mays. We characterize thousands of plant terminators, including many that outperform bacterial terminators commonly used in plants. Terminator activity is species-specific, differing in tobacco leaf and maize protoplast assays. While recapitulating known biology, our results reveal the relative contributions of polyadenylation motifs to terminator strength. We built a computational model to predict terminator strength and used it to conduct in silico evolution that generated optimized synthetic terminators. Additionally, we discover alternative polyadenylation sites across tens of thousands of terminators; however, the strongest terminators tend to have a dominant cleavage site. Our results establish features of plant terminator function and identify strong naturally occurring and synthetic terminators.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Arabidopsisand Maize Terminator Strength is Determined by GC Content, Polyadenylation Motifs and Cleavage Probability

Gorjifard

Jores

Tonnies

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Akagi and colleagues construct an interpretable convolutional neural network (CNN) model, by using cistrome datasets, to predict genome-wide expression patterns in tomato and then experimentally validated the impacts of the predicted key sites on fruit ripening initiation by mutating the nucleotide residues (Akagi et al 2022). Additionally, iCREPCP, a deep learning-based platform, identifies critical CREs in the plant core promoter sequences with base-level resolution, thereby providing important candidate targets for genome editing (Deng et al 2023). Zhou et al (2023) combined regressionbased methodology with empirical knowledge to develop a weighted average prediction algorithm, which is able to estimate the potential impact of editing different regions of the promoter on gene expression.…”

Section: Mining Cis-regulatory Elements and Key Sitesmentioning

confidence: 99%

Integrating machine learning and genome editing for crop improvement

Chen,

Liu,

Zhang

2024

aBIOTECH

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Genome editing is a promising technique that has been broadly utilized for basic gene function studies and trait improvements. Simultaneously, the exponential growth of computational power and big data now promote the application of machine learning for biological research. In this regard, machine learning shows great potential in the refinement of genome editing systems and crop improvement. Here, we review the advances of machine learning to genome editing optimization, with emphasis placed on editing efficiency and specificity enhancement. Additionally, we demonstrate how machine learning bridges genome editing and crop breeding, by accurate key site detection and guide RNA design. Finally, we discuss the current challenges and prospects of these two techniques in crop improvement. By integrating advanced genome editing techniques with machine learning, progress in crop breeding will be further accelerated in the future.

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Advancing plant biology through deep learning-powered natural language processing

Peng,

Rajjou

2024

Plant Cell Rep

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iCREPCP: A deep learning-based web server for identifying base-resolution cis-regulatory elements within plant core promoters

Cited by 6 publications

References 8 publications

Arabidopsisand Maize Terminator Strength is Determined by GC Content, Polyadenylation Motifs and Cleavage Probability

Arabidopsisand Maize Terminator Strength is Determined by GC Content, Polyadenylation Motifs and Cleavage Probability

Integrating machine learning and genome editing for crop improvement

Advancing plant biology through deep learning-powered natural language processing

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