Dataset Information

PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants.

ABSTRACT: Characterizing regulatory effects of genomic variants in plants remains a challenge. Although several tools based on deep-learning models and large-scale chromatin-profiling data have been available to predict regulatory elements and variant effects, no dedicated tools or web services have been reported in plants. Here, we present PlantDeepSEA as a deep learning-based web service to predict regulatory effects of genomic variants in multiple tissues of six plant species (including four crops). PlantDeepSEA provides two main functions. One is called Variant Effector, which aims to predict the effects of sequence variants on chromatin accessibility. Another is Sequence Profiler, a utility that performs 'in silico saturated mutagenesis' analysis to discover high-impact sites (e.g., cis-regulatory elements) within a sequence. When validated on independent test sets, the area under receiver operating characteristic curve of deep learning models in PlantDeepSEA ranges from 0.93 to 0.99. We demonstrate the usability of the web service with two examples. PlantDeepSEA could help to prioritize regulatory causal variants and might improve our understanding of their mechanisms of action in different tissues in plants. PlantDeepSEA is available at http://plantdeepsea.ncpgr.cn/.

SUBMITTER: Zhao H

PROVIDER: S-EPMC8262748 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundStructural genomic variants (SVs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider only Single Nucleotide Polymorphisms (SNPs). Deep Learning (DL) is a promising strategy for genomic prediction, but its performance using SVs and SNPs as genetic markers remains unknown.ResultsWe used rice to investigate whether combining SVs and SNPs can result in better trait prediction over SNPs alone and examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models. Specifically, the performances of BayesC (considering additive effects) and a Bayesian Reproducible Kernel Hilbert space (RKHS) regression (considering both additive and non-additive effects) were compared to those of two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network, to explore their prediction ability by using various marker input strategies. We found that exploiting structural and nucleotide variation slightly improved prediction ability on complex traits in 87% of the cases. DL models outperformed Bayesian models in 75% of the studied cases, considering the four traits and the two validation strategies used. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models.ConclusionsOur study reveals that the use of structural genomic variants can improve trait prediction in rice, independently of the methodology used. Also, our results suggest that Deep Learning (DL) networks can perform better than Bayesian models in the prediction of binary traits, and in quantitative traits when the training and target sets are not closely related. This highlights the potential of DL to enhance crop improvement in specific scenarios and the importance to consider SVs in addition to SNPs in genomic selection.

Dataset Information

PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants.

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