基因
计算生物学
调节顺序
生物
生物信息学
遗传学
单核苷酸多态性
基因表达调控
基因型
作者
T. H. Li,Hui Xu,Shouzhen Teng,Mingrui Suo,Revocatus Bahitwa,Mingchi Xu,Yiheng Qian,Guillaume P. Ramstein,Baoxing Song,Edward S. Buckler,Hai Wang
标识
DOI:10.1073/pnas.2319811121
摘要
Rational design of plant cis -regulatory DNA sequences without expert intervention or prior domain knowledge is still a daunting task. Here, we developed PhytoExpr, a deep learning framework capable of predicting both mRNA abundance and plant species using the proximal regulatory sequence as the sole input. PhytoExpr was trained over 17 species representative of major clades of the plant kingdom to enhance its generalizability. Via input perturbation, quantitative functional annotation of the input sequence was achieved at single-nucleotide resolution, revealing an abundance of predicted high-impact nucleotides in conserved noncoding sequences and transcription factor binding sites. Evaluation of maize HapMap3 single-nucleotide polymorphisms (SNPs) by PhytoExpr demonstrates an enrichment of predicted high-impact SNPs in cis -eQTL. Additionally, we provided two algorithms that harnessed the power of PhytoExpr in designing functional cis -regulatory variants, and de novo creation of species-specific cis -regulatory sequences through in silico evolution of random DNA sequences. Our model represents a general and robust approach for functional variant discovery in population genetics and rational design of regulatory sequences for genome editing and synthetic biology.
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