Unveiling human origins of replication using deep learning: accurate prediction and comprehensive analysis

被引:3
|
作者
Yin, Zhen-Ning [1 ]
Lai, Fei-Liao [1 ]
Gao, Feng [1 ,2 ,3 ]
机构
[1] Tianjin Univ, Dept Phys, Sch Sci, Tianjin 300072, Peoples R China
[2] Tianjin Univ, Frontiers Sci Ctr Synthet Biol, Tianjin 300072, Peoples R China
[3] Tianjin Univ, Minist Educ, Key Lab Syst Bioengn, Tianjin 300072, Peoples R China
基金
中国国家自然科学基金;
关键词
human genome; origin of replication; deep learning; Z-curve method; DNA-REPLICATION; INITIATION; DATABASE; IDENTIFICATION; SEQUENCES; CANCER;
D O I
10.1093/bib/bbad432
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Accurate identification of replication origins (ORIs) is crucial for a comprehensive investigation into the progression of human cell growth and cancer therapy. Here, we proposed a computational approach Ori-FinderH, which can efficiently and precisely predict the human ORIs of various lengths by combining the Z-curve method with deep learning approach. Compared with existing methods, Ori-FinderH exhibits superior performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.9616 for K562 cell line in 10-fold cross-validation. In addition, we also established a cross-cell-line predictive model, which yielded a further improved AUC of 0.9706. The model was subsequently employed as a fitness function to support genetic algorithm for generating artificial ORIs. Sequence analysis through iORI-Euk revealed that a vast majority of the created sequences, specifically 98% or more, incorporate at least one ORI for three cell lines (Hela, MCF7 and K562). This innovative approach could provide more efficient, accurate and comprehensive information for experimental investigation, thereby further advancing the development of this field.
引用
收藏
页数:9
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