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胡学海

博士生导师
硕士生导师
教师姓名:胡学海
教师拼音名称:huxuehai
职务:大数据系系主任
职称:教授
学历:博士研究生毕业
学位:理学博士学位
办公地点:逸夫楼C609
电子邮箱:
毕业院校:武汉大学
所属院系:信息学院
所在单位:信息学院
学科:统计学其他专业    生物信息学    
其他联系方式
论文成果
Improved Prediction of Regulatory Element Using Hybrid Abelian Complexity Features with DNA Sequences
发布时间:2021-04-30    点击次数:

影响因子:4.556

DOI码:10.3390/ijms20071704

发表刊物:International Journal of Molecular Sciences

关键字:regulatory element; enhancer; abelian complexity; prediction

摘要:Deciphering the code of cis-regulatory element (CRE) is one of the core issues of current biology. As an important category of CRE, enhancers play crucial roles in gene transcriptional regulations in a distant manner. Further, the disruption of an enhancer can cause abnormal transcription and, thus, trigger human diseases, which means that its accurate identification is currently of broad interest. Here, we introduce an innovative concept, i.e., abelian complexity function (ACF), which is a more complex extension of the classic subword complexity function, for a new coding of DNA sequences. After feature selection by an upper bound estimation and integration with DNA composition features, we developed an enhancer prediction model with hybrid abelian complexity features (HACF). Compared with existing methods, HACF shows consistently superior performance on three sources of enhancer datasets. We tested the generalization ability of HACF by scanning human chromosome 22 to validate previously reported super-enhancers. Meanwhile, we identified novel candidate enhancers which have supports from enhancer-related ENCODE ChIP-seq signals. In summary, HACF improves current enhancer prediction and may be beneficial for further prioritization of functional noncoding variants.

论文类型:期刊论文

学科门类:理学

一级学科:生物学

卷号:20

页面范围:1704

是否译文:

发表时间:2019-01-01

收录刊物:SCI