Improved Prediction of Regulatory Element Using Hybrid Abelian Complexity Features with DNA Sequences

International Journal of Molecular Sciences

Xuehai Hu¹  Yunxia Liu¹  Chengchao Wu¹  Jin Chen² 

[1] College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China;College of Science, Huazhong Agricultural University, Wuhan 430070 China;

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

DOI  :  10.3390/ijms20071704

来源: DOAJ

【 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.

【 授权许可】

Unknown