Click:   The Last Update Time:--

胡学海

Supervisor of Doctorate Candidates
Supervisor of Master's Candidates
Name (Simplified Chinese):胡学海
Name (Pinyin):huxuehai
Administrative Position:大数据系系主任
Professional Title:Professor
Education Level:With Certificate of Graduation for Doctorate Study
Degree:Doctoral Degree in Science
Business Address:逸夫楼C609
E-Mail:
Alma Mater:武汉大学
Teacher College:College of Informatics
School/Department:信息学院
Discipline:Other specialties in Statistics    bioinformatics    
Other Contact Information:

Email:

Paper Publications
Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human
Release time:2021-04-30    Hits:

Impact Factor:4.556

DOI number:10.3390/ijms18020420

Journal:International Journal of Molecular Sciences,

Key Words:DNA methylation; predicted model; sequence complexity

Abstract:DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species redictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the xperimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation.

Indexed by:Journal paper

Volume:18

Page Number:420

Translation or Not:no

Date of Publication:2017-01-01

Included Journals:SCI