Title :
Regulatory Elements in Low-Methylated Regions Predict Directional Change of Gene Expression
Author :
Hong Hu ; Jingting Xu ; Yang Dai
Author_Institution :
Dept. of Bioeng., Univ. of Illinois at Chicago, Chicago, IL, USA
Abstract :
Recent studies on methylomes obtained from the whole genome bisulfite sequencing (WGBS) indicate that low-methylated regions (LMRs) are related to potential active distal regulatory regions such as enhancers in mammalian genomes. To investigate the potential effect of regulatory elements in LMRs on gene expression, we proposed penalized logistic regression models to predict the directional change of differentially expressed genes using predicted transcription factor binding sites (TFBSs) in LMRs that are distinctive between two cell types. We evaluated our models on four cell types where the WGBS and RNA-seq data were available. The average area under the ROC curve (AUC) from the tenfold cross validation was computed over the six pairs of cell types in each model. The models using TFBSs in LMRs in intergenic or genebody region are more predictive (AUC 0.71 and 0.66, respectively) compared with the one using TFBSs from promoter regions alone (AUC 0.62). When using a model that combines TFBSs in LMRs from both intergenic and genebody regions, the best prediction was obtained (AUC 0.78). Our models are capable of identifying subsets of LMRs that are significantly enriched for the ChIP-seq binding sites of the insulator protein CTCF and p300 co-activator and other transcription factors. Our framework provides further evidence of putative distal regulatory elements from LMRs located in intergenic and genebody regions.
Keywords :
RNA; bioinformatics; cellular biophysics; genetics; genomics; molecular biophysics; molecular configurations; proteins; regression analysis; AUC; ChIP-seq binding sites; LMR; RNA-seq data; TFBS; WGBS; area under the ROC curve; cell types; distal regulatory regions; gene expression; genebody region; insulator protein CTCF; intergenic region; low-methylated regions; mammalian genomes; methylomes; p300 co-activator; penalized logistic regression models; predicted transcription factor binding sites; regulatory elements; transcription factors; whole genome bisulfite sequencing; Bioinformatics; Biological system modeling; Data models; Gene expression; Genomics; Logistics; Pulse width modulation; Gene expression; Low-methylated region; Penalized logistic regression; Transcription factor binding site; low-methylated region (LMR); penalized logistic regression; transcription factor binding site;
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
DOI :
10.1109/JBHI.2015.2431640