DeePEL: Deep learning architecture to recognize plncRNA and elncRNA promoters Promoter regions of long noncoding RNA (lncRNA) genes are crucial to understand their transcriptional regulatory pattern. LncRNA genes, being more cryptic than proteincoding genes in terms of their functionality and biogenesis divergence, are lacking in number of existing studies to elucidate the roles of their promoters compared to their counterparts. Based on the overlap between epigenetic marks and transcription start sites, human lncRNAs were categorized into two broad categories: enhanceroriginated lncRNAs (elncRNAs) and promoteroriginated lncRNAs (plncRNAs) and hence these two groups are subject to distinct transcriptional regulatory programs. To understand the difference in the transcriptional regulatory mechanisms that governs p and elncRNAs, we studied the promoter sequences of these two groups of lncRNAs including distinct transcription factor (TF) proteins that favor pover elncRNA (and vice versa). In addition, we developed a convolution neural network (CNN) based deep learning (DL) framework DeePEL (deep p, elncRNA promoter recognizer), to classify the promoter of p and elncRNAs. To the best of our knowledge, this is the first attempt to classify these two groups of lncRNA promoters, using sequence and TF information, based on DL framework. We report several sequence specific signatures in the promoter regions as well as several distinct TFs specific to groups of lncRNAs that will help in understanding the promoterproximal transcriptional regulation of plncRNAs and elncRNAs.

Using Convolutional Neural Network to Study the Regulatory Relationship Between DNA Methylation and Gene Expression In this work, we propose a novel framework based on convolutional neural networks to integrate information of DNA methylation and DNA sequences for studying their relationship with gene expression. Using the data from 8 cell types, we show that the trained models provide higher explained variation of gene expression from DNA methylation (up to 30%) compared to methods that only consider one genomic data type or from one region (up to 12%). In addition, the extracted sequence motifs from the filters of the first convolutional layers reveal the involvement of regulatory modulators including DNA Methyltransferase 1 (DNMT1) and other transcription factors, shedding light on the complex regulatory mechanisms of DNA methylation. We found that more than half of the 20 most enriched motifs correspond to regulators that are well supported by previous publications and ChIPSeq transcription factor binding datasets. Our work has demonstrated the usefulness of convolutional neural networks in revealing complex relationships from omics data.