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Deoxyribonucleic acid (DNA) methylation (DNAm) is an important epigenetic mechanism that plays a role in chromatin structure and transcriptional regulation. Elucidating the relationship between DNAm and gene expression is of great importance for understanding its role in transcriptional regulation. The conventional approach is to construct machine-learning-based methods to predict gene expression based on mean methylation signals in promoter regions. However, this type of strategy only explains about 25% of gene expression variation, and hence is inadequate in elucidating the relationship between DNAm and transcriptional activity. In addition, using mean methylation as input features neglects the heterogeneity of cell populations that can be reflected by DNAm haplotypes. We here developed TRAmaHap, a novel deep-learning framework that predicts gene expression by utilizing the characteristics of DNAm haplotypes in proximal promoters and distal enhancers. Using benchmark data of human and mouse normal tissues, TRAmHap shows much higher accuracy than existing machine-learning based methods, by explaining 60~80% of gene expression variation across tissue types and disease conditions. Our model demonstrated that gene expression can be accurately predicted by DNAm patterns in promoters and long-range enhancers as far as 25 kb away from transcription start site, especially in the presence of intra-gene chromatin interactions. [ABSTRACT FROM AUTHOR]

The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional distribution and evolution process. However, the existing automated methods to characterize yardangs are of limited generalization that may only be feasible for specific types of yardangs in certain areas. Deep learning methods, which are superior in representation learning, provide potential solutions for mapping yardangs with complex and variable features. In this study, we apply Mask region-based convolutional neural networks (Mask R-CNN) to automatically delineate and classify yardangs using very high spatial resolution images from Google Earth. The yardang field in the Qaidam Basin, northwestern China is selected to conduct the experiments and the method yields mean average precisions of 0.869 and 0.671 for intersection of union (IoU) thresholds of 0.5 and 0.75, respectively. The manual validation results on images of additional study sites show an overall detection accuracy of 74%, while more than 90% of the detected yardangs can be correctly classified and delineated. We then conclude that Mask R-CNN is a robust model to characterize multi-scale yardangs of various types and allows for the research of the morphological and evolutionary aspects of aeolian landform. [ABSTRACT FROM AUTHOR]