WashU Epigenome Browser (https://epigenomegateway.wustl.edu/browser/) is a web-based genomic data exploration tool that provides visualization, integration, and analysis of epigenomic datasets. The newly renovated user interface and functions have enabled researchers to engage with the browser and genomic data more efficiently and effectively since 2018. Here, we introduce a new integrated panel design in the browser that allows users to interact with 1D (genomic features), 2D (such as Hi-C), 3D (genome structure), and 4D (time series) data in a single web page. The browser can display three-dimensional chromatin structures with the 3D viewer module. The 4D tracks, called ‘Dynamic’ tracks, animatedly display time-series data, allowing for a more striking visual impact to identify the gene or genomic region candidates as a function of time. Genomic data, such as annotation features, numerical values, and chromatin interaction data can all be viewed in the dynamic track mode. Imaging data from microscopy experiments can also be displayed in the browser. In addition to software development, we continue to service and expand the data hubs we host for large consortia including 4DN, Roadmap Epigenomics, TaRGET and ENCODE, among others. Our growing user/developer community developed additional track types as plugins, such as qBed and dynseq tracks, which extend the utility of the browser. The browser serves as a foundation for additional genomics platforms including the WashU Virus Genome Browser (for COVID-19 research) and the Comparative Genome Browser. The WashU Epigenome Browser can also be accessed freely through Amazon Web Services at https://epigenomegateway.org/.

Medulloblastoma (MB) is the most common malignant brain tumor in infants and children. Four molecular subtypes of MB are recognized: WNT, SHH, Group 3 (G3), and Group 4 (G4). Compared with WNT and SHH subtypes, G3 MBs exhibit significantly worse outcomes and higher metastatic rates, and there is no effective treatment yet. Moreover, G3 and G4 MBs are much more common in boys than girls, i.e., sex bias, which also plays important roles in cancer prognosis and drug response. However, the molecular mechanism of G3 remains unclear, and there are no well-identified biomarker genes associated with these phenotypes, i.e., worse survival rate, higher metastasis rate, and sex bias. In this exploratory study, we aim to identify potential biomarkers associated with the three phenotypes using integrative analysis of gene expression, methylation and copy number variation datasets. In the results, we identified a set of biomarker genes and linked them into a network signature. The network signature showed better performance in the separation of G3 MB patients into subtypes with a significant difference in terms of the three phenotypes. To identify potentially effective drugs for G3 MBs, a set of drugs with diverse targets were prioritized, which can potentially inhibit the network signature. These drugs or combinations thereof might be effective for G3 treatment.