bioRxivCancer BiologyPreprint
The Breast Cancer Epigenomics Track Hub
Abstract
BackgroundPioneering research has shown that high-throughput epigenomics assays such as ChlP-seq and ATAC-seq are applicable to patient-derived breast tumor samples. A host of public data has been accumulated since then, which are potentially of high value for basic research as well as personalized medicine. Such data sets constitute encyclopedias of biological knowledge. However, their impact has so far been limited by access obstacles, especially with regard to extraction and visualization of small portions of data that could potentially answer specific questions arising in a research context. ResultsWe developed the breast cancer epigenomics track hub (BC hub), a resource intended to make it easy for occasional users to find, access and view data of their interest. The BC hub harbors ChIP-seq, ATAC-seq and copy number data from breast tumors, normal breast cells, patient-derived xenografts and breast cancer cell lines in a genome browsable track format. The tracks can be accessed via hyperlinks that automatically configure customized views for different interest groups. Here, we present a detailed description of the resource and informative use cases illustrating its potential in answering specific biological questions. ConclusionsWe show that track hubs constitute a powerful way of bringing epigenomics data to the user who could benefit from them. The examples presented highlight the added-value of joint visualization of breast cancer data from different sources. The proof-of-concept provided here exemplifies and underscores the importance of efforts to make biological data FAIR (findable, accessible, interoperable and reusable), and may serve as an encouragement of similar bottom-up initiatives in other research fields. The BC hub is freely accessible at https://bchub.epfl.ch.
- organism: human
- assay: ChIP-seq
- assay: ATAC-seq
- assay: DNA methylation
- research focus: database / resource
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: experimental assay
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