Mariner: Explore the Hi-Cs**
Author(s): Eric Scott Davis,Manjari Kiran,Sarah M Parker,Nicole Kramer,Douglas Phanstiel
Affiliation(s): The University of North Carolina at Chapel Hill
Social media: https://twitter.com/ericscottdavis1
3D chromatin structure plays an integral, yet incompletely understood role in the long-distance regulation of genes by enhancers and repressors. Disruption or aberrant formation of these long-range interactions can result in developmental abnormalities and diseases, such as cancer. Therefore, deriving biological insights from 3D chromatin structure experiments, such as Hi-C or Micro-C, is essential for understanding and correcting human disease. A major barrier to understanding 3D chromatin structure is a lack of flexible and cohesive software for exploring Hi-C data. Existing software packages are unable to flexibly query, aggregate, and visualize Hi-C data all in one ecosystem. Here, we describe mariner an R/Bioconductor package for interfacing with and extracting biologically relevant findings from Hi-C and Micro-C datasets. mariner carries out all downstream steps of processed Hi-C data analysis including manipulation of paired ranges, generating paired ranges from single-ranged data, flexibly querying and aggregating interaction data, and custom visualizations. Because mariner uses and extends common Bioconductor classes, it can be easily integrated into existing workflows and is compatible with genomic visualization platforms such as plotgardener. We provide multiple software vignettes and workflows with detailed examples of Hi-C analysis with mariner.