Motivation The organization of the genome into domains plays a central role in gene expression and other cellular activities. Researchers identify genomic domains mainly through two views: 1D functional assays such as ChIP-seq, and chromatin conformation assays such as Hi-C. Fully understanding domains requires integrative modeling that combines these two views. However, the predominant form of integrative modeling uses segmentation and genome annotation (SAGA) along with the rigid assumption that loci in contact are more likely to share the same domain type, which is not necessarily true for epigenomic domain types and genome-wide chromatin interactions. Results Here, we present an integrative approach that annotates domains using both 1D functional genomic signals and Hi-C measurements of genome-wide 3D interactions without the use of a pairwise prior. We do so by using a graph embedding to learn structural features corresponding to each genomic region, then inputting learned structural features along with functional genomic signals to a SAGA algorithm. We show that our domain types recapitulate well-known subcompartments with an additional granularity that distinguishes a combination of the spatial and functional states of the genomic regions. In particular, we identified a division of the previously-identified A2 subcompartment such that the divided domain types have significantly varying expression levels. Availability https://github.com/nedashokraneh/IChDA Supplementary information Supplementary information

The organization of the genome into domains plays a central role in gene expression and other cellular activities. Researchers identify genomic domains mainly through two views: 1D functional assays such as ChIP-seq, and chromatin conformation assays such as Hi-C. Fully understanding domains requires integrative modeling that combines these two views. However, the predominant form of integrative modeling uses segmentation and genome annotation (SAGA) along with the rigid assumption that loci in contact are more likely to share the same domain type, which is not necessarily true for epigenomic domain types and genome-wide chromatin interactions. Here, we present an integrative approach that annotates domains using both 1D functional genomic signals and Hi-C measurements of genome-wide 3D interactions without the use of a pairwise prior. We do so by using a graph embedding to learn structural features corresponding to each genomic region, then inputting learned structural features along with functional genomic signals to a SAGA algorithm. We show that our domain types recapitulate well-known subcompartments with an additional granularity that distinguishes a combination of the spatial and functional states of the genomic regions. In particular, we identified a division of the previously-identified A2 subcompartment such that the divided domain types have significantly varying expression levels.

Motivation Increasingly complex omics datasets are being generated, along with associated diverse categories of metadata (environmental, clinical, etc.). Looking at the correlation between these variables can be critical to identify potential confounding factors and novel relationships. To date, some correlation globe software has been developed to aid investigations, however they lack secure, dynamic visualisation capability. Results GlobeCorr.ca is a web-based application designed to provide user-friendly, interactive visualisation, and analysis of correlation datasets. Users load tabular data listing pairwise variables and their correlation values, and GlobeCorr creates a dynamic visualisation using ribbons to represent positive and negative correlations, optionally grouped by domain/category (such as microbiome taxa against other metadata). GlobeCorr runs securely (locally on a user’s computer) and provides a simple method for users to visualise and summarise complex datasets. This tool is applicable to a wide range of disciplines and domains of interest, including the bioinformatics/microbiome and metadata examples provided within. Availability and Implementation See https://GlobeCorr.ca, Code provided under an open source MIT license: https://github.com/brinkmanlab/globecorr Supplementary information Supplementary data are available at Bioinformatics online.