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. 2013 Jul;41(Web Server issue):W218-24.
doi: 10.1093/nar/gkt473. Epub 2013 Jun 8.

The UCSC Interaction Browser: Multidimensional Data Views in Pathway Context

Free PMC article

The UCSC Interaction Browser: Multidimensional Data Views in Pathway Context

Christopher K Wong et al. Nucleic Acids Res. .
Free PMC article


High-throughput data sets such as genome-wide protein-protein interactions, protein-DNA interactions and gene expression data have been published for several model systems, especially for human cancer samples. The University of California, Santa Cruz (UCSC) Interaction Browser ( is an online tool for biologists to view high-throughput data sets simultaneously for the analysis of functional relationships between biological entities. Users can access several public interaction networks and functional genomics data sets through the portal as well as upload their own networks and data sets for analysis. Users can navigate through correlative relationships for focused sets of genes belonging to biological pathways using a standard web browser. Using a new visual modality called the CircleMap, multiple 'omics' data sets can be viewed simultaneously within the context of curated, predicted, directed and undirected regulatory interactions. The Interaction Browser provides an integrative viewing of biological networks based on the consensus of many observations about genes and their products, which may provide new insights about normal and disease processes not obvious from any isolated data set.


Figure 1.
Figure 1.
Overview of the UCSC IB workflow. Users select or supply (i) a network of interactions (left box) and (ii) a data set for viewing (right box). The data are viewed for a selected set of genes in the main panel using a CircleMap display (center box). Data for individual samples are displayed as individual ‘tick marks’, different data platforms are displayed as separate rings (‘Full CircleMap’). Samples can be aggregated together into groups, displayed as segments with averaged color hue (‘Aggregated CircleMap’).
Figure 2.
Figure 2.
A toy example illustrating relationship of a CircleMap to a standard heatmap. (A) Left matrix represents DNA methylation data; right matrix mRNA expression data for the same genes (rows) and 10 samples (columns) as for the DNA methylation data. Two genes (gene A and gene B) co-cluster in the DNA methylation data (top) but do not in the mRNA data owing to gene B’s anti-correlation with gene A’s expression. (B) CircleMap shows data across the 10 samples for genes A and B with mRNA expression on the inner ring and DNA methylation on the outer ring. Each ‘spoke’ represents one sample (column) in the heatmap of part A.
Figure 3.
Figure 3.
Case study of the TCGA colorectal adenocarcinoma data set. (A) A zoom-in view of the BRAF oncogene’s CircleMap showing the full detail of all samples as individual spokes. Rings correspond to (inside to outside) somatic missense mutations, copy number estimates from the GISTIC algorithm, PARADIGM pathway inferences and a hypermutation phenotype indicator. (B) Same as in part A but the values within the hypermutated and non-hypermutated groups are averaged within each ring showing the aggregated view. (C) CircleMap display of most of the Wnt-, TGF-beta and PI3K-signaling members regulating MYC oncogene activation. Both curated regulatory links from the UCSC SuperPathway are shown (purple directed links) as well as protein–protein interactions collected from human protein reference database (brown undirected links). All nodes are sorted according to the hypermutated versus non-hypermutated as the primary sort and BRAF mutation as the secondary sort, controllable from the zoom-in on BRAF.

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