UCSC Cancer Genomics Browser

UCSC Cancer Genomics Browser provides integration, visualization, and analysis of cancer genomics and clinical data to enable comparative and clinically contextual interpretation of genome-wide experimental measurements across samples.

Key Features:

• Data integration: Integrates cancer genomics datasets with associated clinical information and UCSC Genome Browser biological and genetics annotations.
• Whole-genome views: Displays genome-wide experimental measurements across multiple samples in whole-genome context.
• Heatmap tracks: Presents multiple datasets simultaneously as coordinated heatmap tracks to compare data across studies and modalities.
• Data operations: Provides ordering, filtering, aggregating, classifying, and displaying based on clinical characteristics, annotated biological pathways, and user-contributed gene collections.
• Statistical analysis: Includes integrated standard statistical tools for dynamic quantitative analysis across datasets.
• Public data repository: Hosts an expanding repository of publicly accessible cancer genomics data from multiple cancer types, including Cancer Genome Atlas (TCGA) contributions.
• hgMicroscope: Offers tumor image visualization via the hgMicroscope tumor image viewer.
• hgSignature: Performs real-time genomic signature evaluation on any track using hgSignature.
• PARADIGM pathway tracks: Displays integrative pathway activity measures using PARADIGM pathway tracks.

Scientific Applications:

• Comparative genomics: Compare genomic measurements across studies, platforms, and sample cohorts using coordinated heatmap tracks.
• Clinical integration: Correlate genomic alterations with clinical characteristics for translational and clinical-context analyses.
• Signature evaluation: Evaluate and apply genomic signatures in real time to genome-scale data tracks.
• Pathway activity analysis: Assess integrative pathway activities using PARADIGM-derived pathway tracks.
• Histology-genomics correlation: Visualize and relate tumor histopathology images to genomic and pathway data via hgMicroscope.

Methodology:

Computational methods include coordinated heatmap tracks for simultaneous dataset display; ordering, filtering, aggregating, and classification based on clinical characteristics, pathways, and gene collections; integrated standard statistical tools for quantitative analysis; hgSignature for real-time genomic signature evaluation; PARADIGM pathway tracks for integrative pathway activity representation; hgMicroscope for tumor image visualization; and incorporation of UCSC Genome Browser biological and genetic annotations.