1. CBioPortal http://www.cbioportal.org/index.do Web resource for exploring, visualizing, and analyzing multidimentional cancer genomics data

2. cBioPortal: Purpose and Advantages reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. Allow researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise.

3. What you need to get started Google Chrome, Firefox 3.0 and above, Safari, and Internet Explorer 9.0 and above. Java Runtime Environment: necessary for launching the Integrative Genomics Viewer (IGV), available at http://www.java.com/getjava/ http://www.java.com/getjava/ Adobe PDF Reader: necessary for viewing the Pathology Reports and for viewing many of the downloadable files, http://get.adobe.com/reader/ http://get.adobe.com/reader/ (Vector graphic editor: necessary for visualizing and editing the SVG file of OncoPrints downloaded from the cBioPortal, http://inkscape.org/ (free)or http://www.adobe.com/products/illustrator.html) http://inkscape.org/ http://www.adobe.com/products/illustrator.html

4. cBioPortal: Utilities visualize patterns of gene alterations across samples in a cancer study compare gene alteration frequencies across multiple cancer studies summarize all relevant genomic alterations in an individual tumor sample supports biological pathway exploration, survival analysis, analysis of mutual exclusivity between genomic alterations, selective data download, programmatic access, and publication-quality summary visualization

5. cBioPortal: Data type available somatic mutations, DNA copy-number alterations (CNAs), mRNA and microRNA (miRNA) expression, DNA methylation, protein abundance, and phosphoprotein abundance. Source of these data: Cancer Cell Line Encyclopedia (CCLE), TCGA integrate multiple data types at the gene level and then query for the presence of specific biological events (genetic mutation, gene homozygous deletion, gene amplification, increased or decreased mRNA or miRNA expression, and increased or decreased protein abundance) in each sample.

6. cBioPortal: Example Searches TP53 KRAS EGFR Explore

7. Oncomine bioinformatics initiative aimed at collecting, standardizing, analyzing, and delivering cancer transcriptome data to the biomedical research community genes, pathways, and networks deregulated across 18,000 cancer gene expression microarrays, spanning the majority of cancer types and subtypes

8. What you need to get started Java script Oncomine Login

9. Oncomine: examples EGFR, pathway associated Co-expression analysis bookmark to save your searches

10. Type of questions that can be answered with oncomine Differential expression Co-expression Outlier analysis: what genes might be good biomarkers for cancer subtypes? What gene expression patterns or gene sets are validated across multiple datasets? Concept (GO) List: Can patient subtypes be associated with this signature or gene list Representing underlying biology? Concept Associations: What genes are over-expressed in a cancer subtype and are members of a literature- defined biological concept?