CANCER Genomics: IncorporatING Large Scale Public and Private Cancer DataSETS

While many large-scale cancer genomic datasets are available online for free (i.e from the TCGA Data Portal, ICGC portal, GTEx portal, etc.), the data is not in an easily accessible or standard format.  OncoLand is an Oncology database and visualization software that helps users explore these cancer genomics datasets (either individually or pan-cancer analysis)  using its "Land" technology, allowing researches to more readily power their research.  

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• Correlate mutation status to expression

• List top gene fusions in disease/study

• Detect exact fusion breakpoints for gene fusions

• Create sample groupings on-the-fly and correlate to copy number, expression, and more.

• Expression pattern of gene of interest (RNA-Seq or chip-based)

• Mutation patterns or distributions (RNA or DNA level)

• Integrate Copy Number and Expression data

• Co-mutation frequencies

• Compare normal tissue distribution to tumor or cell line distribution

Public Studies (TCGA and More)

• Thousands of samples across large-scale cancer genomics Oncology clinical and cell line studies.
• Compound and siRNA/shRNA can be used for correlation analysis and visualizations.
• Clinical data allows organization of data by tumor type, histology, survival information, or disease specific end points (i.e Gleason Grade or triple-negative status).
• Omicsoft's pipeline includes re-processing of all available RNA-Seq data, including calls for alternative splicing, gene fusions, and mutations.
• TCGA (The Cancer Genome Atlas)
• International Cancer Genome Consortium 
• TARGET (Childhood cancers)
• More...
• Omicsoft does not provide access to "restricted" data and follows all applicable rules and laws regarding access to controlled datasets.

• Contact sales@omicsoft.com for a full list of currently available datasets.

Private Studies (Customer Datasets)

• Integrate your organization's data into OncoLand.
• Create your own "Land" and provide users the same searches, analytics, and visualizations available with the public data.
• What does this mean in practice?  Many companies have screening data on thousands of compounds.  Use the information found in the patient data to find trends that match up with sensitivity/resistance information of company compounds.
• Use compound sensitivity information to scan for significant mutations, expression correlation, copy number correlation, and more.

• RNA-Seq: EM normalized counts, normalized RPKM, Mutations, Fusions, Exon-level counts, and exon junctions
• DNA-Seq: Germline Mutations and Somatic Mutations
• Expression: Log2 expression, Present/Absent calls, Cross-platform percentiles

• Copy Number: Segment-level, Log2 Ratios
• Protein: RPPA
• Methylation
• Measurements: ic50 values, Sensitive/Resistant status, siRNA/shRNA, AUC, more...