AACR 2017

Diagnosing and Classifying Cancer Using AI

Read Brief on AI

Come View Our Posters

And Meet Our Scientists

Presenting data on predictive modeling and PDX analysis

Comprehensive assessment of mouse contamination removal strategies from patient-derived xenograft model sequencing data

April 4th, 8am-noon
Poster: Section 23, #3586

Novel feature selection strategies for enhancing predictive modeling and deep learning in the biosciences

April 4th, 1-5pm
Poster: Section 24, #4539

Learn about novel TCGA analysis tools at booth #1050

Novel TCGA Visualization and Analysis Tools

Wuxi NextCODE has developed a novel Tumor Mutation Analyzer (TMA) powered by our Genomic Ordered Relational (GOR) database architecture. Fully integrated with our germline Clinical Sequence Analyzer, it provides a uniquely fast, intuitive, and visual means to analyze and compare next-generation sequence data from tumor-normal pairs, tumor-only and PDX analysis.

  • Visualize DNA, RNA, miRNA sequencing, variant calls, copy number variation, and methylation tracks all in one view
  • Rapidly analyze entire TCGA datasets
  • Annotate results ‘on the fly’
  • Validate mutation calls

Tumor Mutation Analyzer

The Tumor mutation analyzer rapidly combines the results of multiple variant callers, such as MuTect and VarScan2 to identify the most promising cancer variants/genes while simultaneously annotating hits. This tool provides modules for performing standard analyses and the ability to perform high-level custom queries. Data from cancer databases, such as COSMIC, may be accessed and interrogated together with user results.

The results are prioritized according to:

  • Functional mutational classes
  • Variants in actionable tumor genes and pathways
  • Drug class of the gene and variant
  • Known tumor genes and recurrent mutations

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