Big Data in Cancer Genomics

They’re scientists. They’re miners. They dig deep through seemingly endless streams of numbers and terabytes of data to discover hidden gems of understanding about how and why cancer forms. Several recently announced collaborations between academic research institutions and big data analytics vendors powering advanced discovery technologies will bring the power of big data to new studies that may have significant impacts on future therapies and treatments.  With oncology and genomics in the spotlight, personalized medicine is poised to take a leap forward as researchers dive into complex diseases. By analysing data from multiple cancer types, we could evaluate prognostic models and identify gene alterations that led to tumor formation. This wouldn’t have been obtained by looking at tumor data from just one cancer type.

The Center for Big Data in Translational Genomics is a multi-institution partnership coordinated by UC Santa Cruz to create scalable infrastructure for the broad application of genomics in biomedicine. This multinational collaboration between academia and industry will create data models and analysis tools to analyze massive data sets of genomic information. Such tools can be used to analyze genomes and gene expression data from thousands of individuals to uncover the contribution of gene variants to disease, with an initial focus on cancer. This knowledge will be instrumental in the development of precision diagnostic and treatment methods.

At least half of all diseases have a substantial genomic component, often including contributions from millions of individually rare but collectively common genetic variations. Only by studying the genomes and transcriptomes of very large numbers of individuals will scientists have the statistical power to discover and understand this vital aspect of the genomic contribution to disease. For this genomics must be brought into the big data era, making possible the analyses of huge data sets and wide deployment of precision diagnosis and treatment based on genomic information.

The center will make software solutions interoperable by developing standard application programming interfaces (APIs) and tools at multiple levels, from raw sequence data to genetic variation and functional data, through to systems, pathways, and phenotypes. The overriding goal is to create implementations capable of handling genomics datasets that are orders of magnitude larger than those that can now be handled. The APIs and all academic reference implementations will be open source, while several major corporate partners not funded by the project will provide proprietary implementations, creating a competitive ecosystem of interoperable big data genomics software.

  • Data mining in translational biomedical informatics
  • Computational Regulatory Cancer Genomics
  • Computational Regulatory Cancer Genomics
  • Computational methods for cancer sequencing

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