Big Data in Genomics

Big Data holds tremendous promise in the field of Genomics.  It is inherently a Big Data problem, given the size and variety of genetic information.  It enables the detection predisposition towards diseases and may help in the fight against cancer.

One goal of genomic medicine is to understand how an individual’s DNA impacts their risk to different diseases (Leung, Delong, Alipanahi, & Frey, 2016). The idea is to treat the variations as variables and model the problem using machine learning techniques.  An example of a cell variable is the location where a protein binds to a strand of DNA containing a particular gene.

One problem with traditional genomics approaches is that they often rely on brittle, complex, and dated technologies.  The use of Big Data Analytics approach can reduce this risk.  The goal should be to design a scalable and portable system, with the goal of reproducibility in mind.

The application of this technology is on the horizon.  Foundation Medicine was created to enable the type of testing that Steve Jobs did during his battle with cancer (Regalado, 2013).  The company sells a test that sequences the DNA of a person with cancer with the hope that the factors driving cancer can be identified. The gathering of DNA sequences from individuals known to have cancer enables research previously not possible.  Collaboration amongst companies, such as H3 Biomedicine, accessing a common data set can lead to connections between genetic aberrations and disease (Adams, 2017).

The application of Big Data Analytics to Genomics is an exciting area.  As with many Big Data project, it has the potential to yield breakthrough results.  It is also impacted by the same data quality, architectural, and compliance challenges that face other Big Data projects.

References

Adams, B. (2017). H3 Biomedicine boosts its cancer collab with Foundation Medicine | FierceBiotech.   Retrieved from http://www.fiercebiotech.com/biotech/h3-biomedicine-boosts-its-cancer-collab-foundation-medicine

Leung, M. K., Delong, A., Alipanahi, B., & Frey, B. J. (2016). Machine learning in genomic medicine: a review of computational problems and data sets. Proceedings of the IEEE, 104(1), 176-197.

Regalado, A. (2013). Steve Jobs Legacy and the Foundation Medicine IPO.