PERSONALIZING MEDICINE: ANALYZING NEXT GENERATION SEQUENCING USING A SYMPTOMS-BASED APPROACH ON PUBMED

Abstract

Personalized medicine is the future of healthcare. Let us assume that there is a child with a rare genetic illness that results in cardiac arrest during teenage years. To best diagnose and treat this illness, doctors and scientists have to figure out which one of his 25,000 genes is defective. This modern version of finding a needle in a haystack is costly when patients are suffering.

Next Generation Sequencing (NGS) promises to accelerate this process and revolutionize medicine. Using advanced technologies to identify variants in whole genomes, NGS allows prediction and diagnosis of disease and personalized treatments to the individual. Unfortunately, NGS identifies tens of thousands of variants: some real, some false positive and some false negative. Many variants can be excluded by comparing across genomes and rationalizing using different filter criteria. However, the list of potential variants involved in a given genetic disease remains several thousand long and we are back to looking for a needle in a haystack.

Overcoming this barrier would be a monumental advance in personalized medicine. We propose that the clues to finding the affective gene are hidden in massive mines of biomedical data online. Using this and the genetic information unique to each patient, we created an online platform that creates ‘biograms’ – personalized reports that identify disease causing genes based on the symptoms of a genetic disease.