Leveraging Cloud Computing for Genome Analysis to Provide Patient-Specific HIV Treatments and Insight Into HIV Susceptibility Patterns

Due to its high mutation rate, HIV-1 currently stands as one of the most difficult viruses for treating and vaccinating. Resistance to the limited repertoire of treatments is also well documented. While personalized treatments have been proposed for HIV-1, genome analysis has proven to be cost-prohibitive as the amount of data produced has far exceeded the processing capabilities of conventional servers. Instead of utilizing aging machines at bioinformatics labs, this article proposes that sequence analysis be moved to on-demand servers known as cloud servers as a method of cost containment. By leveraging on-demand computing power, labs are able to cut down on IT personnel and avoid paying for server upkeep when not in use. As the cost of sequence analysis decreases from this shift, I speculate that individuals will be able to obtain an affordable personalized treatment regimen devoid of ineffective drugs by utilizing a centralized cloud database for analysis. The collection of patient and viral genome data can also enable big data analysis. Since cloud computing provides practically limitless computing power, a large archive of HIV-1 patient and viral genomes would open the door for large-scale mathematical modeling of possible HIV-1 mutation patterns. Constructing a map of these patterns to predict certain mutations in an individual before they occur could offer exciting opportunities for drug and vaccine development. With the availability of patient genome data, analysis of genetic markers associated with elite controllers would also become a possibility. Currently, numerous candidate genetic markers thought to be responsible for elite controller status. With the level of analyses afforded by much lareger data collection, cloud computing could be leveraged to reveal these genetic markers. However, when big data are involved, concerns over patient anonymity and database breaches must be addressed.  Because both cloud computing platforms and sequence analysis pipelines are individually well developed, a smooth integration between the two is probable and could lead to better patient quality of life and enormous contributions to scientific knowledge.