Genetic Mapping of Diseases through Big Data Techniques

The development of sophisticated sequencing machines and DNA techniques has enabled advances to be made in the medical field of genetics research. However, due to the large amount of data that sequencers produce, new methods and programs are required to allow an efficient and rapid analysis of the data. MapReduce is a data-intensive computing model that handles large volumes that are easy to program by means of two basic functions (Map and Reduce). This work introduces GMS, a genetic mapping system that can assist doctors in the clinical diagnosis of patients by conducting an analysis of the genetic mutations contained in their DNA. As a result, the model can offer a good method for analyzing the data generated by sequencers, by providing a scalable system that can handle a large amount of data. The use of several medical databases at the same time makes it possible to determine susceptibilities to diseases through big data analysis mechanisms. The results show scalability and offer a possible diagnosis that can improve the genetic diagnosis with a powerful tool for health professionals.

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