The High-Throughput Analyses Era: Are We Ready for the Data Struggle?

Abstract

Recent and rapid technological advances in molecular sciences have dramatically increased the ability to carry out high-throughput studies characterized by big data production. This, in turn, led to the consequent negative effect of highlighting the presence of a gap between data yield and their analysis. Indeed, big data management is becoming an increasingly important aspect of many fields of molecular research including the study of human diseases. Now, the challenge is to identify, within the huge amount of data obtained, that which is of clinical relevance. In this context, issues related to data interpretation, sharing and storage need to be assessed and standardized. Once this is achieved, the integration of data from different -omic approaches will improve the diagnosis, monitoring and therapy of diseases by allowing the identification of novel, potentially actionably biomarkers in view of personalized medicine.

Keywords: -omic sciences; big data; high-throughput analysis; next-generation sequencing; personalized medicine..

Figure 1

The integration between different -omic sciences and validated and standardized tools for big data analysis will bring personalized medicine into real clinical practice.