Big Data in Pharmacogenomics: Current Applications, Perspectives and Pitfalls

Abstract

The efficiency of new generation sequencing methods and the reduction of their cost has led pharmacogenomics to gradually supplant pharmacogenetics, leading to new applications in personalized medicine along with new perspectives in drug design or identification of drug response factors. The amount of data generated in genomics fits the definition of big data, and need a specific bioinformatics processing following standard steps: data collection, processing, analysis and interpretation. Pitfalls of pharmacogenomics studies are directly related to these steps. This review aims to describe these steps from a pharmacogenomic point of view, focusing on bioinformatics aspects.

Keywords::

• big data
• bioinformatics
• pharmacogenomics

Financial & competing interests disclosure

C-C Barrot received a PhD grant from Inserm (the French Institute of Health and Medical Research). The authors have no other relevant affiliations or financialinvolvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Author contributions

CCB drafts the manuscript; JBW and NP revised it critically and finally approved a version to be submitted.

Additional information

Funding

C-C Barrot received a PhD grant from Inserm (the French Institute of Health and Medical Research). The authors have no other relevant affiliations or financialinvolvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.