High throughput discovery and characterization of tick and pathogen vaccine protective antigens using vaccinomics with intelligent Big Data analytic techniques

ABSTRACT

Introduction: The incidence of tick-borne diseases (TBDs) is growing worldwide, and vaccines appear as the most effective and environmentally sound intervention for the prevention and control of TBDs.

Areas covered: The vaccinomics approach combines omics technologies and bioinformatics for the characterization of tick-host-pathogen molecular interactions and the development of next-generation vaccines. The two main challenges of the vaccinomics approach are the integration and analysis of omics datasets, and the development of screening platforms for the identification of candidate protective antigens. To address these challenges we propose the application of intelligent Big Data analytic techniques for the high throughput discovery and characterization of tick and pathogen derived candidate vaccine protective antigens.

Expert commentary: This innovative approach should improve the development and efficacy of vaccines for the control and prevention of TBDs.

KEYWORDS:

• Tick
• tick-borne diseases
• vaccine
• vaccinomics
• Big Data analytics
• graph theory
• fuzzy deformable prototypes

Declaration of interest

JA Olivas received funding from FEDER and the State Research Agency (AEI) of the Spanish Ministry of Economy and Competition under grant MERINET: TIN2016-76843-C4-2-R (AEI/FEDER, UE). The authors have no other relevant affiliations or financial involvement 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.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This work has been partially supported by FEDER and the State Research Agency (AEI) of the Spanish Ministry of Economy and Competition under grant MERINET: TIN2016-76843-C4-2-R (AEI/FEDER, UE).