High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis | JoVE Visualize

High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis

Vahan Simonyan¹, Konstantin Chumakov², Hayley Dingerdissen³

¹Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20993, USA vahan.simonyan@fda.hhs.gov vahansim@gmail.com.

Database : the Journal of Biological Databases and Curation|March 19, 2016

View abstract on PubMed

Summary

The High-performance Integrated Virtual Environment (HIVE) offers a secure cloud platform for next-generation sequencing (NGS) data analysis. Its innovative data model and computation approach enhance efficiency and security for researchers.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) generates vast amounts of data, requiring specialized infrastructure for storage and analysis.
Existing distributed computing environments often face challenges with data management and computational efficiency.
Secure and flexible platforms are crucial for collaborative research involving sensitive biological data.

Purpose of the Study:

To introduce the High-performance Integrated Virtual Environment (HIVE) as a solution for NGS data management and analysis.
To highlight HIVE's novel architecture, data model, and computational paradigm.
To emphasize the security, efficiency, and flexibility offered by the HIVE platform.

Main Methods:

Development of a multicomponent cloud infrastructure with secure web access.
Implementation of a cloud control server that virtualizes services, not processes.
Design of the honeycomb data model with a unified application program interface (API) for data manipulation.
Establishment of a hierarchical access control and permission system.

Main Results:

HIVE provides a robust and flexible environment for depositing, retrieving, annotating, and computing on NGS data.
The 'move computation to data' paradigm significantly reduces hardware and network strain.
The honeycomb data model simplifies data integration and minimizes database restructuring.
HIVE ensures efficient and secure NGS analysis through a granular access control system.

Conclusions:

HIVE offers a powerful and secure platform for next-generation sequencing data analysis.
The platform's innovative architecture and data model enhance computational efficiency and data management.
HIVE is suitable for both public and private domain collaborations, fostering scientific advancement.