BigQ: a NoSQL based framework to handle genomic variants in i2b2

Area of Science:

• Bioinformatics
• Genomic Data Management
• Precision Medicine

Background:

• Precision medicine necessitates integrating clinical and molecular data.
• High-throughput genomic data management is crucial for identifying genomic signatures linked to human phenotypes.
• The Informatics for Integrating Biology and the Bedside (i2b2) framework facilitates clinical data use for research and precision medicine.

Purpose of the Study:

• To advance the i2b2 framework by developing efficient management solutions for Next Generation Sequencing (NGS) data.
• To integrate patient clinical phenotypes with genomic variant profiles from NGS data.
• To enable dynamic querying of integrated clinical and genomic data for research.

Main Methods:

• Development of BigQ, an extension to the i2b2 framework.
• Implementation of a document-based database for managing genomic variant annotations.
• Creation of a visual programming i2b2 plugin for filtering and retrieving genomic variants based on annotations.

Main Results:

• BigQ successfully integrates patient clinical phenotypes with NGS-generated genomic variant profiles.
• The visual programming plugin allows cohort variant retrieval via annotation filtering.
• System evaluation demonstrated linear scalability in query time and disk space with over 11 million variants.

Conclusions:

• A novel i2b2 web service, BigQ, efficiently manages genomic variant annotations.
• The system integrates clinical and genetic data, supporting precision medicine research.
• BigQ provides a scalable solution for managing and querying the growing volume of genomic variant data.