GENE2D: A NoSQL Integrated Data Repository of Genetic Disorders Data

Abstract

There are few sources from which to obtain clinical and genetic data for use in research in Saudi Arabia. Numerous obstacles led to the difficulty of integrating these data from silos and scattered sources to provide standardized access to large data sets for patients with common health conditions. To this end, we sought to contribute to this area and offer a practical and easy-to-implement solution. In this paper, we aim to design and implement a “not only SQL” (NoSQL) based integration framework to generate an Integrated Data Repository of Genetic Disorders Data (GENE2D) to integrate data from various genetic clinics and research centers in Saudi Arabia and provide an easy-to-use query interface for researchers to conduct their studies on large datasets. The major components involved in the GENE2D architecture consists of the data sources, the integrated data repository (IDR) as a central database, and the application interface. The IDR uses a NoSQL document store via MongoDB (an open source document-oriented database program) as a backend database. The application interface called Query Builder provides multiple services for data retrieval from the database using a custom query to answer simple or complex research questions. The GENE2D system demonstrates its potential to help grow and develop a national genetic disorders database in Saudi Arabia.

Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross‐sectional analysis of the Saudi human genome program

“…The sharing of genetic data will broaden opportunities for researchers and medical practitioners to accelerate gene therapy discovery, improve the diagnosis of genetic diseases and develop personalized medicine for patients 27 , 34 , 35 . Consistent with this idea, other investigators have called to establish a national genomic data-sharing policy in the SA that allows data to be freely shared among institutions to enhance bio-marker discovery and computational biology analysis, improving the treatment of genetic disease complications 34 , 35 . A lack of a genetic data sharing policy will limit the use, access, and analysis of the SHGP data.…”

Integration of artificial intelligence in clinical laboratory medicine: Advancements and challenges

“…The absence of open data interfaces and data sharing systems leads to the emergence of isolated data repositories. 115,116 Therefore, prior to implementing AI-based analysis, it is imperative to develop dedicated server programs and establish standardized data formats. A notable illustration of this approach is found in the work of Mohn et al, who enhanced the adaptability of existing models across diverse institutions by fine-tuning them using locally sourced data.…”

“…Furthermore, various clinical centers often employ distinct data collection and testing platforms, where elements like barcode size, reagent formula, label type, and data normalization may vary significantly. The absence of open data interfaces and data sharing systems leads to the emergence of isolated data repositories 115,116 . Therefore, prior to implementing AI‐based analysis, it is imperative to develop dedicated server programs and establish standardized data formats.…”