A Comparative Analysis of Biological Data Integration Systems Famous for Data Exploitation and Knowledge Discovery

Integrating heterogeneous biological databases for unveiling the new intra-molecular and inter-molecular attributes, behaviors, and relationships in the human cellular system has always been a focused research area of computational biology. In this context, a lot of biological data integration systems have been deployed in the last couple of decades. One of the prime and common objectives of all these systems is to better facilitate the end-users for exploring, exploiting, and analyzing the integrated biological data for knowledge extraction. With the advent of especially high-throughput data generation technologies, biological data is growing and dispersing continuously, exponentially, heterogeneously, and geographically. Due to this, biological data integration systems face data integration and data organization-related current and future challenges. The objective of this review is to quantitatively evaluate and compare some of the recent warehouse-based multi-omics data integration systems to check their compliance with the current and future data integration needs. For this, we identified some of the major data integration design characteristics that should be in the multi-omics data integration model to comprehensively address the current and future data integration challenges. Based on these design characteristics and the evaluation criteria, we evaluated some of the recent data warehouse systems and showed categorical and comparative analysis results. Results show that most of the systems exhibit no or partial compliance with the required data integration design characteristics. So, these systems need design improvements to adequately address the current and future data integration challenges while keeping their service level commitments in place.