Abstract
Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format.Database URL: http://targetmine.mizuguchilab.org
Highlights
The proliferation of high-throughput ‘omics’ experiments has led to a surge in the availability of biomedical data that need to be properly analysed
Cellular networks themselves comprise multiple organizational layers made up of different types of biomolecular interactions such as microRNA– target interactions (MTIs), protein–protein interactions (PPIs) and transcription factor (TF)–target gene interactions, which together modulate the functioning of the living systems
We have previously developed TargetMine, an integrated data warehouse based on the versatile InterMine framework [8, 10, 13], which models biological entities as ‘objects’ and their relationships as ‘references’ to other objects
Summary
The proliferation of high-throughput ‘omics’ experiments has led to a surge in the availability of biomedical data that need to be properly analysed. Leveraging biological information from different data types yields deeper insights into gene function and provides a better understanding of the biological process under study, which can be further transformed into actionable research. Cellular networks themselves comprise multiple organizational layers made up of different types of biomolecular interactions such as microRNA (miRNA)– target interactions (MTIs), protein–protein interactions (PPIs) and transcription factor (TF)–target gene interactions, which together modulate the functioning of the living systems. The ability to correlate these data with gene expression patterns and a priori knowledge of the genetic determinants of various diseases is key to a deeper understanding of disease mechanisms and development of better therapeutic strategies
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