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Clin Kidney J. 2016 Jun;9(3):343-52. doi: 10.1093/ckj/sfv155. Epub 2016 Mar 21.

Omics databases on kidney disease: where they can be found and how to benefit from them.

Author information

1
Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Toulouse, France; Université Toulouse III Paul-Sabatier, Toulouse, France.
2
Biotechnology Division, Biomedical Research Foundation Academy of Athens, Athens, Greece; Institute for Molecular Cardiovascular Research, Universitätsklinikum RWTH Aachen, Aachen, Germany.
3
Mosaiques Diagnostics GmbH , Hannover , Germany.
4
BHF Glasgow Cardiovascular Research Centre , University of Glasgow , Glasgow , UK.
5
School of Computer Science , University of Manchester , Manchester , UK.

Abstract

In the recent decades, the evolution of omics technologies has led to advances in all biological fields, creating a demand for effective storage, management and exchange of rapidly generated data and research discoveries. To address this need, the development of databases of experimental outputs has become a common part of scientific practice in order to serve as knowledge sources and data-sharing platforms, providing information about genes, transcripts, proteins or metabolites. In this review, we present omics databases available currently, with a special focus on their application in kidney research and possibly in clinical practice. Databases are divided into two categories: general databases with a broad information scope and kidney-specific databases distinctively concentrated on kidney pathologies. In research, databases can be used as a rich source of information about pathophysiological mechanisms and molecular targets. In the future, databases will support clinicians with their decisions, providing better and faster diagnoses and setting the direction towards more preventive, personalized medicine. We also provide a test case demonstrating the potential of biological databases in comparing multi-omics datasets and generating new hypotheses to answer a critical and common diagnostic problem in nephrology practice. In the future, employment of databases combined with data integration and data mining should provide powerful insights into unlocking the mysteries of kidney disease, leading to a potential impact on pharmacological intervention and therapeutic disease management.

KEYWORDS:

bioinformatics; data integration; kidney disease; omics databases; system biology

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