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O'Reilly PG, Wen Q, Bankhead P, et al. QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics. BMC Bioinformatics. 2016;17(1):198doi: 10.1186/s12859-016-1062-1.
O'Reilly, P. G., Wen, Q., Bankhead, P., Dunne, P. D., McArt, D. G., McPherson, S., Hamilton, P. W., Mills, K. I., & Zhang, S. D. (2016). QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics. BMC bioinformatics, 17(1), 198. https://doi.org/10.1186/s12859-016-1062-1
O'Reilly, Paul G, et al. "QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics." BMC bioinformatics vol. 17,1 (2016): 198. doi: https://doi.org/10.1186/s12859-016-1062-1
O'Reilly PG, Wen Q, Bankhead P, Dunne PD, McArt DG, McPherson S, Hamilton PW, Mills KI, Zhang SD. QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics. BMC Bioinformatics. 2016 May 04;17(1):198. doi: 10.1186/s12859-016-1062-1. PMID: 27143038; PMCID: PMC4855472.
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BMC Bioinformatics. 2016 May 04;17(1):198. doi: 10.1186/s12859-016-1062-1.

QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics.

BMC bioinformatics

Paul G O'Reilly, Qing Wen, Peter Bankhead, Philip D Dunne, Darragh G McArt, Suzanne McPherson, Peter W Hamilton, Ken I Mills, Shu-Dong Zhang

Affiliations

  1. Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, BT9 7AE, UK.
  2. Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, BT9 7AE, UK. [email protected].
  3. Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, BT9 7AE, UK. [email protected].
  4. Northern Ireland Centre for Stratified Medicine, University of Ulster, C-TRIC Building, Altnagelvin Hospital campus, Glenshane Road, Derry/Londonderry, BT47 6SB, UK. [email protected].

PMID: 27143038 PMCID: PMC4855472 DOI: 10.1186/s12859-016-1062-1

Abstract

BACKGROUND: Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds, which may be further investigated as candidates for repurposing to treat diseases. The public release of voluminous data from the Library of Integrated Cellular Signatures (LINCS) programme further enhanced the utilities and potentials of gene expression connectivity mapping in biomedicine.

RESULTS: We describe QUADrATiC ( http://go.qub.ac.uk/QUADrATiC ), a user-friendly tool for the exploration of gene expression connectivity on the subset of the LINCS data set corresponding to FDA-approved small molecule compounds. It enables the identification of compounds for repurposing therapeutic potentials. The software is designed to cope with the increased volume of data over existing tools, by taking advantage of multicore computing architectures to provide a scalable solution, which may be installed and operated on a range of computers, from laptops to servers. This scalability is provided by the use of the modern concurrent programming paradigm provided by the Akka framework. The QUADrATiC Graphical User Interface (GUI) has been developed using advanced Javascript frameworks, providing novel visualization capabilities for further analysis of connections. There is also a web services interface, allowing integration with other programs or scripts.

CONCLUSIONS: QUADrATiC has been shown to provide an improvement over existing connectivity map software, in terms of scope (based on the LINCS data set), applicability (using FDA-approved compounds), usability and speed. It offers potential to biological researchers to analyze transcriptional data and generate potential therapeutics for focussed study in the lab. QUADrATiC represents a step change in the process of investigating gene expression connectivity and provides more biologically-relevant results than previous alternative solutions.

Keywords: Big data; Bioinformatics; Computational biology; Connectivity mapping; Drug discovery; Multicore programming; Repurposing

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