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Bioinformatics Advance Access originally published online on April 26, 2007
Bioinformatics 2007 23(13):1648-1657; doi:10.1093/bioinformatics/btm145
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

An efficient method for the detection and elimination of systematic error in high-throughput screening

Vladimir Makarenkov 1,*, Pablo Zentilli 1, Dmytro Kevorkov 1, Andrei Gagarin 1, Nathalie Malo 2,3 and Robert Nadon 2,4

1Department d’informatique, Université du Québec à Montreal, C.P.8888, s. Centre Ville, Montreal, QC, Canada, H3C 3P8, 2McGill University and Genome Quebec Innovation Centre, 740 Dr. Penfield Ave., Montreal, QC, Canada, H3A 1A4, 3Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1020 Pine Av. West, Montreal, QC, Canada, H3A 1A4 and 4Department of Human Genetics, McGill University, 1205 Dr. Penfield Ave., N5/13, Montreal, QC, Canada, H3A 1B1

*To whom correspondence should be addressed.


  Abstract

Motivation: High-throughput screening (HTS) is an early-stage process in drug discovery which allows thousands of chemical compounds to be tested in a single study. We report a method for correcting HTS data prior to the hit selection process (i.e. selection of active compounds). The proposed correction minimizes the impact of systematic errors which may affect the hit selection in HTS. The introduced method, called a well correction, proceeds by correcting the distribution of measurements within wells of a given HTS assay. We use simulated and experimental data to illustrate the advantages of the new method compared to other widely-used methods of data correction and hit selection in HTS.

Contact: makarenkov.vladimir{at}uqam.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Jonathan Wren

Received on December 7, 2006; revised on February 22, 2007; accepted on April 10, 2007

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