Literature DB >> 17463024

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

Vladimir Makarenkov1, Pablo Zentilli, Dmytro Kevorkov, Andrei Gagarin, Nathalie Malo, Robert Nadon.   

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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mesh:

Year:  2007        PMID: 17463024     DOI: 10.1093/bioinformatics/btm145

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  22 in total

1.  Hybrid median filter background estimator for correcting distortions in microtiter plate data.

Authors:  Paul J Bushway; Behrad Azimi; Susanne Heynen-Genel; Jeffrey H Price; Mark Mercola
Journal:  Assay Drug Dev Technol       Date:  2010-04       Impact factor: 1.738

2.  An automated high throughput screening-compatible assay to identify regulators of stem cell neural differentiation.

Authors:  Laura Casalino; Dario Magnani; Sandro De Falco; Stefania Filosa; Gabriella Minchiotti; Eduardo J Patriarca; Dario De Cesare
Journal:  Mol Biotechnol       Date:  2012-03       Impact factor: 2.695

3.  Enhancing the rate of scaffold discovery with diversity-oriented prioritization.

Authors:  S Joshua Swamidass; Bradley T Calhoun; Joshua A Bittker; Nicole E Bodycombe; Paul A Clemons
Journal:  Bioinformatics       Date:  2011-06-17       Impact factor: 6.937

Review 4.  Impact of high-throughput screening in biomedical research.

Authors:  Ricardo Macarron; Martyn N Banks; Dejan Bojanic; David J Burns; Dragan A Cirovic; Tina Garyantes; Darren V S Green; Robert P Hertzberg; William P Janzen; Jeff W Paslay; Ulrich Schopfer; G Sitta Sittampalam
Journal:  Nat Rev Drug Discov       Date:  2011-03       Impact factor: 84.694

Review 5.  HTS/HCS to screen molecules able to maintain embryonic stem cell self-renewal or to induce differentiation: overview of protocols.

Authors:  Genesia Manganelli; Ugo Masullo; Stefania Filosa
Journal:  Stem Cell Rev Rep       Date:  2014-12       Impact factor: 5.739

6.  Rank ordering plate data facilitates data visualization and normalization in high-throughput screening.

Authors:  Chand S Mangat; Amrita Bharat; Sebastian S Gehrke; Eric D Brown
Journal:  J Biomol Screen       Date:  2014-05-14

7.  Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy.

Authors:  Kristina J Allan; Douglas J Mahoney; Stephen D Baird; Charles A Lefebvre; David F Stojdl
Journal:  J Vis Exp       Date:  2018-04-03       Impact factor: 1.355

8.  Utility-aware screening with clique-oriented prioritization.

Authors:  S Joshua Swamidass; Bradley T Calhoun; Joshua A Bittker; Nicole E Bodycombe; Paul A Clemons
Journal:  J Chem Inf Model       Date:  2011-12-20       Impact factor: 4.956

9.  Development and validation of a high-content bimolecular fluorescence complementation assay for small-molecule inhibitors of HIV-1 Nef dimerization.

Authors:  Jerrod A Poe; Laura Vollmer; Andreas Vogt; Thomas E Smithgall
Journal:  J Biomol Screen       Date:  2013-11-26

10.  NoiseMaker: simulated screens for statistical assessment.

Authors:  Phoenix Kwan; Amanda Birmingham
Journal:  Bioinformatics       Date:  2010-08-11       Impact factor: 6.937

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