scholarly journals Quality Control of Quantitative High Throughput Screening Data

2019 ◽  
Vol 10 ◽  
Author(s):  
Keith R. Shockley ◽  
Shuva Gupta ◽  
Shawn F. Harris ◽  
Soumendra N. Lahiri ◽  
Shyamal D. Peddada
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Screening

Purifying the Masses:  Integrating Prepurification Quality Control, High-Throughput LC/MS Purification, and Compound Plating To Feed High-Throughput Screening

2005 ◽  
Vol 7 (2) ◽  
pp. 210-217 ◽  
Author(s):  
John J. Isbell ◽  
Yingyao Zhou ◽  
Christina Guintu ◽  
Matthew Rynd ◽  
Shumei Jiang ◽  
...  
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Screening ◽  
The Masses

Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies

2020 ◽  
Author(s):  
Xiaohua Douglas Zhang ◽  
Dandan Wang ◽  
Shixue Sun ◽  
Heping Zhang
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Screening ◽  
Theoretical Basis ◽  
Sampling Error ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Automation Technology ◽  
General Public License

Abstract Motivation High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. Results In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. Availability and implementation The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. Supplementary information Supplementary data are available at Bioinformatics online.

NMR-Based Quality Control Approach for the Identification of False Positives and False Negatives in High Throughput Screening

2006 ◽  
Vol 3 (2) ◽  
pp. 115-124 ◽  
Author(s):  
Claudio Dalvit ◽  
Dannica Caronni ◽  
Nicola Mongelli ◽  
Marina Veronesi ◽  
Anna Vulpetti
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Screening ◽  
False Positives ◽  
False Negatives ◽  
Control Approach

scholarly journals Statistical and Graphical Methods for Quality Control Determination of High-Throughput Screening Data

2003 ◽  
Vol 8 (6) ◽  
pp. 624-633 ◽  
Author(s):  
Bert Gunter ◽  
Christine Brideau ◽  
Bill Pikounis ◽  
Andy Liaw
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Screening ◽  
Software Tool ◽  
Quality Data ◽  
Assay Sensitivity ◽  
Protein Targets ◽  
Modern Drug ◽  
Assay Performance ◽  
Server Software

High-throughput screening (HTS) is used in modern drug discovery to screen hundreds of thousands to millions of compounds on selected protein targets. It is an industrial-scale process relying on sophisticated automation and state-of-the-art detection technologies. Quality control (QC) is an integral part of the process and is used to ensure good quality data and mini mize assay variability while maintaining assay sensitivity. The authors describe new QC methods and show numerous real examples from their biologist-friendly Stat Server® HTS application, a custom-developed software tool built from the commercially available S-PLUS® and Stat Server® statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables. It allows users to visualize HTS data and examine assay performance during the HTS campaign to quickly react to or avoid quality problems.

scholarly journals Development of an in-line magnetometer for flow chemistry and its demonstration for magnetic nanoparticle synthesis

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Maximilian O. Besenhard ◽  
Dai Jiang ◽  
Quentin A. Pankhurst ◽  
Paul Southern ◽  
Spyridon Damilos ◽  
...  
Keyword(s):  
Quality Control ◽  
Magnetic Nanoparticles ◽  
High Throughput ◽  
High Throughput Screening ◽  
Magnetic Nanoparticle ◽  
Nanoparticle Synthesis ◽  
Flow Chemistry ◽  
Continuous Quality ◽  
Highly Sensitive

A highly sensitive magnetometer for flow chemistry to characterise magnetic nanoparticles in solution, in situ and in real-time is presented. This facilitates continuous quality control and high-throughput screening of magnetic nanoparticle syntheses.

scholarly journals An Automatic Quality Control Pipeline for High-Throughput Screening Hit Identification

2016 ◽  
Vol 21 (8) ◽  
pp. 832-841 ◽  
Author(s):  
Yufeng Zhai ◽  
Kaisheng Chen ◽  
Yang Zhong ◽  
Bin Zhou ◽  
Edward Ainscow ◽  
...  
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
In Silico Analysis ◽  
Systematic Errors ◽  
Structure Activity ◽  
Automated Quality Control ◽  
Hit Identification ◽  
Silico Analysis

The correction or removal of signal errors in high-throughput screening (HTS) data is critical to the identification of high-quality lead candidates. Although a number of strategies have been previously developed to correct systematic errors and to remove screening artifacts, they are not universally effective and still require fair amount of human intervention. We introduce a fully automated quality control (QC) pipeline that can correct generic interplate systematic errors and remove intraplate random artifacts. The new pipeline was first applied to ~100 large-scale historical HTS assays; in silico analysis showed auto-QC led to a noticeably stronger structure-activity relationship. The method was further tested in several independent HTS runs, where QC results were sampled for experimental validation. Significantly increased hit confirmation rates were obtained after the QC steps, confirming that the proposed method was effective in enriching true-positive hits. An implementation of the algorithm is available to the screening community.

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