scholarly journals Breeze: an integrated quality control and data analysis application for high-throughput drug screening

2020 ◽  
Vol 36 (11) ◽  
pp. 3602-3604 ◽  
Author(s):  
Swapnil Potdar ◽  
Aleksandr Ianevski ◽  
John-Patrick Mpindi ◽  
Dmitrii Bychkov ◽  
Clément Fiere ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Complete Solution ◽  
Supplementary Information ◽  
Source Analysis ◽  
Technical Documentation ◽  
Resistance Patterns ◽  
Analysis Application

Abstract Summary High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose–response curve fitting and quantification of the drug responses along with interactive visualization of the results. Availability and implementation The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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.

Evaluating Real-Life High-Throughput Screening Data

2005 ◽  
Vol 10 (2) ◽  
pp. 99-107 ◽  
Author(s):  
Philip Gribbon ◽  
Richard Lyons ◽  
Philip Laflin ◽  
Joe Bradley ◽  
Chris Chambers ◽  
...  
Keyword(s):  
Information Technology ◽  
Quality Control ◽  
Data Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Real Life ◽  
Multiple Views ◽  
Concerted Effort ◽  
Screening Process ◽  
Reductionist Approach

High-throughput screening (HTS) is the result of a concerted effort of chemistry, biology, information technology, and engineering. Many factors beyond the biology of the assay influence the quality and outcome of the screening process, yet data analysis and quality control are often focused on the analysis of a limited set of control wells and the calculated values derived from these wells. Taking into account the large number of variables and the amount of data generated, multiple views of the screening data are necessary to guarantee quality and validity of HTS results. This article does not aim to give an exhaustive outlook on HTS data analysis but tries to illustrate the shortfalls of a reductionist approach focused on control wells and give examples for further analysis.

Data Analysis of High-Throughput Screening Results:  Application of Multidomain Clustering to the NCI Anti-HIV Data Set

2002 ◽  
Vol 45 (14) ◽  
pp. 3082-3093 ◽  
Author(s):  
Susan Y. Tamura ◽  
Patricia A. Bacha ◽  
Heather S. Gruver ◽  
Ruth F. Nutt
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Data Set ◽  
Anti Hiv

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

scholarly journals Alternative Statistical Parameter for High-Throughput Screening Assay Quality Assessment

2007 ◽  
Vol 12 (2) ◽  
pp. 229-234 ◽  
Author(s):  
Yunxia Sui ◽  
Zhijin Wu
Keyword(s):  
Data Analysis ◽  
Quality Assessment ◽  
High Throughput ◽  
High Throughput Screening ◽  
Statistical Parameter ◽  
Direct Analysis ◽  
Quality Parameters ◽  
Screening Assay ◽  
Active Compounds ◽  
High Throughput Screening Assay

High-throughput screening is an essential process in drug discovery. The ability to identify true active compounds depends on the high quality of assays and proper analysis of data. The Z factor, presented by Zhang et al. in 1999, provides an easy and useful summary of assay quality and has been a widely accepted standard. However, as data analysis has undergone much improvement recently, the assessment of assay quality has not evolved in parallel. In this article, the authors study the implications of Z factor values under different conditions and link the Z factor with the power of discovering true active compounds. They discuss the different interpretations of Z factor depending on error distributions and advocate direct analysis of power as assay quality assessment. They also propose that in estimating assay quality parameters, adjustments in data analysis should be taken into account. Studying the power of identifying true “hits” gives a more direct interpretation of assay quality and may provide guidance in assay optimization on some occasions.

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
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