Literature DB >> 22586178

HCS-Analyzer: open source software for high-content screening data correction and analysis.

Arnaud Ogier1, Thierry Dorval.   

Abstract

MOTIVATION: High-throughput screening is a powerful technology principally used by pharmaceutical industries allowing the identification of molecules of interest within large libraries. Originally target based, cellular assays provide a way to test compounds (or other biological material such as small interfering RNA) in a more physiologically realistic in vitro environment. High-content screening (HCS) platforms are now available at lower cost, giving the opportunity for universities or research institutes to access those technologies for research purposes. However, the amount of information extracted from each experiment is multiplexed and hence difficult to handle. In such context, there is an important need for an easy-to-use, but still powerful software able to manage multidimensional screening data by performing adapted quality control and classification. HCS-analyzer includes: a user-friendly interface specifically dedicated to HCS readouts, an automated approach to identify systematic errors potentially occurring during screening and a set of tools to classify, cluster and identify phenotypes of interest among large and multivariate data. AVAILABILITY: The application, the C# .Net source code, as well as detailed documentation, are freely available at the following URL: http://hcs-analyzer.ip-korea.org.

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Year:  2012        PMID: 22586178     DOI: 10.1093/bioinformatics/bts288

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


  6 in total

1.  SbacHTS: spatial background noise correction for high-throughput RNAi screening.

Authors:  Rui Zhong; Min Soo Kim; Michael A White; Yang Xie; Guanghua Xiao
Journal:  Bioinformatics       Date:  2013-06-28       Impact factor: 6.937

2.  Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data.

Authors:  Robert Krueger; Johanna Beyer; Won-Dong Jang; Nam Wook Kim; Artem Sokolov; Peter K Sorger; Hanspeter Pfister
Journal:  IEEE Trans Vis Comput Graph       Date:  2019-09-10       Impact factor: 4.579

Review 3.  Biologically Relevant Heterogeneity: Metrics and Practical Insights.

Authors:  Albert Gough; Andrew M Stern; John Maier; Timothy Lezon; Tong-Ying Shun; Chakra Chennubhotla; Mark E Schurdak; Steven A Haney; D Lansing Taylor
Journal:  SLAS Discov       Date:  2017-01-06       Impact factor: 3.341

4.  Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738.

Authors:  Stephen Checkley; Linda MacCallum; James Yates; Paul Jasper; Haobin Luo; John Tolsma; Claus Bendtsen
Journal:  Sci Rep       Date:  2015-08-27       Impact factor: 4.379

Review 5.  Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening.

Authors:  Shardul Paricharak; Oscar Méndez-Lucio; Aakash Chavan Ravindranath; Andreas Bender; Adriaan P IJzerman; Gerard J P van Westen
Journal:  Brief Bioinform       Date:  2018-03-01       Impact factor: 11.622

6.  Hepatocellular carcinoma-targeted drug discovery through image-based phenotypic screening in co-cultures of HCC cells with hepatocytes.

Authors:  Jae-Woo Jang; Yeonhwa Song; Kang Mo Kim; Jin-Sun Kim; Eun Kyung Choi; Joon Kim; Haengran Seo
Journal:  BMC Cancer       Date:  2016-10-18       Impact factor: 4.430

  6 in total

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