Z’ Factor including siRNA design quality parameter in RNAi screening experiments
RNA interference (RNAi) high-content screening (HCS) enables massive parallel gene silencing and is increasingly being used to reveal novel connections between genes and disease-relevant phenotypes. The application of genome-scale RNAi relies on the development of high quality HCS assays. The Z’ fac...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
01 May 2012
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| In: |
RNA biology
Year: 2012, Volume: 9, Issue: 5, Pages: 624-632 |
| ISSN: | 1555-8584 |
| DOI: | 10.4161/rna.19759 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.4161/rna.19759 Verlag, Volltext: https://doi.org/10.4161/rna.19759 
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| Author Notes: | Sławomir Mazur and Karol Kozak |
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| 520 | |a RNA interference (RNAi) high-content screening (HCS) enables massive parallel gene silencing and is increasingly being used to reveal novel connections between genes and disease-relevant phenotypes. The application of genome-scale RNAi relies on the development of high quality HCS assays. The Z’ factor statistic provides a way to evaluate whether or not screening run conditions (reagents, protocols, instrumentation, kinetics, and other conditions not directly related to the test compounds) are optimized. Z’ factor, introduced by Zhang et al.,1 is a dimensionless value that represents both the variability and the dynamic range between two sets of sample control data. This paper describe a new extension of the Z' factor, which integrates bioinformatics RNAi non-target compounds for screening quality assessment. Currently presented Z’ factor is based on positive and negative control, which may not be sufficient for RNAi experiments including oligonucleotides (oligo) with lack of knock-down. This paper proposes an algorithm which extends existing algorithm by using additional controls generetaed from on-target analysis. | ||
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