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Related Concept Videos

RNA Interference01:23

RNA Interference

RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
RNA Interference01:23

RNA Interference

RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
Experimental RNAi02:15

Experimental RNAi

RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...

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Related Experiment Video

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Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster
09:39

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

Published on: August 21, 2014

A novel specific edge effect correction method for RNA interference screenings.

Jean-Philippe Carralot1, Arnaud Ogier, Annette Boese

  • 1Biology of Intracellular Pathogens, Inserm Avenir Team, Institut Pasteur Korea, Seongnam-si, Korea. jean-philippe.carralot@roche.com

Bioinformatics (Oxford, England)
|November 29, 2011
PubMed
Summary
This summary is machine-generated.

A new algorithm corrects edge effects in high-throughput screening (HTS) data, improving drug discovery accuracy. This method enhances RNA interference (RNAi) screening by addressing plate-specific artefacts for better hit selection.

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Last Updated: May 27, 2026

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09:39

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Published on: August 21, 2014

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Published on: December 2, 2009

Area of Science:

  • Biotechnology
  • Drug Discovery
  • Bioinformatics

Background:

  • High-throughput screening (HTS) is crucial for drug discovery, evaluating numerous candidate compounds.
  • HTS data, often from microwell plates, can be affected by artefacts, biasing results.
  • RNA interference (RNAi) screening presents unique challenges due to specific artefacts.

Purpose of the Study:

  • To develop and validate a novel algorithm for correcting edge effects in HTS data.
  • To improve the accuracy of hit selection in RNAi screening.
  • To address a common HTS artefact without relying on the entire dataset.

Main Methods:

  • A novel edge effect correction algorithm based on a diffusion model.
  • Individual plate normalization using data from a single control column.
  • Validation using control plates and application to a genome-wide siRNA screen for HIV-host interactions.

Main Results:

  • The algorithm effectively estimates and corrects edge effects on a per-plate basis.
  • Correction of edge effects improved the quality of assay data.
  • Enhanced assay quality led to a more reliable hit-selection process.

Conclusions:

  • The proposed algorithm successfully corrects a recurrent HTS artefact.
  • This method enhances the reliability of RNAi screening and drug discovery pipelines.
  • The algorithm offers a targeted approach to improve HTS data quality and hit identification.