Name
Abstract | ||
|---|---|---|
High-throughput screening (HTS) is an important method in drug discovery in which the activities of a large number of candidate chemicals or genetic materials are rapidly evaluated. Data are usually obtained by measurements on samples in microwell plates and are often subjected to artefacts that can bias the result selection. We report here a novel edge effect correction algorithm suitable for RNA interference (RNAi) screening, because its normalization does not rely on the entire dataset and takes into account the specificities of such a screening process. The proposed method is able to estimate the edge effects for each assay plate individually using the data from a single control column based on diffusion model, and thus targeting a specific but recurrent well-known HTS artefact. This method was first developed and validated using control plates and was then applied to the correction of experimental data generated during a genome-wide siRNA screen aimed at studying HIV-host interactions. The proposed algorithm was able to correct the edge effect biasing the control data and thus improve assay quality and, consequently, the hit-selection step. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1093/bioinformatics/btr648 | Bioinformatics |
Keywords | Field | DocType |
supplementary data,correction method,high-throughput screening,experimental data,important method,novel specific edge effect,screening process,control data,rna interference screening,novel edge effect correction,edge effect,control plate | Data mining,Drug discovery,Normalization (statistics),Correction algorithm,Experimental data,Computer science,Genetic Materials,Bioinformatics | Journal |
Volume | Issue | ISSN |
28 | 2 | 1367-4811 |
Citations | PageRank | References |
5 | 0.64 | 3 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean-Philippe Carralot | 1 | 5 | 0.64 |
| Arnaud Ogier | 2 | 20 | 4.09 |
| Annette Boese | 3 | 5 | 0.64 |
| Auguste Genovesio | 4 | 42 | 7.98 |
| Priscille Brodin | 5 | 9 | 1.34 |
| Peter Sommer | 6 | 5 | 0.64 |
| Thierry Dorval | 7 | 22 | 3.23 |