Abstract | ||
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Background Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes.
Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling
a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of
similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both.
Results We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives
and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives.
To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for
re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction
network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens
identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from
this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related
protein complexes: the eukaryotic translation initiation factor 3 (eIF3) complex, the COP9 signalosome, and the proteasome
lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity,
the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition.
Conclusions Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed
between similar large-scale RNAi-based screens. Our results also show that protein network data can be used to minimize false
negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process. Finally, our
data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival. |
Year | DOI | Venue |
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2011 | 10.1186/1752-0509-5-65 | BMC systems biology |
Keywords | Field | DocType |
phenotype,protein complex,computational biology,rna interference,proteins,cell cycle,cell cycle regulation,algorithms,dna,systems biology,false positive,bioinformatics,genotype | COP9 signalosome,Gene,Phenotype,Biology,Systems biology,Cell biology,Caenorhabditis elegans,Bioinformatics,Drosophila melanogaster,RNA interference,False positive paradox | Journal |
Volume | Issue | ISSN |
5 | 1 | null |
Citations | PageRank | References |
7 | 0.55 | 10 |
Authors | ||
6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Stephen Guest | 1 | 56 | 3.02 |
Jingkai Yu | 2 | 70 | 4.20 |
Dongmei Liu | 3 | 7 | 0.55 |
Julie A. Hines | 4 | 7 | 0.55 |
Maria A. Kashat | 5 | 7 | 0.55 |
Russell L. Finley Jr. | 6 | 57 | 3.06 |