ArticleCombinatorial chemistry & high throughput screening2013
Systematic analysis of RNAi reports identifies dismal commonality at gene-level and reveals an unprecedented enrichment in pooled shRNA screens.
Article in Combinatorial chemistry & high throughput screening, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- PRODE recovers essential and context-essential genes through neighborhood-informed scores.Genome biology · 2025Article
- SIGNAL: A web-based iterative analysis platform integrating pathway and network approaches optimizes hit selection from genome-scale assays.Cell systems · 2021Article
- The Long Non-coding RNA Cyrano Is Dispensable for Pluripotency of Murine and Human Pluripotent Stem Cells.Stem cell reports · 2020Article
- Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls.Journal of molecular biology · 2018Review
- Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection.BMC bioinformatics · 2018Article
- Synthetic lethality and cancer.Nature reviews. Genetics · 2017Review
- An interactive web-based application for Comprehensive Analysis of RNAi-screen Data.Nature communications · 2016Article
- The New State of the Art: Cas9 for Gene Activation and Repression.Molecular and cellular biology · 2015Review
- Phospholipase C γ1 regulates early secretory trafficking and cell migration via interaction with p115.Molecular biology of the cell · 2015Article
- Review
- Plasmid-based shRNA lentiviral particle production for RNAi applications.Journal of biomolecular screening · 2014Article
- RNAi screening comes of age: improved techniques and complementary approaches.Nature reviews. Molecular cell biology · 2014Review
- Comparative analysis of RNAi screening technologies at genome-scale reveals an inherent processing inefficiency of the plasmid-based shRNA hairpin.Combinatorial chemistry & high throughput screening · 2014Article
- Article
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Authors and funding
2 authors.
Funding
Abstract
RNA interference (RNAi) has opened promising avenues to better understand gene function. Though many RNAi screens report on the identification of genes, very few, if any, have been further studied and validated. Data discrepancy is emerging as one of RNAi main pitfalls. We reasoned that a systematic analysis of lethality-based screens, since they score for cell death, would examine the extent of hit discordance at inter-screen level. To this end, we developed a methodology for literature mining and overlap analysis of several screens using both siRNA and shRNA flavors, and obtained 64 gene lists censoring an initial list of 7,430 nominated genes. We further performed a comparative analysis first at a global level followed by hit re-assessment under much more stringent conditions. To our surprise, none of the hits overlapped across the board even for PLK1, which emerged as a strong candidate in siRNA screens; but only marginally in the shRNA ones. Furthermore, EIF5B emerges as the most common hit only in the shRNA screens. A highly unusual and unprecedented result was the observation that 5,269 out of 6,664 nominated genes (~80%) in the shRNA screens were exclusive to the pooled format, raising concerns as to the merits of pooled screens which qualify hits based on relative depletions, possibly due to multiple integrations per cell, data deconvolution or inaccuracies in intracellular processing causing off-target effects. Without golden standards in place, we would encourage the community to pay more attention to RNAi screening data analysis practices, bearing in mind that it is combinatorial in nature and one active siRNA duplex or shRNA hairpin per gene does not suffice credible hit nomination. Finally, we also would like to caution interpretation of pooled shRNA screening outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.