Minimizing the risk of reporting false positives in large-scale RNAi screens

By C. Echeverri, P. Beachy, B. Baum, Michael Boutros, F. Buchholz, S. Chanda, Julian Downward, Jan Ellenberg, Andrew G. Fraser, Nir Hacohen and 12 more. Nature Methods, 2006

480 citations recorded by the source corpus.

Large-scale RNA interference (RNAi)-based analyses, very much as other 'omic' approaches, have inherent rates of false positives and negatives. The variability in the standards of care applied to validate results from these studies, if left unchecked, could eventually begin to undermine the credibility of RNAi as a powerful functional approach. This Commentary is an invitation to an open discussion started among various users of RNAi to set forth accepted standards that would insure the quality and accuracy of information in the large datasets coming out of genome-scale screens. Please visit methagora to view and post comments on this article

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0e27015bc4f287b75d7a35723a0a2a90db0c75f8

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Citations in the corpus

The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. (2009, methodology): “MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments, pronounced mykee) is modeled on similar guidelines drawn up for DNA microarray analysis (16), proteomics experiments (17), genome sequence specification (18), and those under discussion for RNA interference work ( 1”
Global analysis of host-pathogen interactions that regulate early stage HIV-1 replication (2008, background): “Since 154 of the genes in the Brass et al. study were supported by the activity of only a single siRNA, it is likely that a fraction of these reported host factors represent false-positive readouts due to off-target RNAi activity (Echeverri et al., 2006).”
Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning (2009, background): “Whereas screening for perturbations of general cellular functions like cell division has yielded large networks of genes (14, 34), the ability to identify more subtle and rare cellular morphologies should yield more tightly focused families of genes worthy of study (35).”
Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo (2010, unknown): “Two different siRNAs were used to rule out off-target effects (Echeverri et al, 2006), and two biological replicates were used for each siRNA to decrease noise.”
Evaluation and Design of Genome-wide CRISPR/Cas9 Knockout Screens (2017, background): “In particular, the design of early CRISPR libraries to include several gRNAs targeting each gene has been driven by experience with pooled library shRNA screens [12, 13], as well as the unknowns surrounding the application of CRISPR technology in human cells on a large scale.”
Bruchpilot, A Synaptic Active Zone Protein for Anesthesia-Resistant Memory (2011, background): “2), making it difficult to explain the observed memory phenotype by an off-target effect (Echeverri et al., 2006).”
Statistical Methods for Analysis of High-Throughput RNA Interference Screens (2009, background): “Sequence-specific off-target effects of RNAi 4–6 resulting from partial sequence complementarity between siRNA and mRNA complicate interpretation of RNAi experiments.”
Evaluation and Design of Genome-Wide CRISPR/SpCas9 Knockout Screens (2017, background): “…design of early CRISPR libraries to include several guide RNAs (gRNAs) targeting each gene has been driven by experience with pooled-library shRNA screens (Kaelin, 2012; Echeverri et al. 2006), as well as the unknowns surrounding the application of CRISPR technology in human cells on a large scale.”
High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. (2015, unknown): “…has been constrained by technology: RNA interference (RNAi) has been the best available tool but, despite improvements in reagent design and analytical approaches, its utility is limited by imperfect mRNA knockdown and confounding off-target effects (Echeverri et al., 2006; Moffat et al., 2007).”
Recognizing and avoiding siRNA off-target effects for target identification and therapeutic application (2010, background): “As each siRNA has a unique off-target spectrum, but the same intended target, observing the same phenotype with multiple individual siRNAs that contain distinct seed sequences increases confidence that the phenotype results from silencing the intended targe”