ReviewWorld journal of virology2013
High-throughput RNA interference screens integrative analysis: Towards a comprehensive understanding of the virus-host interplay.
Review in World journal of virology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Artificial Intelligence Methods in Infection Biology Research.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Article
- Article
- Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation.PLoS computational biology · 2020Article
- Host-Induced Gene Silencing of an Adenylate Kinase Gene Involved in Fungal Energy Metabolism Improves Plant Resistance toBiomolecules · 2020Article
- Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy.Journal of visualized experiments : JoVE · 2018Article
- CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes.Nature biotechnology · 2016Article
- An integrative approach for a network based meta-analysis of viral RNAi screens.Algorithms for molecular biology : AMB · 2015Article
- Reprogramming the host: Modification of cell functions upon viral infection.World journal of virology · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Viruses are extremely heterogeneous entities; the size and the nature of their genetic information, as well as the strategies employed to amplify and propagate their genomes, are highly variable. However, as obligatory intracellular parasites, replication of all viruses relies on the host cell. Having co-evolved with their host for several million years, viruses have developed very sophisticated strategies to hijack cellular factors that promote virus uptake, replication, and spread. Identification of host cell factors (HCFs) required for these processes is a major challenge for researchers, but it enables the identification of new, highly selective targets for anti viral therapeutics. To this end, the establishment of platforms enabling genome-wide high-throughput RNA interference (HT-RNAi) screens has led to the identification of several key factors involved in the viral life cycle. A number of genome-wide HT-RNAi screens have been performed for major human pathogens. These studies enable first inter-viral comparisons related to HCF requirements. Although several cellular functions appear to be uniformly required for the life cycle of most viruses tested (such as the proteasome and the Golgi-mediated secretory pathways), some factors, like the lipid kinase Phosphatidylinositol 4-kinase IIIα in the case of hepatitis C virus, are selectively required for individual viruses. However, despite the amount of data available, we are still far away from a comprehensive understanding of the interplay between viruses and host factors. Major limitations towards this goal are the low sensitivity and specificity of such screens, resulting in limited overlap between different screens performed with the same virus. This review focuses on how statistical and bioinformatic analysis methods applied to HT-RNAi screens can help overcoming these issues thus increasing the reliability and impact of such studies.
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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.