ArticleBMC bioinformatics2014
Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 47 citations in OpenAlex.
- Advanced computational predictive models of miRNA-mRNA interaction efficiency.Computational and structural biotechnology journal · 2024Article
- RNAi-Based Approaches to Control Mycotoxin Producers: Challenges and Perspectives.Journal of fungi (Basel, Switzerland) · 2024Review
- ZetaSuite: computational analysis of two-dimensional high-throughput data from multi-target screens and single-cell transcriptomics.Genome biology · 2022Article
- Therapeutic RNA interference: A novel approach to the treatment of primary hyperoxaluria.British journal of clinical pharmacology · 2022Review
- In Silico Design of siRNAs for Silencing CLEC5A Receptor as a Potential Therapeutic Approach Against Dengue and Japanese Encephalitis Virus Infection in Human.Bioinformatics and biology insights · 2022Article
- A Gene Expression High-Throughput Screen (GE-HTS) for Coordinated Detection of Functionally Similar Effectors in Cancer.Cancers · 2020Review
- Article
- Allele-Specific Small Interfering RNA Corrects Aberrant Cellular Phenotype in Keratitis-Ichthyosis-Deafness Syndrome Keratinocytes.The Journal of investigative dermatology · 2020Article
- Discovery of a ZIP7 inhibitor from a Notch pathway screen.Nature chemical biology · 2019Article
- Pan-cancer characterisation of microRNA across cancer hallmarks reveals microRNA-mediated downregulation of tumour suppressors.Nature communications · 2018Article
- Evaluation and control of miRNA-like off-target repression for RNA interference.Cellular and molecular life sciences : CMLS · 2018Review
- While it is not deliberate, much of today's biomedical research contains logical and technical flaws, showing a need for corrective action.International journal of medical sciences · 2018Review
- Defining a Cancer Dependency Map.Cell · 2017Article
- Seed-effect modeling improves the consistency of genome-wide loss-of-function screens and identifies synthetic lethal vulnerabilities in cancer cells.Genome medicine · 2017Article
- MicroRNAs and Periodontal Homeostasis.Journal of dental research · 2017Review
- Knocking down the obstacles to functional genomics data sharing.Scientific data · 2017Article
- FlyRNAi.org-the database of the Drosophila RNAi screening center and transgenic RNAi project: 2017 update.Nucleic acids research · 2017Article
- Article
- High-Throughput Small Interfering RNA Screening Identifies Phosphatidylinositol 3-Kinase Class II Alpha as Important for Production of Human Cytomegalovirus Virions.Journal of virology · 2016Article
- Human Argonaute 2 Is Tethered to Ribosomal RNA through MicroRNA Interactions.The Journal of biological chemistry · 2016Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundRNA interference (RNAi) is an effective and important tool used to study gene function. For large-scale screens, RNAi is used to systematically down-regulate genes of interest and analyze their roles in a biological process. However, RNAi is associated with off-target effects (OTEs), including microRNA (miRNA)-like OTEs. The contribution of reagent-specific OTEs to RNAi screen data sets can be significant. In addition, the post-screen validation process is time and labor intensive. Thus, the availability of robust approaches to identify candidate off-targeted transcripts would be beneficial.
resultsSignificant efforts have been made to eliminate false positive results attributable to sequence-specific OTEs associated with RNAi. These approaches have included improved algorithms for RNAi reagent design, incorporation of chemical modifications into siRNAs, and the use of various bioinformatics strategies to identify possible OTEs in screen results. Genome-wide Enrichment of Seed Sequence matches (GESS) was developed to identify potential off-targeted transcripts in large-scale screen data by seed-region analysis. Here, we introduce a user-friendly web application that provides researchers a relatively quick and easy way to perform GESS analysis on data from human or mouse cell-based screens using short interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs), as well as for Drosophila screens using shRNAs. Online GESS relies on up-to-date transcript sequence annotations for human and mouse genes extracted from NCBI Reference Sequence (RefSeq) and Drosophila genes from FlyBase. The tool also accommodates analysis with user-provided reference sequence files.
conclusionOnline GESS provides a straightforward user interface for genome-wide seed region analysis for human, mouse and Drosophila RNAi screen data. With the tool, users can either use a built-in database or provide a database of transcripts for analysis. This makes it possible to analyze RNAi data from any organism for which the user can provide transcript sequences.
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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.