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dc.contributor.authorYilmazel, Baharen_US
dc.contributor.authorHu, Yanhuien_US
dc.contributor.authorSigoillot, Fredericen_US
dc.contributor.authorSmith, Jennifer Aen_US
dc.contributor.authorShamu, Caroline Een_US
dc.contributor.authorPerrimon, Norberten_US
dc.contributor.authorMohr, Stephanie Een_US
dc.date.accessioned2014-07-07T17:01:25Z
dc.date.issued2014en_US
dc.identifier.citationYilmazel, Bahar, Yanhui Hu, Frederic Sigoillot, Jennifer A Smith, Caroline E Shamu, Norbert Perrimon, and Stephanie E Mohr. 2014. “Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis.” BMC Bioinformatics 15 (1): 192. doi:10.1186/1471-2105-15-192. http://dx.doi.org/10.1186/1471-2105-15-192.en
dc.identifier.issn1471-2105en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12406537
dc.description.abstractBackground: RNA 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. Results: Significant 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. Conclusion: Online 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.en
dc.language.isoen_USen
dc.publisherBioMed Centralen
dc.relation.isversionofdoi:10.1186/1471-2105-15-192en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4073188/pdf/en
dash.licenseLAAen_US
dc.subjectRNAien
dc.subjectOff-target effectsen
dc.subjectData analysisen
dc.subjectSeed regionen
dc.subjectmiRNAen
dc.subjectsiRNAen
dc.subjectshRNAen
dc.subjectHigh-throughput screeningen
dc.titleOnline GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysisen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalBMC Bioinformaticsen
dash.depositing.authorYilmazel, Baharen_US
dc.date.available2014-07-07T17:01:25Z
dc.identifier.doi10.1186/1471-2105-15-192*
dash.contributor.affiliatedYilmazel, Bahar
dash.contributor.affiliatedShamu, Caroline
dash.contributor.affiliatedMohr, Stephanie
dash.contributor.affiliatedHu, Yanhui
dash.contributor.affiliatedPerrimon, Norbert


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