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Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis
BACKGROUND: 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), inc...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4073188/ https://www.ncbi.nlm.nih.gov/pubmed/24934636 http://dx.doi.org/10.1186/1471-2105-15-192 |
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author | Yilmazel, Bahar Hu, Yanhui Sigoillot, Frederic Smith, Jennifer A Shamu, Caroline E Perrimon, Norbert Mohr, Stephanie E |
author_facet | Yilmazel, Bahar Hu, Yanhui Sigoillot, Frederic Smith, Jennifer A Shamu, Caroline E Perrimon, Norbert Mohr, Stephanie E |
author_sort | Yilmazel, Bahar |
collection | PubMed |
description | BACKGROUND: 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. |
format | Online Article Text |
id | pubmed-4073188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40731882014-06-28 Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis Yilmazel, Bahar Hu, Yanhui Sigoillot, Frederic Smith, Jennifer A Shamu, Caroline E Perrimon, Norbert Mohr, Stephanie E BMC Bioinformatics Software BACKGROUND: 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. BioMed Central 2014-06-17 /pmc/articles/PMC4073188/ /pubmed/24934636 http://dx.doi.org/10.1186/1471-2105-15-192 Text en Copyright © 2014 Yilmazel et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Yilmazel, Bahar Hu, Yanhui Sigoillot, Frederic Smith, Jennifer A Shamu, Caroline E Perrimon, Norbert Mohr, Stephanie E Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis |
title | Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis |
title_full | Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis |
title_fullStr | Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis |
title_full_unstemmed | Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis |
title_short | Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis |
title_sort | online gess: prediction of mirna-like off-target effects in large-scale rnai screen data by seed region analysis |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4073188/ https://www.ncbi.nlm.nih.gov/pubmed/24934636 http://dx.doi.org/10.1186/1471-2105-15-192 |
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